From 67379054565a85c5a170f9488bbdb831fc8e1d43 Mon Sep 17 00:00:00 2001 From: mpetersen94 Date: Sun, 12 Mar 2023 19:25:25 -0400 Subject: [PATCH 1/2] Adjust to deprecated Drake code --- models/schunk_wsg_50_welded_fingers.sdf | 6 +++--- reproduction/bimanual/bimanual_iiwa_example.ipynb | 2 +- reproduction/bimanual/helpers.py | 4 ++-- reproduction/prm_comparison/prm_comparison.ipynb | 2 +- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/models/schunk_wsg_50_welded_fingers.sdf b/models/schunk_wsg_50_welded_fingers.sdf index e186979..5529fd4 100644 --- a/models/schunk_wsg_50_welded_fingers.sdf +++ b/models/schunk_wsg_50_welded_fingers.sdf @@ -20,7 +20,7 @@ 1 1 1 - package://drake/manipulation/models/wsg_50_description/meshes/wsg_body.obj + package://drake_models/wsg_50_description/meshes/wsg_body.obj @@ -102,7 +102,7 @@ 1 1 1 - package://drake/manipulation/models/wsg_50_description/meshes/finger_without_tip.obj + package://drake_models/wsg_50_description/meshes/finger_without_tip.obj @@ -181,7 +181,7 @@ 1 1 1 - package://drake/manipulation/models/wsg_50_description/meshes/finger_without_tip.obj + package://drake_models/wsg_50_description/meshes/finger_without_tip.obj diff --git a/reproduction/bimanual/bimanual_iiwa_example.ipynb b/reproduction/bimanual/bimanual_iiwa_example.ipynb index be74e35..8867a1a 100644 --- a/reproduction/bimanual/bimanual_iiwa_example.ipynb +++ b/reproduction/bimanual/bimanual_iiwa_example.ipynb @@ -91,7 +91,7 @@ "context = diagram.CreateDefaultContext()\n", "plant_context = plant.GetMyMutableContextFromRoot(context)\n", "sg_context = scene_graph.GetMyMutableContextFromRoot(context)\n", - "diagram.Publish(context)" + "diagram.ForcedPublish(context)" ] }, { diff --git a/reproduction/bimanual/helpers.py b/reproduction/bimanual/helpers.py index 376c412..795f573 100644 --- a/reproduction/bimanual/helpers.py +++ b/reproduction/bimanual/helpers.py @@ -293,7 +293,7 @@ def runBimanualIK(plant, context, wsg1_id, wsg2_id, wsg1_pose, wsg2_pose, def visualizeConfig(diagram, plant, context, q): plant_context = plant.GetMyMutableContextFromRoot(context) plant.SetPositions(plant_context, q) - diagram.Publish(context) + diagram.ForcedPublish(context) def getLinearGcsPath(regions, sequence): path = [sequence[0]] @@ -506,4 +506,4 @@ def generate_segment_pics(traj, segment, meshcat): rgba=Rgba(0, 0, 1, 1)) plant.SetPositions(plant_context, np.concatenate((q_waypoints[:, 0], q_waypoints[:, -1]))) - diagram.Publish(context) + diagram.ForcedPublish(context) diff --git a/reproduction/prm_comparison/prm_comparison.ipynb b/reproduction/prm_comparison/prm_comparison.ipynb index 60d4a8b..b5e010f 100644 --- a/reproduction/prm_comparison/prm_comparison.ipynb +++ b/reproduction/prm_comparison/prm_comparison.ipynb @@ -95,7 +95,7 @@ "plant_context = plant.GetMyMutableContextFromRoot(context)\n", "q0 =[0, 0.3, 0, -1.8, 0, 1, 1.57]\n", "plant.SetPositions(plant_context, q0)\n", - "diagram.Publish(context)" + "diagram.ForcedPublish(context)" ] }, { From ab077211170e8fe1ec9a9abf99c55d5d5d4e2f12 Mon Sep 17 00:00:00 2001 From: wrangelvid Date: Thu, 16 Mar 2023 10:19:36 -0400 Subject: [PATCH 2/2] improve prm speed with dedicated C implementation --- .gitignore | 1 - README.md | 2 +- data/prm_comparison/IRIS.reg | Bin 0 -> 44861 bytes data/prm_comparison/roadmap.rmp | Bin 0 -> 4283093 bytes examples/uav_planning.ipynb | 4 +- ...wa14_spheres_collision_welded_gripper.yaml | 2 +- models/iiwa14_welded_gripper.yaml | 89 +++ ...wsg_50_welded_fingers_sphere_collision.sdf | 208 ++++++ .../bimanual/bimanual_iiwa_example.ipynb | 4 +- reproduction/bimanual/helpers.py | 4 +- reproduction/prm_comparison/helpers.py | 534 ++++++++++---- reproduction/prm_comparison/planning.py | 424 ++++------- .../prm_comparison/prm_comparison.ipynb | 691 +++++++++++++----- reproduction/uav/uav_generated_rooms.ipynb | 4 +- 14 files changed, 1314 insertions(+), 653 deletions(-) create mode 100644 data/prm_comparison/IRIS.reg create mode 100644 data/prm_comparison/roadmap.rmp create mode 100644 models/iiwa14_welded_gripper.yaml create mode 100644 models/schunk_wsg_50_welded_fingers_sphere_collision.sdf diff --git a/.gitignore b/.gitignore index ef37aa9..5a3f948 100644 --- a/.gitignore +++ b/.gitignore @@ -3,7 +3,6 @@ /bazel-* /build/ /cmake-build-*/ -/data/ /.vscode models/room_gen/building.sdf diff --git a/README.md b/README.md index f0fc9ca..49092d1 100644 --- a/README.md +++ b/README.md @@ -32,7 +32,7 @@ from inside this repository. If you want to compare GCS to sampling based planners (such as PRM), you'll need to install a custom fork of drake that includes bindings for sampling based planners. To do this run the following, including any of the proprietary solvers you have access to. ``` -git clone -b gcs2 git@github.com:mpetersen94/drake.git +git clone -b gcs_paper git@github.com:wrangelvid/drake.git mkdir drake-build cd drake-build cmake -DWITH_MOSEK=ON [-DWITH_GUROBI=ON -DWITH_ROBOTLOCOMOTION_SNOPT=ON] ../drake diff --git a/data/prm_comparison/IRIS.reg b/data/prm_comparison/IRIS.reg new file mode 100644 index 0000000000000000000000000000000000000000..3193666af5f36307d2e4d1b45eaa1055d4c11bc3 GIT binary patch literal 44861 zcmeFZX*||l_dbjgqLf0Ulm=yJFhnU!hDu13ltcp|naLC>5t+wAndfr13gVsW6M(phNe1tI#!QNbqw@iPJd zeC_MXsBflcXnE?1fwAT(J+tSgI**KvA6fdk(y1G3J~7ZYF*P=OVoDn8O8Jk0zOGdI z8vh*P<10hv;44A?*!P{UqVEYAav4<_Do0;!8dpjUQ%gh5|9KI`Q(<3cr+?nx;!1we z_r{GI*ME~9|9FxiGxdG!Bz|b`KYzym;o0tA_x@}dYz`jfmx)II-CK?nDx`wt63g>6 zD?ji*^|RiKu>x4~qv(6@7s5}u8Pj)9LZQs6KW3s9asTPWAO3zNShtWW!oweezIMM^ zCTGInrq|-5=FD=4>#z^AH5`GISn>=tf3h!EZwjDOIN9%33LssN8P+RE& 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import *\n", "\n", - "g_lic = GurobiSolver.AcquireLicense()\n", - "m_lic = MosekSolver.AcquireLicense()" + "gurobi_license = GurobiSolver.AcquireLicense()\n", + "mosek_license = MosekSolver.AcquireLicense()" ] }, { diff --git a/models/iiwa14_spheres_collision_welded_gripper.yaml b/models/iiwa14_spheres_collision_welded_gripper.yaml index 1742873..a34a404 100644 --- a/models/iiwa14_spheres_collision_welded_gripper.yaml +++ b/models/iiwa14_spheres_collision_welded_gripper.yaml @@ -13,7 +13,7 @@ directives: # Add schunk - add_model: name: wsg - file: package://gcs/models/schunk_wsg_50_welded_fingers.sdf + file: package://gcs/models/schunk_wsg_50_welded_fingers_sphere_collision.sdf - add_frame: name: iiwa::wsg_attach diff --git a/models/iiwa14_welded_gripper.yaml b/models/iiwa14_welded_gripper.yaml new file mode 100644 index 0000000..d5ff3dc --- /dev/null +++ b/models/iiwa14_welded_gripper.yaml @@ -0,0 +1,89 @@ +# Model directive for iiwa with sphere collision and welded gripper +directives: + +# Add iiwa +- add_model: + name: iiwa + file: package://drake/manipulation/models/iiwa_description/urdf/iiwa14_spheres_collision.urdf + +- add_weld: + parent: world + child: iiwa::base + +# Add schunk +- add_model: + name: wsg + file: package://drake/manipulation/models/wsg_50_description/sdf/schunk_wsg_50_welded_fingers.sdf + +- add_frame: + name: iiwa::wsg_attach + X_PF: + base_frame: iiwa::iiwa_link_7 + translation: [0, 0, 0.114] + rotation: !Rpy { deg: [90.0, 0.0, 0.0 ]} + +- add_weld: + parent: iiwa::wsg_attach + child: wsg::body + +# Add shelves +- add_model: + name: shelves + file: package://gcs/models/shelves/shelves.sdf + +- add_frame: + name: shelf_origin + X_PF: + base_frame: world + translation: [0.85, 0, 0.4] + +- add_weld: + parent: shelf_origin + child: shelves::shelves_body + +# Add Bins +- add_model: + name: binR + file: package://gcs/models/bin/bin.sdf + +- add_frame: + name: bin_originR + X_PF: + base_frame: world + translation: [0, -0.6, 0] + rotation: !Rpy { deg: [0.0, 0.0, 90.0 ]} + +- add_weld: + parent: bin_originR + child: binR::bin_base + +- add_model: + name: binL + file: package://gcs/models/bin/bin.sdf + +- add_frame: + name: bin_originL + X_PF: + base_frame: world + translation: [0, 0.6, 0] + rotation: !Rpy { deg: [0.0, 0.0, 90.0 ]} + +- add_weld: + parent: bin_originL + child: binL::bin_base + +# Add table +- add_model: + name: table + file: package://gcs/models/table/table_wide.sdf + +- add_frame: + name: table_origin + X_PF: + base_frame: world + translation: [0.4, 0.0, 0.0] + rotation: !Rpy { deg: [0., 0., 00]} + +- add_weld: + parent: table_origin + child: table::table_body diff --git a/models/schunk_wsg_50_welded_fingers_sphere_collision.sdf b/models/schunk_wsg_50_welded_fingers_sphere_collision.sdf new file mode 100644 index 0000000..8e5b641 --- /dev/null +++ b/models/schunk_wsg_50_welded_fingers_sphere_collision.sdf @@ -0,0 +1,208 @@ + + + + + + 0 -0.049133 0 0 0 0 + + 0.988882 + + 0.162992 + 0 + 0 + 0.162992 + 0 + 0.164814 + + + 0 + + + + 1 1 1 + package://drake_models/wsg_50_description/meshes/wsg_body.obj + + + + 0.7 0.7 0.7 1 + + + + 0.02 0 0.0 0 0 0 + + + 0.05 + + + + + -0.02 0 0.0 0 0 0 + + + 0.05 + + + + + -0.045 0.005 0.0 0 0 0 + + + 0.05 + + + + + 0.045 0.005 0.0 0 0 0 + + + 0.05 + + + + + + + + + -0.06 0.028 0 0 3.141592 0 + + 0.05 + + 0.16 + 0 + 0 + 0.16 + 0 + 0.16 + + + 0 + + + + 1 1 1 + package://drake_models/wsg_50_description/meshes/finger_without_tip.obj + + + + 0.2 0.2 0.2 1 + + + + 0.005 -0.02 0 0 0 0 + + + 0.018 + + + + + 0.005 -0.01 0 0 0 0 + + + 0.017 + + + + + 0.002 0.005 0 0 0 0 + + + 0.014 + + + + + 0.00 0.02 0 0 0 0 + + + 0.01 + + + + + 0.00 0.032 0 0 0 0 + + + 0.011 + + + + + + 0.06 0.028 0 0 0 0 + + 0.05 + + 0.16 + 0 + 0 + 0.16 + 0 + 0.16 + + + 0 + + + + 1 1 1 + package://drake_models/wsg_50_description/meshes/finger_without_tip.obj + + + + 0.2 0.2 0.2 1 + + + + 0.005 -0.02 0 0 0 0 + + + 0.018 + + + + + 0.005 -0.01 0 0 0 0 + + + 0.017 + + + + + 0.002 0.005 0 0 0 0 + + + 0.014 + + + + + 0.00 0.02 0 0 0 0 + + + 0.01 + + + + + 0.00 0.032 0 0 0 0 + + + 0.011 + + + + + + body + left_finger + + + body + right_finger + + 0 + + diff --git a/reproduction/bimanual/bimanual_iiwa_example.ipynb b/reproduction/bimanual/bimanual_iiwa_example.ipynb index 8867a1a..dfc7a7d 100644 --- a/reproduction/bimanual/bimanual_iiwa_example.ipynb +++ b/reproduction/bimanual/bimanual_iiwa_example.ipynb @@ -33,8 +33,8 @@ "from reproduction.bimanual.helpers import *\n", "from reproduction.util import *\n", "\n", - "GurobiSolver.AcquireLicense()\n", - "MosekSolver.AcquireLicense()" + "gurobi_license = GurobiSolver.AcquireLicense()\n", + "mosek_license = MosekSolver.AcquireLicense()" ] }, { diff --git a/reproduction/bimanual/helpers.py b/reproduction/bimanual/helpers.py index 795f573..d3db2e7 100644 --- a/reproduction/bimanual/helpers.py +++ b/reproduction/bimanual/helpers.py @@ -26,7 +26,7 @@ from gcs.bezier import BezierGCS from gcs.linear import LinearGCS from gcs.rounding import * -from reproduction.prm_comparison.helpers import lower_alpha +from reproduction.prm_comparison.helpers import set_transparency_of_models from reproduction.util import * def getIkSeeds(): @@ -467,7 +467,7 @@ def generate_segment_pics(traj, segment, meshcat): arm_models = [iiwa1_start.model_instance, wsg1_start.model_instance, iiwa2_start.model_instance, wsg2_start.model_instance, iiwa1_goal, wsg1_goal, iiwa2_goal, wsg2_goal] - lower_alpha(plant, scene_graph.model_inspector(), arm_models, 0.4, scene_graph) + set_transparency_of_models(plant, arm_models, 0.4, scene_graph) plant.Finalize() diff --git a/reproduction/prm_comparison/helpers.py b/reproduction/prm_comparison/helpers.py index f818d6c..a1e5e37 100644 --- a/reproduction/prm_comparison/helpers.py +++ b/reproduction/prm_comparison/helpers.py @@ -1,20 +1,16 @@ +from typing import Sequence, List, Union, Optional import matplotlib.pyplot as plt -import os import numpy as np +import pandas as pd from pydrake.systems.framework import DiagramBuilder from pydrake.multibody.plant import AddMultibodyPlantSceneGraph, MultibodyPlant from pydrake.multibody.parsing import LoadModelDirectives, Parser, ProcessModelDirectives from pydrake.common import FindResourceOrThrow -from pydrake.geometry import ( - IllustrationProperties, - MeshcatVisualizer, - MeshcatVisualizerParams, - Rgba, - RoleAssign, - Role, - SceneGraph -) +from pydrake.geometry import (IllustrationProperties, MeshcatVisualizer, + MeshcatVisualizerParams, Rgba, RoleAssign, Role, + SceneGraph) +from pydrake.geometry.optimization import VPolytope, HPolyhedron from pydrake.math import RigidTransform, RollPitchYaw, RotationMatrix from pydrake.perception import PointCloud from pydrake.all import MultibodyPositionToGeometryPose @@ -25,64 +21,87 @@ from pydrake.multibody import inverse_kinematics from pydrake.solvers import Solve -from reproduction.util import * +from reproduction.util import GcsDir, FindModelFile -def ForwardKinematics(q_list): + +def ForwardKinematics(q_list: List[Sequence[float]]) -> List[RigidTransform]: + """Returns the end-effector pose for the given joint angles. + + The end-effector is the body of the wsg gripper. + + Args: + q_list: List of joint angles. + + Returns: + List of end-effector poses. + """ builder = DiagramBuilder() - plant, scene_graph = AddMultibodyPlantSceneGraph(builder, time_step=0.0) + plant, _ = AddMultibodyPlantSceneGraph(builder, time_step=0.0) parser = Parser(plant) parser.package_map().Add("gcs", GcsDir()) - directives_file = FindModelFile("models/iiwa14_spheres_collision_welded_gripper.yaml") + directives_file = FindModelFile( + "models/iiwa14_spheres_collision_welded_gripper.yaml") directives = LoadModelDirectives(directives_file) - models = ProcessModelDirectives(directives, plant, parser) - [iiwa, wsg, shelf, binR, binL, table] = models + ProcessModelDirectives(directives, plant, parser) plant.Finalize() diagram = builder.Build() - - FKcontext = diagram.CreateDefaultContext() - FKplant_context = plant.GetMyMutableContextFromRoot(FKcontext) - + context = diagram.CreateDefaultContext() + plant_context = plant.GetMyMutableContextFromRoot(context) + X_list = [] for q in q_list: - plant.SetPositions(FKplant_context, q) - X_list.append(plant.EvalBodyPoseInWorld(FKplant_context, plant.GetBodyByName("body"))) + plant.SetPositions(plant_context, q) + X_list.append( + plant.EvalBodyPoseInWorld(plant_context, + plant.GetBodyByName("body"))) return X_list -def InverseKinematics(q0, translation, rpy): + +def InverseKinematics(q0: Sequence, translation: Sequence, + rpy: Sequence) -> Sequence: + """Returns the joint angles for the given end-effector pose. + + Args: + q0: Initial guess for the joint angles. + translation: Desired end-effector translation. + rpy: Desired end-effector orientation in roll-pitch-yaw. + + Returns: + The joint angles for the given end-effector pose. + """ builder = DiagramBuilder() - plant, scene_graph = AddMultibodyPlantSceneGraph(builder, time_step=0.0) + plant, _ = AddMultibodyPlantSceneGraph(builder, time_step=0.0) parser = Parser(plant) parser.package_map().Add("gcs", GcsDir()) - directives_file = FindModelFile("models/iiwa14_spheres_collision_welded_gripper.yaml") + directives_file = FindModelFile( + "models/iiwa14_spheres_collision_welded_gripper.yaml") directives = LoadModelDirectives(directives_file) - models = ProcessModelDirectives(directives, plant, parser) - [iiwa, wsg, shelf, binR, binL, table] = models + ProcessModelDirectives(directives, plant, parser) plant.Finalize() diagram = builder.Build() - - IKcontext = diagram.CreateDefaultContext() - IKplant_context = plant.GetMyMutableContextFromRoot(IKcontext) - + + context = diagram.CreateDefaultContext() + plant_context = plant.GetMyMutableContextFromRoot(context) gripper_frame = plant.GetBodyByName("body").body_frame() - ik = inverse_kinematics.InverseKinematics(plant, IKplant_context) - ik.AddPositionConstraint( - gripper_frame, [0, 0, 0], plant.world_frame(), - translation, translation) - - ik.AddOrientationConstraint(gripper_frame, RotationMatrix(), plant.world_frame(), - RotationMatrix(RollPitchYaw(*rpy)), 0.001) - + ik = inverse_kinematics.InverseKinematics(plant, plant_context) + ik.AddPositionConstraint(gripper_frame, [0, 0, 0], plant.world_frame(), + translation, translation) + + ik.AddOrientationConstraint(gripper_frame, RotationMatrix(), + plant.world_frame(), + RotationMatrix(RollPitchYaw(*rpy)), 0.001) + prog = ik.get_mutable_prog() q = ik.q() - + prog.AddQuadraticErrorCost(np.identity(len(q)), q0, q) prog.SetInitialGuess(q, q0) result = Solve(ik.prog()) @@ -92,176 +111,387 @@ def InverseKinematics(q0, translation, rpy): q1 = result.GetSolution(q) return q1 -def lower_alpha(plant, inspector, model_instances, alpha, scene_graph): + +def set_transparency_of_models(plant, model_instances, alpha, scene_graph): + """Sets the transparency of the given models.""" + inspector = scene_graph.model_inspector() for model in model_instances: for body_id in plant.GetBodyIndices(model): frame_id = plant.GetBodyFrameIdOrThrow(body_id) - geometry_ids = inspector.GetGeometries(frame_id, Role.kIllustration) - for g_id in geometry_ids: - prop = inspector.GetIllustrationProperties(g_id) - new_props = IllustrationProperties(prop) - phong = prop.GetProperty("phong", "diffuse") + for geometry_id in inspector.GetGeometries(frame_id, + Role.kIllustration): + properties = inspector.GetIllustrationProperties(geometry_id) + phong = properties.GetProperty("phong", "diffuse") phong.set(phong.r(), phong.g(), phong.b(), alpha) - new_props.UpdateProperty("phong", "diffuse", phong) - scene_graph.AssignRole(plant.get_source_id(), g_id, new_props, RoleAssign.kReplace) + properties.UpdateProperty("phong", "diffuse", phong) + scene_graph.AssignRole(plant.get_source_id(), geometry_id, + properties, RoleAssign.kReplace) -def combine_trajectory(traj_list, wait = 2): + +def combine_trajectories( + traj_list: List[PiecewisePolynomial.FirstOrderHold], + wait: float = 2.0) -> PiecewisePolynomial.FirstOrderHold: + """Combines multiple trajectories into one. + + Args: + traj_list: List of trajectories to be combined. + wait: Wait time between trajectories (seconds). + + Returns: + A combined trajectory. + """ knotList = [] - time_delta = 0 time_list = [] for traj in traj_list: knots = traj.vector_values(traj.get_segment_times()).T knotList.append(knots) - + duration = traj.end_time() - traj.start_time() - offset = 0 - try: - offset = time_list[-1][-1] + 0.1 - except: - pass - time_list.append(np.linspace(offset, duration + offset, knots.shape[0])) - - #add wait time + offset = time_list[-1][-1] + 0.1 if time_list else 0 + time_list.append(np.linspace(offset, duration + offset, + knots.shape[0])) + + # Add wait time. if wait > 0.0: - knotList.append(knotList[-1][-1,:]) + knotList.append(knotList[-1][-1, :]) time_list.append(np.array([time_list[-1][-1] + wait])) - - + path = np.vstack(knotList).T time_break = np.hstack(time_list) return PiecewisePolynomial.FirstOrderHold(time_break, path) -def make_traj(path, speed = 2): + +def make_traj(path: np.array, + speed: float = 2) -> PiecewisePolynomial.FirstOrderHold: + """Returns a trajectory for the given path with constant speed. + + Args: + path: The path to be turned into a trajectory. + speed: The speed of the trajectory. + Returns: + A trajectory for the given path with constant speed. + """ t_breaks = [0] - movement_between_segment = np.sqrt(np.sum(np.square(path.T[1:,:] - path.T[:-1,:]), axis = 1)) - for s in movement_between_segment/speed: - t_breaks += [s + t_breaks[-1]] + distance_between_knots = np.sqrt( + np.sum(np.square(path.T[1:, :] - path.T[:-1, :]), axis=1)) + for segment_duration in distance_between_knots / speed: + # Add a small number to ensure all times are increasing. + t_breaks += [segment_duration + 1e-6 + t_breaks[-1]] return PiecewisePolynomial.FirstOrderHold(t_breaks, path) -def get_traj_length(trajectory, bspline = False, weights = None): +def get_traj_length(trajectory: PiecewisePolynomial.FirstOrderHold, + weights: Union[float, np.array] = 1) -> float: + """Returns the length of the trajectory. + + Args: + trajectory: The trajectory to be evaluated. + weights: The weights for the different joint angles. Default is 1 for all joints. + + Returns: + The length of the trajectory. + """ path_length = 0 - if bspline: - knots = trajectory.vector_values(np.linspace(trajectory.start_time(), trajectory.end_time(), 1000)) - else: - knots = trajectory.vector_values(trajectory.get_segment_times()) - - individual_mov = [] - if weights is None: - weights = np.ones(knots.shape[0]) - for ii in range(knots.shape[1] - 1): - path_length += np.sqrt(np.square(knots[:, ii+1] - knots[:, ii]).dot(weights)) - individual_mov.append([np.linalg.norm(knots[j, ii+1] - knots[j, ii]) for j in range(7)]) - + knots = trajectory.vector_values(trajectory.get_segment_times()) + + for i in range(knots.shape[1] - 1): + path_length += np.linalg.norm( + (knots[:, i + 1] - knots[:, i]).dot(weights)) + return path_length -def is_traj_confined(trajectory, regions): - knots = trajectory.vector_values(np.linspace(trajectory.start_time(), trajectory.end_time(), 1000)).T - not_contained_knots = list(filter(lambda knot: any([r.PointInSet(knot) for r in regions.values()]), knots)) - return len(not_contained_knots)/len(knots) +def visualize_trajectory(meshcat, + trajectories: List[ + PiecewisePolynomial.FirstOrderHold], + show_path: bool = False, + robot_configurations: Optional[np.array] = None, + transparency: float = 1.0, + regions: Optional[List[HPolyhedron]] = None) -> None: + """Visualizes the given trajectories in meshcat. -def visualize_trajectory(meshcat, traj_list, show_line = False, iiwa_ghosts = [], alpha = 0.5, regions = []): - if not isinstance(traj_list, list): - traj_list = [traj_list] - - combined_traj = combine_trajectory(traj_list) + Args: + meshcat: The meshcat visualizer. + trajectories: A list of trajectories to be executed with a two second + delay. Supports up to three trajectories. + show_path: If True, the path of the end-effector base frame is shown + for each trajectory in a different color. + robot_configurations: A list of robot configurations to be visualized + statically. + transparency: The transparency of the robot configurations. Values + between 0 and 1. + regions: A list of regions to be visualized. Each region will be + translated to a vpolytope, where the vertices will be used to + visualize the corresponding robot configurations end-effector + position as a point in task space. + """ + + if len(trajectories) > 3: + raise ValueError("Only up to three trajectories are supported.") + combined_traj = combine_trajectories(trajectories) builder = DiagramBuilder() scene_graph = builder.AddSystem(SceneGraph()) plant = MultibodyPlant(time_step=0.0) plant.RegisterAsSourceForSceneGraph(scene_graph) - inspector = scene_graph.model_inspector() - - + parser = Parser(plant, scene_graph) parser.package_map().Add("gcs", GcsDir()) - directives_file = FindModelFile("models/iiwa14_spheres_collision_welded_gripper.yaml") + directives_file = FindModelFile( + "models/iiwa14_spheres_collision_welded_gripper.yaml") directives = LoadModelDirectives(directives_file) - models = ProcessModelDirectives(directives, plant, parser) - [iiwa, wsg, shelf, binR, binL, table] = models - - #add clones versions of the iiwa - if len(iiwa_ghosts): - lower_alpha(plant, inspector, [iiwa.model_instance, wsg.model_instance], alpha,scene_graph) - visual_iiwas = [] - visual_wsgs = [] - iiwa_file = FindResourceOrThrow( - "drake/manipulation/models/iiwa_description/urdf/iiwa14_spheres_collision.urdf") - wsg_file = FindModelFile("models/schunk_wsg_50_welded_fingers.sdf") - - for i, q in enumerate(iiwa_ghosts): - new_iiwa = parser.AddModelFromFile(iiwa_file, "vis_iiwa_"+str(i)) - new_wsg = parser.AddModelFromFile(wsg_file, "vis_wsg_"+str(i)) - plant.WeldFrames(plant.world_frame(), plant.GetFrameByName("base", new_iiwa), RigidTransform()) - plant.WeldFrames(plant.GetFrameByName("iiwa_link_7", new_iiwa), plant.GetFrameByName("body", new_wsg), - RigidTransform(rpy=RollPitchYaw([np.pi/2., 0, 0]), p=[0, 0, 0.114])) - visual_iiwas.append(new_iiwa) - visual_wsgs.append(new_wsg) - lower_alpha(plant, inspector, [new_iiwa, new_wsg], alpha, scene_graph) - index = 0 - for joint_index in plant.GetJointIndices(visual_iiwas[i]): - joint = plant.get_mutable_joint(joint_index) - if isinstance(joint, RevoluteJoint): - joint.set_default_angle(q[index]) - index += 1 - + iiwa, wsg, _, _, _, _ = ProcessModelDirectives(directives, plant, parser) + + # Set transparency of main arm and gripper. + set_transparency_of_models(plant, + [iiwa.model_instance, wsg.model_instance], + transparency, scene_graph) + + # Add static configurations of the iiwa for visalization. + if robot_configurations is not None: + iiwa_file = FindResourceOrThrow( + "drake/manipulation/models/iiwa_description/urdf/iiwa14_spheres_collision.urdf" + ) + wsg_file = FindModelFile("models/schunk_wsg_50_welded_fingers.sdf") + + for i, q in enumerate(robot_configurations): + # Add iiwa and wsg for visualization. + new_iiwa = parser.AddModelFromFile(iiwa_file, f"vis_iiwa_{i}") + new_wsg = parser.AddModelFromFile(wsg_file, f"vis_wsg_{i}") + + # Weld iiwa to the world frame. + plant.WeldFrames(plant.world_frame(), + plant.GetFrameByName("base", new_iiwa), + RigidTransform()) + # Weld wsg to the iiwa end-effector. + plant.WeldFrames( + plant.GetFrameByName("iiwa_link_7", new_iiwa), + plant.GetFrameByName("body", new_wsg), + RigidTransform(rpy=RollPitchYaw([np.pi / 2., 0, 0]), + p=[0, 0, 0.114])) + + set_transparency_of_models(plant, [new_iiwa, new_wsg], + transparency, scene_graph) plant.Finalize() + # Set default joint angles. + if robot_configurations: + plant.SetDefaultPositions( + np.hstack([np.zeros(7)] + robot_configurations)) + else: + plant.SetDefaultPositions(np.zeros(7)) + # Add the trajectory source to the diagram. to_pose = builder.AddSystem(MultibodyPositionToGeometryPose(plant)) - builder.Connect(to_pose.get_output_port(), scene_graph.get_source_pose_port(plant.get_source_id())) + builder.Connect(to_pose.get_output_port(), + scene_graph.get_source_pose_port(plant.get_source_id())) traj_system = builder.AddSystem(TrajectorySource(combined_traj)) - mux = builder.AddSystem(Multiplexer([7 for _ in range(1 + len(iiwa_ghosts))])) + mux = builder.AddSystem( + Multiplexer([7 for _ in range(1 + len(robot_configurations))])) builder.Connect(traj_system.get_output_port(), mux.get_input_port(0)) - - for i, q in enumerate(iiwa_ghosts): - ghost_pos = builder.AddSystem(ConstantVectorSource(q)) - builder.Connect(ghost_pos.get_output_port(), mux.get_input_port(1+i) ) - - - builder.Connect(mux.get_output_port(), to_pose.get_input_port()) + if robot_configurations is not None: + for i, q in enumerate(robot_configurations): + ghost_pos = builder.AddSystem(ConstantVectorSource(q)) + builder.Connect(ghost_pos.get_output_port(), + mux.get_input_port(1 + i)) + + builder.Connect(mux.get_output_port(), to_pose.get_input_port()) meshcat_params = MeshcatVisualizerParams() meshcat_params.delete_on_initialization_event = False meshcat_params.role = Role.kIllustration - # meshcat_params.role = Role.kProximity - meshcat_cpp = MeshcatVisualizer.AddToBuilder(builder, scene_graph, meshcat, meshcat_params) + visalizer = MeshcatVisualizer.AddToBuilder(builder, scene_graph, meshcat, + meshcat_params) meshcat.Delete() diagram = builder.Build() - - if show_line: + + if show_path: X_lists = [] - for traj in traj_list: - #X_list = [ForwardKinematics(k) for k in traj.vector_values(traj.get_segment_times()).T.tolist()] - X_list = ForwardKinematics(traj.vector_values(np.linspace(traj.start_time(), traj.end_time(), 15000)).T.tolist()) + for traj in trajectories: + X_list = ForwardKinematics( + traj.vector_values( + np.linspace(traj.start_time(), traj.end_time(), + 15000)).T.tolist()) X_lists.append(X_list) - - c_list_rgb = [[i/255 for i in (0, 0, 255, 255)],[i/255 for i in (255, 191, 0, 255)],[i/255 for i in (255, 64, 0, 255)]] + + c_list_rgb = [[i / 255 for i in (0, 0, 255, 255)], + [i / 255 for i in (255, 191, 0, 255)], + [i / 255 for i in (255, 64, 0, 255)]] for i, X_list in enumerate(X_lists): pointcloud = PointCloud(len(X_list)) - pointcloud.mutable_xyzs()[:] = np.array(list(map(lambda X: X.translation(), X_list))).T[:] - meshcat.SetObject("paths/" + str(i), pointcloud, 0.015, + pointcloud.mutable_xyzs()[:] = np.array( + list(map(lambda X: X.translation(), X_list))).T[:] + meshcat.SetObject("paths/" + str(i), + pointcloud, + 0.015 + i * 0.005, rgba=Rgba(*c_list_rgb[i])) - - - reg_colors = plt.cm.viridis(np.linspace(0, 1,len(regions))) - reg_colors[:,3] = 1.0 - + + if regions is not None: + reg_colors = plt.cm.viridis(np.linspace(0, 1, len(regions))) + reg_colors[:, 3] = 1.0 + for i, reg in enumerate(regions): - X_reg = ForwardKinematics(spider_web(reg)) - pointcloud = PointCloud(len(reg)) - pointcloud.mutable_xyzs()[:] = np.array(list(map(lambda X: X.translation(), X_reg))).T[:] - meshcat.SetObject("regions/" + str(i), pointcloud, 0.015, + X_reg = ForwardKinematics(VPolytope(reg).vertices().T) + pointcloud = PointCloud(len(X_reg)) + pointcloud.mutable_xyzs()[:] = np.array( + list(map(lambda X: X.translation(), X_reg))).T[:] + meshcat.SetObject("regions/" + str(i), + pointcloud, + 0.015, rgba=Rgba(*reg_colors[i])) - + simulator = Simulator(diagram) - meshcat_cpp.StartRecording() + visalizer.StartRecording() simulator.AdvanceTo(combined_traj.end_time()) - meshcat_cpp.PublishRecording() + visalizer.PublishRecording() + + +def make_result_table(gcs_data: dict, prm_data: dict, + sprm_data: dict) -> pd.DataFrame: + """Returns a table with the results of the given data.""" + gcs_len = np.mean( + [gcs_data[k]['Path Length (rad)'] for k in gcs_data.keys()], axis=1) + prm_len = np.mean( + [prm_data[k]['Path Length (rad)'] for k in prm_data.keys()], axis=1) + sprm_len = np.mean( + [sprm_data[k]['Path Length (rad)'] for k in sprm_data.keys()], axis=1) + + gcs_time = np.mean([gcs_data[k]['Time (ms)'] for k in gcs_data.keys()], + axis=1) + prm_time = np.mean([prm_data[k]['Time (ms)'] for k in prm_data.keys()], + axis=1) + sprm_time = np.mean([sprm_data[k]['Time (ms)'] for k in sprm_data.keys()], + axis=1) + cols = { + "GCS Planner": sum(zip(gcs_len, gcs_time), ()), + "Regular PRM": sum(zip(prm_len, prm_time), ()), + "Shortcut PRM": sum(zip(sprm_len, sprm_time), ()) + } + + index = pd.MultiIndex.from_tuples(sum( + zip([(task_name, 'length (rad)') for task_name in gcs_data.keys()], + [(task_name, 'runtime (ms)') for task_name in gcs_data.keys()]), + ()), + names=["Task", ""]) + + df = pd.DataFrame(data=cols, index=index) + + return df + + +def plot_results(gcs_data: dict, prm_data: dict, sprm_data: dict) -> None: + """Plots the results of the given data.""" + plt.figure(figsize=(4.5, 7)) + + gcs = [gcs_data[k]['Path Length (rad)'] for k in gcs_data.keys()] + prm = [prm_data[k]['Path Length (rad)'] for k in prm_data.keys()] + sprm = [sprm_data[k]['Path Length (rad)'] for k in sprm_data.keys()] + + plt.subplot(2, 1, 1) + _make_bars(gcs, prm, sprm) + plt.ylabel('Trajectory length (rad)') + plt.gca().set_xticklabels([]) + plt.ylim([0, max([np.max(gcs), np.max(prm), np.max(sprm)]) * 1.3]) + plt.legend(loc='upper center', + bbox_to_anchor=(0.5, 1.15), + ncol=3, + fancybox=True) + + gcs = [gcs_data[k]['Time (ms)'] for k in gcs_data.keys()] + prm = [prm_data[k]['Time (ms)'] for k in prm_data.keys()] + sprm = [sprm_data[k]['Time (ms)'] for k in sprm_data.keys()] + + plt.subplot(2, 1, 2) + _make_bars(gcs, prm, sprm, 0) + plt.xlabel('Task') + plt.ylabel('Runtime (ms)') + plt.ylim([ + 0, + max([ + np.mean(gcs) + np.std(gcs), + np.mean(prm) + np.std(prm), + np.mean(sprm) + np.std(sprm) + ]) * 1.9 + ]) + + plt.subplots_adjust(hspace=0.1) + + +def _make_bars(gcs_data, prm_data, sprm_data, round_to=2): + c_list_rgb = [[i / 255 for i in (0, 0, 255, 255)], + [i / 255 for i in (255, 191, 0, 255)], + [i / 255 for i in (255, 64, 0, 255)]] + c_gcs = c_list_rgb[0] + c_prm = c_list_rgb[1] + c_prm_shortcut = c_list_rgb[2] + + width = 0.2 + ticks = np.arange(1, 6) + + bar_options = { + 'zorder': 3, + 'edgecolor': 'k', + } + + text_options = { + 'va': 'bottom', + 'ha': 'center', + 'fontsize': 8, + 'rotation': 90, + } + + gcs_mean = np.mean(gcs_data, axis=1) + gcs_std = np.std(gcs_data, axis=1) + + prm_mean = np.mean(prm_data, axis=1) + prm_std = np.std(prm_data, axis=1) + + sprm_mean = np.mean(sprm_data, axis=1) + sprm_std = np.std(sprm_data, axis=1) + + plt.bar(ticks - width, + gcs_mean, + width, + yerr=gcs_std, + label='GCS', + color=c_gcs, + **bar_options) + plt.bar(ticks, + prm_mean, + width, + yerr=prm_std, + label='PRM', + color=c_prm, + **bar_options) + plt.bar(ticks + width, + sprm_mean, + width, + yerr=sprm_std, + label='Shortcut PRM', + color=c_prm_shortcut, + **bar_options) + + offset = max([max(gcs_mean), max(prm_mean), max(sprm_mean)]) / 50 + + gcs_mean = np.round(gcs_mean, + round_to) if round_to else gcs_mean.astype(int) + prm_mean = np.round(prm_mean, + round_to) if round_to else prm_mean.astype(int) + sprm_mean = np.round(sprm_mean, + round_to) if round_to else sprm_mean.astype(int) + for i, x in enumerate(ticks): + plt.text(x - width, gcs_mean[i] + gcs_std[i] + offset, gcs_mean[i], + **text_options) + plt.text(x, prm_mean[i] + prm_std[i] + offset, prm_mean[i], + **text_options) + plt.text(x + width, sprm_mean[i] + sprm_std[i] + offset, sprm_mean[i], + **text_options) + + plt.xticks(ticks) + plt.grid() diff --git a/reproduction/prm_comparison/planning.py b/reproduction/prm_comparison/planning.py index 90e75b2..54482bf 100644 --- a/reproduction/prm_comparison/planning.py +++ b/reproduction/prm_comparison/planning.py @@ -1,303 +1,143 @@ -from pydrake.planning.common_robotics_utilities import ( - Graph, GraphNode, - GrowRoadMap, - UpdateRoadMapEdges, - QueryPath, - LazyQueryPath, - AddNodeToRoadmap, - NNDistanceDirection, - ShortcutSmoothPath, - SimpleRRTPlannerState, - SimpleRRTPlannerTree, - MakeKinematicLinearBiRRTNearestNeighborsFunction, - MakeKinematicBiRRTExtendPropagationFunction, - MakeKinematicBiRRTConnectPropagationFunction, - MakeBiRRTTimeoutTerminationFunction, - BiRRTPlanSinglePath, PropagatedState) -from pydrake.common import RandomGenerator -import numpy as np +from typing import Tuple, Sequence, Optional +from pydrake.all import (PRMPlanner, PathProcessor, + HolonomicKinematicPlanningSpace, JointLimits, + VoxelizedEnvironmentCollisionChecker, + RobotDiagramBuilder, LoadModelDirectives, + ProcessModelDirectives, PRMPlannerQueryParameters, + PathProcessorParameters) + +from reproduction.util import FindModelFile, GcsDir import pickle -import time -import multiprocessing as mp - -class ClassicalPlanning: - def __init__(self, plant, diagram, collision_step_size, seed): - self.plant = plant - self.diagram = diagram - self.collision_step_size = collision_step_size - - self.PositionUpperLimits = self.plant.GetPositionUpperLimits() - self.PositionLowerLimits = self.plant.GetPositionLowerLimits() - - np.random.seed(seed) - - def make_context(self): - context = self.diagram.CreateDefaultContext() - return self.plant.GetMyContextFromRoot(context) - - def distance_fn(self, q_1, q_2): - return np.linalg.norm(q_2 - q_1) - - def check_state_validity_fn(self, q): - plant_context = self.make_context() - self.plant.SetPositions(plant_context, q) - query_object = self.plant.get_geometry_query_input_port().Eval(plant_context) - return not query_object.HasCollisions() - - def InterpolateWaypoint(self, start, end, ratio): - return start + ratio*(end-start) - - def check_edge_validity_fn(self, start, end): - plant_context = self.make_context() #to make it threadsafe - num_steps = np.ceil(self.distance_fn(start, end)/self.collision_step_size) - - steps = np.arange(int(num_steps)+1) - #TODO sort steps in an alternating way s.t. we approach the middle from both sides - for step in steps: - interpolation_ratio = step / num_steps - interpolated_point = self.InterpolateWaypoint(start, end, interpolation_ratio) - - self.plant.SetPositions(plant_context, interpolated_point) - query_object = self.plant.get_geometry_query_input_port().Eval(plant_context) - - if query_object.HasCollisions(): - return False - - return True - - def shortcut(self, path, max_iter, max_failed_iter, max_backtracking_steps,max_shortcut_fraction, - resample_shortcuts_interval, check_for_marginal_shortcuts): - - shortcutted_path = ShortcutSmoothPath(path, - max_iter, - max_failed_iter, - max_backtracking_steps, - max_shortcut_fraction, - resample_shortcuts_interval, - check_for_marginal_shortcuts, - self.check_edge_validity_fn, - self.distance_fn, - self.InterpolateWaypoint, - np.random.rand) - return shortcutted_path - - -class PRM(ClassicalPlanning): - def __init__(self, plant, diagram, collision_step_size, seed, K, q_init = []): - ClassicalPlanning.__init__(self, plant, diagram, collision_step_size, seed) - self.K = K - self.roadmap = Graph([GraphNode(q) for q in q_init]) - self.roadmap_size = 0 - - def prm_sampling_fn(self): - return np.random.rand(len(self.PositionLowerLimits))*(self.PositionUpperLimits-self.PositionLowerLimits) + self.PositionLowerLimits - - def roadmap_termination_fn(self, current_roadmap_size): - return current_roadmap_size >= self.roadmap_size - - def GrowRoadMap(self, target_size, use_parralel = False): - if target_size <= 0: - raise Exception(f"Target size must be greater then 0") - if target_size <= self.roadmap_size: - raise Exception(f"Target size of {target_size} must be greater then previous size {self.roadmap_size}") - - self.roadmap_size = target_size - data_stats = GrowRoadMap(self.roadmap, self.prm_sampling_fn, self.distance_fn, - self.check_state_validity_fn, self.check_edge_validity_fn, - self.roadmap_termination_fn, self.K, use_parralel, True, False) - return data_stats - - def resetRoadmap(self): - self.roadmap = Graph() - self.roadmap_size = 0 - - def addNodes(self, q_list): - for q in q_list: - AddNodeToRoadmap(q, NNDistanceDirection(), self.roadmap, self.distance_fn, self.check_edge_validity_fn, self.K, False,True,False ) - - self.roadmap_size = self.roadmap.Size() +import numpy as np - def getPath(self, sequence, verbose = False, path_processing = lambda path: path): +class PresplinedPRM: + + def __init__( + self, + edge_step_size: float = 0.05, + env_padding: float = 0.01, + self_padding: float = 0.01, + propagation_step_size: float = 0.5, + grid_size: Tuple[float] = (2.0, 2.0, 2.0), + grid_resolution: float = 0.02, + seed: int = 0, + ): + """Initialize the planner. + + Args: + edge_step_size: The step size to use when checking for collisions + between two configurations. + env_padding: The padding to use when voxelizing the environment. + propagation_step_size: The step size to use when propagating + between two configurations. + grid_size: The size of the voxelized environment. + grid_resolution: The size of each voxel. + seed: The seed to use for the random number generator. + """ + + # Build the scene. + builder = RobotDiagramBuilder(time_step=0.0) + builder.parser().package_map().Add("gcs", GcsDir()) + + directives_file = FindModelFile( + "models/iiwa14_spheres_collision_welded_gripper.yaml") + directives = LoadModelDirectives(directives_file) + ProcessModelDirectives(directives, builder.parser()) + + iiwa_idx = builder.plant().GetModelInstanceByName("iiwa") + wsg_idx = builder.plant().GetModelInstanceByName("wsg") + + named_joint_distance_weights = dict() + named_joint_distance_weights["iiwa_joint_1"] = 1.0 + named_joint_distance_weights["iiwa_joint_2"] = 1.0 + named_joint_distance_weights["iiwa_joint_3"] = 1.0 + named_joint_distance_weights["iiwa_joint_4"] = 1.0 + named_joint_distance_weights["iiwa_joint_5"] = 1.0 + named_joint_distance_weights["iiwa_joint_6"] = 1.0 + named_joint_distance_weights["iiwa_joint_7"] = 1.0 + + builder.plant().Finalize() + joint_limits = JointLimits(builder.plant()) + diagram = builder.Build() + + robot_model_instances = [iiwa_idx, wsg_idx] + + # Build collision checker. + collision_checker = VoxelizedEnvironmentCollisionChecker( + model=diagram, + robot_model_instances=robot_model_instances, + edge_step_size=edge_step_size, + env_collision_padding=env_padding, + self_collision_padding=self_padding, + named_joint_distance_weights=named_joint_distance_weights, + default_joint_distance_weight=1.0, + ) + + collision_checker.VoxelizeEnvironment(grid_size, grid_resolution) + + # Make the planning space. + self.planning_space = HolonomicKinematicPlanningSpace( + collision_checker, joint_limits, propagation_step_size, seed) + self.roadmap = None + + def plan( + self, + sequence: Sequence[np.array], + query_paramters: PRMPlannerQueryParameters, + post_processing_parameters: Optional[PathProcessorParameters] = None + ) -> Tuple[np.array, float]: + """Plan a path through the sequence of configurations. + + Args: + sequence: The sequence of configurations to plan through. + prm_query_parameters: The parameters to use for the PRM. + post_processing_parameters: The parameters to use for post + processing. If None, no post processing is done. + + Returns: + The path through the sequence of configurations and the + total run time. + """ + if self.roadmap is None: + raise Exception("Roadmap not built yet.") path = [sequence[0]] run_time = 0.0 - for start_pt, goal_pt in zip(sequence[:-1], sequence[1:]): - start_time = time.time() - prm_path = QueryPath([start_pt], [goal_pt], self.roadmap, self.distance_fn, - self.check_edge_validity_fn, self.K, - use_parallel=False, - connection_is_symmetric=True, - add_duplicate_states=False, - limit_astar_pqueue_duplicates=True).Path() - - prm_path = path_processing(prm_path) - run_time += time.time() - start_time - - if len(prm_path) == 0: - if verbose: - print(f"Failed between {start_pt} and {goal_pt}") - return None - - path += prm_path[1:] - - return np.stack(path).T, run_time - - - def save(self, filename): - with open(filename, 'wb') as f: - pickle.dump(self.roadmap,f) - - def load(self, filename): - with open(filename, 'rb') as f: - self.roadmap = pickle.load(f) - self.roadmap_size = self.roadmap.Size() - - def idx2value(self, idx): - return self.roadmap.GetNodeImmutable(idx).GetValueImmutable() - - def SubgraphsIdxAndTrees(self, include_tree_fn = lambda node_val: True): - milestones_idx = set(range(len(self.roadmap.GetNodesMutable()))) - subgraphs = [] - subtrees = [] - - while len(milestones_idx) != 0: - #pick a random milestone - root = milestones_idx.pop() - - subgraph = set() - subtree = [] - queue = [{"parent_idx":-1, "node_index": 0, "milestone_idx":root}] - - include_subtree = False - - #start expanding bfs style - tree_index_cnt = 1 - while len(queue) != 0: - to_expand = queue.pop(0) - if to_expand["milestone_idx"] in subgraph: - #already expanded - continue - - subgraph.add(to_expand["milestone_idx"]) - subtree.append(SimpleRRTPlannerState(self.idx2value(to_expand["milestone_idx"]), to_expand["parent_idx"])) - - expand_node = self.roadmap.GetNodeImmutable(to_expand["milestone_idx"]) - if include_tree_fn(self.idx2value(to_expand["milestone_idx"])): - include_subtree = True - - for child_idx in [edge.GetToIndex() for edge in expand_node.GetOutEdgesImmutable()]: - queue.append({"parent_idx":to_expand["node_index"], "node_index": tree_index_cnt, "milestone_idx": child_idx}) - tree_index_cnt += 1 - - if include_subtree: - subgraphs.append(subgraph) - subtrees.append(subtree) - #remove subgraph from milestones - milestones_idx -= set(subgraph) - - return zip(*sorted(zip(subgraphs, subtrees), key = lambda x: -len(x[0]))) - - -class BiRRT(ClassicalPlanning): - def __init__(self, plant, diagram, collision_step_size, seed, step_size, goal_bias, solve_timeout): - ClassicalPlanning.__init__(self, plant, diagram, collision_step_size, seed) - self.step_size = step_size - self.goal_bias = goal_bias - self.solve_timeout = solve_timeout - - def connect_fn(self, nearest, sample, is_start_tree): - total_dist = self.distance_fn(nearest, sample) - total_steps = np.ceil(total_dist / float(self.step_size)).astype(int) - - propagated_states = [] - parent_offset = -1 - current = nearest - for steps in range(total_steps): - current_target = None - target_dist = self.distance_fn(current, sample) - if target_dist > self.step_size: - #interpolate - current_target = self.InterpolateWaypoint(current, sample, self.step_size/target_dist) - - elif target_dist < 1e-6: - break + for start, goal in zip(sequence[:-1], sequence[1:]): + path_result, prm_run_time = PRMPlanner.TimedPlanLazy( + start, goal, query_paramters, self.planning_space, + self.roadmap) + run_time += prm_run_time + + if not path_result.has_solution(): + print(f"Failed between {start} and {goal}") + return None, run_time + + if post_processing_parameters is not None: + processed_path, processing_time = PathProcessor.TimedProcessPath( + path_result.path(), post_processing_parameters, + self.planning_space) + run_time += processing_time + path += processed_path[1:] else: - current_target = sample - - if not self.check_edge_validity_fn(current, current_target): - return propagated_states - - - propagated_states.append(PropagatedState(state=current_target, relative_parent_index=parent_offset)) - parent_offset += 1 - current = current_target - - return propagated_states - - def states_connected_fn(self, source, target, is_start_tree): - return np.linalg.norm(source - target) < 1e-6 - - - def connect(self, start_tree, end_tree, use_parallel): - goal_tree_sampling_bias = len(end_tree)/(len(start_tree) + len(end_tree)) - - def birrt_sampling(): - if np.random.rand() < self.goal_bias: - if np.random.rand() < goal_tree_sampling_bias: - return np.random.choice(end_tree).GetValueImmutable().tolist() - else: - return np.random.choice(start_tree).GetValueImmutable().tolist() - return np.random.rand(len(self.PositionLowerLimits))*(self.PositionUpperLimits - self.PositionLowerLimits) + self.PositionLowerLimits + path += path_result.path()[1:] - nearest_neighbor_fn = MakeKinematicLinearBiRRTNearestNeighborsFunction(distance_fn = self.distance_fn, use_parallel = use_parallel) - - termination_fn = MakeBiRRTTimeoutTerminationFunction(self.solve_timeout) - - propagate_fn = MakeKinematicBiRRTConnectPropagationFunction(self.distance_fn, self.InterpolateWaypoint, self.check_edge_validity_fn, self.step_size) - - start_tree_extended = None - end_tree_extended = None - def goal_bridge_fn(start_tree, start_idx, end_tree, end_idx, is_start_tree): - """used to access final trees""" - nonlocal start_tree_extended, end_tree_extended - start_tree_extended = start_tree - end_tree_extended = end_tree - return None - - connect_result = BiRRTPlanSinglePath( - start_tree=SimpleRRTPlannerTree(start_tree), - goal_tree=SimpleRRTPlannerTree(end_tree), - state_sampling_fn=birrt_sampling, - nearest_neighbor_fn=nearest_neighbor_fn, propagation_fn=propagate_fn, - state_added_callback_fn=None, - states_connected_fn=self.states_connected_fn, - goal_bridge_callback_fn=goal_bridge_fn, - tree_sampling_bias=0.5, p_switch_tree=0.25, - termination_check_fn=termination_fn, - uniform_unit_real_fn=np.random.rand) - return (connect_result, - start_tree_extended.GetNodesImmutable(), - end_tree_extended.GetNodesImmutable()) - - def getPath(self, sequence, verbose = False, path_processing = lambda path: path): - path = [sequence[0]] - run_time = 0.0 - for start_pt, goal_pt in zip(sequence[:-1], sequence[1:]): + return np.stack(path).T, run_time - start_time = time.time() - start_tree = [SimpleRRTPlannerState(start_pt)] - goal_tree = [SimpleRRTPlannerState(goal_pt)] - biRRT_path, _, _ = self.connect(start_tree, goal_tree, False).Path() + def save(self, filename) -> None: + """Save the roadmap to a file. - biRRT_path = path_processing(biRRT_path) - run_time += time.time() - start_time + Args: + filename: The name of the file to save the roadmap to. + """ + with open(filename, 'wb') as f: + pickle.dump(self.roadmap, f) - if len(biRRT_path) == 0: - if verbose: - print(f"Failed between {start_pt} and {goal_pt}") - return None - - path += biRRT_path[1:] - - return np.stack(path).T, run_time + def load(self, filename) -> None: + """Load the roadmap from a file. + Args: + filename: The name of the file to load the roadmap from. + """ + with open(filename, 'rb') as f: + self.roadmap = pickle.load(f) diff --git a/reproduction/prm_comparison/prm_comparison.ipynb b/reproduction/prm_comparison/prm_comparison.ipynb index b5e010f..4b10f71 100644 --- a/reproduction/prm_comparison/prm_comparison.ipynb +++ b/reproduction/prm_comparison/prm_comparison.ipynb @@ -14,31 +14,37 @@ "import numpy as np\n", "import pandas as pd\n", "import multiprocessing as mp\n", - "from IPython.display import HTML, SVG\n", + "from IPython.display import SVG\n", "\n", "from pydrake.geometry import MeshcatVisualizer, MeshcatVisualizerParams, Role, StartMeshcat\n", "from pydrake.geometry.optimization import IrisInConfigurationSpace, IrisOptions\n", "from pydrake.multibody.parsing import LoadModelDirectives, Parser, ProcessModelDirectives\n", "from pydrake.multibody.plant import AddMultibodyPlantSceneGraph\n", - "from pydrake.planning.common_robotics_utilities import SimpleRRTPlannerState\n", "from pydrake.solvers import GurobiSolver, MosekSolver\n", "from pydrake.systems.framework import DiagramBuilder\n", "\n", "from gcs.linear import LinearGCS\n", "from gcs.rounding import randomForwardPathSearch\n", - "from reproduction.prm_comparison.planning import PRM, BiRRT\n", + "from reproduction.prm_comparison.planning import PresplinedPRM\n", + "from pydrake.all import (PRMPlannerCreationParameters, BiRRTPlannerParameters, PRMPlannerCreationParameters, PRMPlannerQueryParameters, PathProcessorParameters)\n", "from reproduction.prm_comparison.helpers import (\n", - " ForwardKinematics,\n", " get_traj_length,\n", " InverseKinematics,\n", - " is_traj_confined,\n", " make_traj,\n", " visualize_trajectory,\n", + " plot_results,\n", + " make_result_table,\n", ")\n", - "from reproduction.util import *\n", "\n", - "GurobiSolver.AcquireLicense()\n", - "MosekSolver.AcquireLicense()" + "from pydrake.all import PRMPlanner,BiRRTPlanner\n", + "from itertools import combinations\n", + "\n", + "from pydrake.all import ChangeOmpNumThreadsWrapper\n", + "\n", + "from reproduction.util import GcsDir, FindModelFile\n", + "\n", + "gurobi_license = GurobiSolver.AcquireLicense()\n", + "mosek_license = MosekSolver.AcquireLicense()" ] }, { @@ -57,7 +63,7 @@ "metadata": {}, "outputs": [], "source": [ - "SEED = 17\n", + "SEED = 0\n", "np.random.seed(SEED)" ] }, @@ -69,41 +75,42 @@ }, "outputs": [], "source": [ + "meshcat.Delete()\n", "builder = DiagramBuilder()\n", "plant, scene_graph = AddMultibodyPlantSceneGraph(builder, time_step=0.0)\n", "parser = Parser(plant)\n", "parser.package_map().Add(\"gcs\", GcsDir())\n", "\n", - "directives_file = FindModelFile(\"models/iiwa14_spheres_collision_welded_gripper.yaml\")\n", + "directives_file = FindModelFile(\"models/iiwa14_welded_gripper.yaml\")\n", "directives = LoadModelDirectives(directives_file)\n", "models = ProcessModelDirectives(directives, plant, parser)\n", - "[iiwa, wsg, shelf, binR, binL, table] = models\n", - " \n", + "[iiwa, wsg, shelf, binR, binL, table] = models\n", + "iiwa_idx = plant.GetModelInstanceByName(\"iiwa\")\n", + "wsg_idx = plant.GetModelInstanceByName(\"wsg\")\n", "\n", "plant.Finalize()\n", "\n", "meshcat_params = MeshcatVisualizerParams()\n", "meshcat_params.delete_on_initialization_event = False\n", "meshcat_params.role = Role.kIllustration\n", - "# meshcat_params.role = Role.kProximity\n", - "meshcat_cpp = MeshcatVisualizer.AddToBuilder(builder, scene_graph, meshcat, meshcat_params)\n", + "visualizer = MeshcatVisualizer.AddToBuilder(builder, scene_graph, meshcat,\n", + " meshcat_params)\n", "\n", "diagram = builder.Build()\n", "\n", "context = diagram.CreateDefaultContext()\n", - "#plant_context = plant.GetMyContextFromRoot(context)\n", "plant_context = plant.GetMyMutableContextFromRoot(context)\n", - "q0 =[0, 0.3, 0, -1.8, 0, 1, 1.57]\n", + "q0 = [0, 0.3, 0, -1.8, 0, 1, 1.57]\n", "plant.SetPositions(plant_context, q0)\n", "diagram.ForcedPublish(context)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# Generate Iris Regions\n", - "### via manual seeds" + "## IRIS Seeds" ] }, { @@ -112,22 +119,38 @@ "metadata": {}, "outputs": [], "source": [ - "milestones = {\"Above Shelve\": [[0.75, 0, 0.9], [0, -np.pi, -np.pi/2]], \n", - " \"Top Rack\": [[0.75, 0, 0.67], [0, -np.pi, -np.pi/2]], \n", - " \"Middle Rack\": [[0.75, 0, 0.41], [0, -np.pi, -np.pi/2]], \n", - " \"Left Bin\": [[0.0, 0.6, 0.22], [np.pi/2, np.pi, 0]], \n", - " \"Right Bin\": [[0.0, -0.6, 0.22], [np.pi/2, np.pi, np.pi]]}\n", - "\n", - "additional_seed_points = {\"Front to Shelve\":np.array([0, 0.2, 0, -2.09, 0, -0.3, np.pi/2]), \n", - " \"Left to Shelve\": np.array([0.8, 0.7, 0, -1.6, 0, 0, np.pi/2]),\n", - " \"Right to Shelve\":np.array([-0.8, 0.7, 0, -1.6, 0, 0, np.pi/2])} \n", - "\n", - "q0 =[0, 0.3, 0, -1.8, 0, 1, 1.57]\n", - "milestone_configurations = {name: InverseKinematics(q0, trans, rot) for name, (trans, rot) in milestones.items()}\n", + "milestones = {\n", + " \"AS\": [[0.75, 0, 0.9], [0, -np.pi, -np.pi / 2]],\n", + " \"TS\": [[0.75, 0, 0.67], [0, -np.pi, -np.pi / 2]],\n", + " \"CS\": [[0.75, 0, 0.41], [0, -np.pi, -np.pi / 2]],\n", + " \"LB\": [[0.0, 0.6, 0.22], [np.pi / 2, np.pi, 0]],\n", + " \"RB\": [[0.0, -0.6, 0.22], [np.pi / 2, np.pi, np.pi]]\n", + "}\n", + "\n", + "additional_seed_points = {\n", + " \"C\": np.array([0, 0.2, 0, -2.09, 0, -0.3, np.pi / 2]),\n", + " \"L\": np.array([0.8, 0.7, 0, -1.6, 0, 0, np.pi / 2]),\n", + " \"R\": np.array([-0.8, 0.7, 0, -1.6, 0, 0, np.pi / 2])\n", + "}\n", + "\n", + "q0 = [0, 0.3, 0, -1.8, 0, 1, 1.57]\n", + "milestone_configurations = {\n", + " name: InverseKinematics(q0, trans, rot)\n", + " for name, (trans, rot) in milestones.items()\n", + "}\n", "\n", "seed_points = {**milestone_configurations, **additional_seed_points}" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generate IRIS Regions\n", + "### via manual seeds" + ] + }, { "cell_type": "code", "execution_count": null, @@ -138,8 +161,10 @@ "iris_options.require_sample_point_is_contained = True\n", "iris_options.iteration_limit = 10\n", "iris_options.termination_threshold = -1\n", - "iris_options.relative_termination_threshold = 0.01\n", - "CORE_CNT = mp.cpu_count() # you may edit this" + "iris_options.relative_termination_threshold = 0.02\n", + "iris_options.num_collision_infeasible_samples = 1\n", + "iris_options.random_seed = SEED\n", + "CORE_CNT = mp.cpu_count() # you may edit this." ] }, { @@ -155,23 +180,30 @@ " context = diagram.CreateDefaultContext()\n", " plant_context = plant.GetMyContextFromRoot(context)\n", " plant.SetPositions(plant_context, seed)\n", - " hpoly = IrisInConfigurationSpace(plant, plant_context, iris_options)\n", - " print(\"Seed:\", seed, \"\\tTime:\", time.time() - start_time, flush=True)\n", + " try:\n", + " hpoly = IrisInConfigurationSpace(plant, plant_context, iris_options)\n", + " except Exception as e:\n", + " print(f\"Seed: {seed} failed with error: {e}\")\n", + " return None\n", + " if verbose:\n", + " print(\"Seed:\", seed, \"\\tTime:\", time.time() - start_time, flush=True)\n", " return hpoly\n", "\n", - "def generateRegions(seed_points, verbose = True):\n", - " seeds = list(seed_points.values()) if type(seed_points) is dict else seed_points\n", + "\n", + "def generateRegions(seed_points, verbose=True):\n", + " seeds = list(\n", + " seed_points.values()) if type(seed_points) is dict else seed_points\n", " regions = []\n", " loop_time = time.time()\n", - " with mp.Pool(processes = CORE_CNT) as pool:\n", + " with mp.Pool(processes=min(CORE_CNT, len(seed_points))) as pool:\n", " regions = pool.starmap(calcRegion, [[seed, verbose] for seed in seeds])\n", - " \n", + "\n", " if verbose:\n", " print(\"Loop time:\", time.time() - loop_time)\n", - " \n", + "\n", " if type(seed_points) is dict:\n", " return dict(list(zip(seed_points.keys(), regions)))\n", - " \n", + "\n", " return regions" ] }, @@ -187,10 +219,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## via pre-generated regions" + "## Or load pre-generated regions" ] }, { @@ -210,15 +243,16 @@ "metadata": {}, "outputs": [], "source": [ - "gcs = LinearGCS({f\"q_{i + 1}\":r for i, r in enumerate(regions.values())})\n", + "gcs = LinearGCS(regions)\n", "SVG(gcs.VisualizeGraph()) " ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# Build PRM" + "# PRM" ] }, { @@ -227,19 +261,42 @@ "metadata": {}, "outputs": [], "source": [ - "collision_step_size = 0.02\n", - "K = 5\n", - "roadmap_size = 15000\n", + "prm_parameters = PRMPlannerCreationParameters()\n", + "prm_parameters.roadmap_size = 10000 \n", + "prm_parameters.num_neighbors = 5\n", + "prm_parameters.max_valid_sample_tries = 100\n", + "prm_parameters.parallelize = True \n", + "\n", + "birrt_parameters = BiRRTPlannerParameters()\n", + "birrt_parameters.tree_sampling_bias = 0.5\n", + "birrt_parameters.p_switch_trees = 0.25\n", + "birrt_parameters.time_limit = 120.0\n", + "birrt_parameters.connection_tolerance = 1e-5\n", + "birrt_parameters.prng_seed = SEED\n", + "birrt_parameters.parallelize_nearest_neighbor = True\n", + "\n", + "query_parameters = PRMPlannerQueryParameters()\n", + "query_parameters.num_neighbors = 5\n", + "query_parameters.parallelize = True\n", "\n", - "#initlize with seeds for fairness\n", - "prm = PRM(plant, diagram, collision_step_size, SEED, K, list(seed_points.values()))" + "postprocessing_parameters = PathProcessorParameters()\n", + "postprocessing_parameters.max_smoothing_shortcut_fraction = 1.0\n", + "postprocessing_parameters.resampled_state_interval = 0.2\n", + "postprocessing_parameters.prng_seed = SEED\n", + "postprocessing_parameters.max_smoothing_iterations = 200\n", + "postprocessing_parameters.max_failed_smoothing_iterations = 200\n", + "postprocessing_parameters.max_backtracking_steps = 0\n", + "postprocessing_parameters.use_shortcut_smoothing = True\n", + "postprocessing_parameters.resample_before_smoothing = True" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "### Load Roadmap if you don't want to wait 15 minutes" + "ChangeOmpNumThreadsWrapper(int(0.95*CORE_CNT)) # We saw Thrashing with 100% CPU usage." ] }, { @@ -248,15 +305,21 @@ "metadata": {}, "outputs": [], "source": [ - "#TODO use wget here to download th prm map from server\n", - "prm.load(os.path.join(GcsDir(), \"data/prm_comparison/15k_presplined.rmp\"))" + "prm = PresplinedPRM(edge_step_size = 0.05,\n", + " env_padding = 0.00,\n", + " self_padding = 0.00,\n", + " propagation_step_size = 0.5,\n", + " grid_size = [1.8, 2.4, 1.6],\n", + " grid_resolution=0.04,\n", + " seed = SEED)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### Or Grow it " + "## Load Roadmap" ] }, { @@ -265,18 +328,15 @@ "metadata": {}, "outputs": [], "source": [ - "solve_timeout = 200\n", - "goal_bias = 0.05\n", - "step_size = 0.1\n", - "birrt = BiRRT(plant, diagram, collision_step_size, SEED, step_size, goal_bias, solve_timeout)" + "prm.load(GcsDir() + \"/data/prm_comparison/roadmap.rmp\")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### Connect via BiRRT first\n", - "Given the knowledge of the Graph of Convex Sets, we will use BiRRT to connect the edges. All of the nodes of the RRT trees will be added to the PRM map" + "## Generate PRM" ] }, { @@ -285,41 +345,33 @@ "metadata": {}, "outputs": [], "source": [ - "pairs_2_connect = [\n", - " [SimpleRRTPlannerState(seed_points[\"Front to Shelve\"]), SimpleRRTPlannerState(seed_points[\"Top Rack\"])],\n", - " [SimpleRRTPlannerState(seed_points[\"Front to Shelve\"]), SimpleRRTPlannerState(seed_points[\"Middle Rack\"])],\n", - " [SimpleRRTPlannerState(seed_points[\"Front to Shelve\"]), SimpleRRTPlannerState(seed_points[\"Left Bin\"])],\n", - " [SimpleRRTPlannerState(seed_points[\"Front to Shelve\"]), SimpleRRTPlannerState(seed_points[\"Right Bin\"])],\n", - " \n", - " [SimpleRRTPlannerState(seed_points[\"Left Bin\"]), SimpleRRTPlannerState(seed_points[\"Above Shelve\"])],\n", - " [SimpleRRTPlannerState(seed_points[\"Left Bin\"]), SimpleRRTPlannerState(seed_points[\"Left to Shelve\"])],\n", - "\n", - " \n", - " [SimpleRRTPlannerState(seed_points[\"Right Bin\"]), SimpleRRTPlannerState(seed_points[\"Above Shelve\"])],\n", - " [SimpleRRTPlannerState(seed_points[\"Right Bin\"]), SimpleRRTPlannerState(seed_points[\"Right to Shelve\"])],\n", - " \n", - " [SimpleRRTPlannerState(seed_points[\"Above Shelve\"]), SimpleRRTPlannerState(seed_points[\"Left to Shelve\"])],\n", - " [SimpleRRTPlannerState(seed_points[\"Above Shelve\"]), SimpleRRTPlannerState(seed_points[\"Right to Shelve\"])],\n", - "]\n", - "\n", - "prm = PRM(plant, diagram, collision_step_size, SEED, K)\n", - "\n", - "start_time = time.time()\n", - "\n", - "for start_tree, goal_tree in pairs_2_connect:\n", - " result, start_tree_extended, end_tree_extended = birrt.connect([start_tree], [goal_tree], use_parallel = True)\n", - " if len(result.Path()) == 0:\n", - " raise Exception(\"Failed to connect nodes\")\n", - " prm.addNodes(list(map(lambda x: x.GetValueImmutable(), start_tree_extended)) + list(map(lambda x: x.GetValueImmutable(), end_tree_extended)))\n", - "\n", - "print(f\"Roadmap extended to {prm.roadmap_size} in {round(time.time()-start_time,2)} s\")" + "ChangeOmpNumThreadsWrapper(1) # Running BiRRT on a single thread is much faster." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "### Continue growing the Roadmap" + "# BiRRT connect.\n", + "splined_path = []\n", + "birrt_times = []\n", + "for start, goal in combinations(seed_points.values(), 2):\n", + " # BiRRT connect.\n", + " result, runtime = BiRRTPlanner.TimedPlan(\n", + " start,\n", + " goal,\n", + " birrt_parameters,\n", + " prm.planning_space,\n", + " )\n", + " birrt_times.append(runtime)\n", + " if result.has_solution():\n", + " splined_path += result.path()\n", + "print(f'Spline number of nodes: {len(splined_path)}')\n", + "print(\n", + " f\"Birrt time: {round(sum(birrt_times), 3)} seconds, {round(max(birrt_times), 3)} seconds max\"\n", + ")\n" ] }, { @@ -328,8 +380,7 @@ "metadata": {}, "outputs": [], "source": [ - "stats = prm.GrowRoadMap(roadmap_size, True)\n", - "print(f'Grow time: {stats[\"growing_time\"]} s') " + "ChangeOmpNumThreadsWrapper(int(0.95*CORE_CNT)) # We saw Thrashing with 100% CPU usage." ] }, { @@ -338,10 +389,14 @@ "metadata": {}, "outputs": [], "source": [ - "prm.save(os.path.join(GcsDir(), \"data/prm_comparison/15k_presplined.rmp\"))" + "splined_path = np.vstack(splined_path)\n", + "prm.roadmap, prm_runtime = PRMPlanner.TimedBuildRoadmap(\n", + " prm_parameters, splined_path, prm.planning_space)\n", + "print(f\"PRM run time: {round(prm_runtime, 3)} seconds\")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -354,48 +409,62 @@ "metadata": {}, "outputs": [], "source": [ - "def getGCSPath(sequence, verbose = False):\n", + "solver_tolerance = 1e-3\n", + "\n", + "\n", + "def getGCSPath(sequence, verbose=False):\n", " path = [sequence[0]]\n", " run_time = 0.0\n", " for start_pt, goal_pt in zip(sequence[:-1], sequence[1:]):\n", - " gcs = LinearGCS(regions.copy())\n", + " gcs = LinearGCS(regions)\n", " gcs.addSourceTarget(start_pt, goal_pt)\n", - " gcs.setRoundingStrategy(randomForwardPathSearch, max_paths = 10, max_trials = 100, seed = SEED)\n", + " gcs.setRoundingStrategy(randomForwardPathSearch,\n", + " max_paths=10,\n", + " max_trials=100,\n", + " seed=SEED)\n", " gcs.setSolver(MosekSolver())\n", - " start_time = time.time()\n", - " waypoints, results_dict = gcs.SolvePath(rounding=True, verbose=False, preprocessing=True)\n", + " gcs.options.solver_options.SetOption(MosekSolver.id(),\n", + " 'MSK_DPAR_INTPNT_TOL_PFEAS',\n", + " solver_tolerance)\n", + " gcs.options.solver_options.SetOption(MosekSolver.id(),\n", + " 'MSK_DPAR_INTPNT_TOL_DFEAS',\n", + " solver_tolerance)\n", + " gcs.options.solver_options.SetOption(MosekSolver.id(),\n", + " 'MSK_DPAR_INTPNT_TOL_REL_GAP',\n", + " solver_tolerance)\n", + " gcs.options.solver_options.SetOption(MosekSolver.id(),\n", + " 'MSK_DPAR_INTPNT_TOL_INFEAS',\n", + " solver_tolerance)\n", + " waypoints, results_dict = gcs.SolvePath(rounding=True,\n", + " verbose=False,\n", + " preprocessing=True)\n", " if waypoints is None:\n", " if verbose:\n", " print(f\"Failed between {start_pt} and {goal_pt}\")\n", " return None\n", "\n", - "# run_time += results_dict[\"preprocessing_stats\"][\"linear_programs\"]\n", " run_time += results_dict[\"relaxation_solver_time\"]\n", - " run_time += results_dict[\"total_rounded_solver_time\"]\n", + " run_time += results_dict[\n", + " \"max_rounded_solver_time\"] # only take the max since rounding can be parallelized\n", " if verbose:\n", - " print(\"Convex Relaxation\", round(results_dict[\"relaxation_cost\"], 3),\n", - " \"min hard result cost\", round(results_dict[\"rounded_cost\"], 3),\n", - " \"Gap\", round((results_dict[\"rounded_cost\"] - results_dict[\"relaxation_cost\"])/results_dict[\"relaxation_cost\"], 3))\n", - " \n", - " path += waypoints.T[1:].tolist()\n", - " return np.stack(path).T, run_time\n", + " print(\n", + " \"Convex Relaxation\", round(results_dict[\"relaxation_cost\"],\n", + " 3), \"min hard result cost\",\n", + " round(results_dict[\"rounded_cost\"], 3), \"Gap\",\n", + " round((results_dict[\"rounded_cost\"] -\n", + " results_dict[\"relaxation_cost\"]) /\n", + " results_dict[\"relaxation_cost\"], 3))\n", "\n", - "def shortcut(path):\n", - " \"\"\"Those values are used by TRI\"\"\"\n", - " return prm.shortcut(np.stack(path),\n", - " max_iter = 200,\n", - " max_failed_iter = 200,\n", - " max_backtracking_steps = 0,\n", - " max_shortcut_fraction = 1.0,\n", - " resample_shortcuts_interval = 0.25,\n", - " check_for_marginal_shortcuts = False)" + " path += waypoints.T[1:].tolist()\n", + " return np.stack(path).T, run_time" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# Demonstration" + "## Demonstration" ] }, { @@ -404,14 +473,19 @@ "metadata": {}, "outputs": [], "source": [ - "demonstration = {\"Above Shelve\": [[0.75, -0.12, 0.9], [0, -np.pi, -np.pi/2]], \n", - " \"Top Rack\": [[0.75, 0.12, 0.67], [0, -np.pi, -np.pi/2]], \n", - " \"Middle Rack\": [[0.75, 0.12, 0.41], [0, -np.pi, -np.pi/2]], \n", - " \"Left Bin\": [[0.08, 0.6, 0.22], [np.pi/2, np.pi, 0]], \n", - " \"Right Bin\": [[-0.08, -0.6, 0.22], [np.pi/2, np.pi, np.pi]]}\n", + "demonstration = {\n", + " \"AS\": [[0.75, -0.12, 0.9], [0, -np.pi, -np.pi / 2]],\n", + " \"TS\": [[0.75, 0.12, 0.67], [0, -np.pi, -np.pi / 2]],\n", + " \"CS\": [[0.75, 0.12, 0.41], [0, -np.pi, -np.pi / 2]],\n", + " \"LB\": [[0.08, 0.6, 0.22], [np.pi / 2, np.pi, 0]],\n", + " \"RB\": [[-0.08, -0.6, 0.22], [np.pi / 2, np.pi, np.pi]]\n", + "}\n", "\n", - "q0 =[0, 0.3, 0, -1.8, 0, 1, 1.57]\n", - "demonstration_configurations = {name: InverseKinematics(q0, trans, rot) for name, (trans, rot) in demonstration.items()}\n" + "q0 = [0, 0.3, 0, -1.8, 0, 1, 1.57]\n", + "demonstration_configurations = {\n", + " name: InverseKinematics(q0, trans, rot)\n", + " for name, (trans, rot) in demonstration.items()\n", + "}" ] }, { @@ -420,32 +494,27 @@ "metadata": {}, "outputs": [], "source": [ - "demo_a = [demonstration_configurations[\"Above Shelve\"],\n", - " demonstration_configurations[\"Top Rack\"]]\n", - "\n", - "demo_b = [demonstration_configurations[\"Top Rack\"],\n", - " demonstration_configurations[\"Middle Rack\"]]\n", - "\n", - "demo_c = [demonstration_configurations[\"Middle Rack\"],\n", - " demonstration_configurations[\"Left Bin\"]]\n", + "task_1 = [demonstration_configurations[\"AS\"],\n", + " demonstration_configurations[\"TS\"]]\n", "\n", - "demo_d = [demonstration_configurations[\"Left Bin\"],\n", - " demonstration_configurations[\"Right Bin\"]]\n", + "task_2 = [demonstration_configurations[\"TS\"],\n", + " demonstration_configurations[\"CS\"]]\n", "\n", - "demo_e = [demonstration_configurations[\"Right Bin\"],\n", - " demonstration_configurations[\"Above Shelve\"]]\n", + "task_3 = [demonstration_configurations[\"CS\"],\n", + " demonstration_configurations[\"LB\"]]\n", "\n", - "demo_f = [demonstration_configurations[\"Above Shelve\"],\n", - " seed_points[\"Left to Shelve\"]]\n", + "task_4 = [demonstration_configurations[\"LB\"],\n", + " demonstration_configurations[\"RB\"]]\n", "\n", + "task_5 = [demonstration_configurations[\"RB\"],\n", + " demonstration_configurations[\"AS\"]]\n", "\n", - "demo_circle = [demonstration_configurations[\"Above Shelve\"],\n", - " demonstration_configurations[\"Top Rack\"],\n", - " demonstration_configurations[\"Middle Rack\"],\n", - " demonstration_configurations[\"Left Bin\"],\n", - " demonstration_configurations[\"Right Bin\"],\n", - " demonstration_configurations[\"Above Shelve\"]]\n", - "\n" + "task_circle = [demonstration_configurations[\"AS\"],\n", + " demonstration_configurations[\"TS\"],\n", + " demonstration_configurations[\"CS\"],\n", + " demonstration_configurations[\"LB\"],\n", + " demonstration_configurations[\"RB\"],\n", + " demonstration_configurations[\"AS\"]]" ] }, { @@ -456,29 +525,55 @@ }, "outputs": [], "source": [ - "execute_demo = demo_circle\n", - "GCS_path, GCS_time = getGCSPath(execute_demo, verbose = True)\n", - "linear_gcs_traj = make_traj(GCS_path, speed = 2)\n", - "print(f\"Linear GCS length: {round(get_traj_length(linear_gcs_traj), 3)}, and time: {round(GCS_time, 3)} s\")\n", + "execute_task = task_circle\n", + "GCS_path, GCS_time = getGCSPath(execute_task, verbose=True)\n", + "linear_gcs_traj = make_traj(GCS_path, speed=2)\n", + "print(\n", + " f\"Linear GCS length: {round(get_traj_length(linear_gcs_traj), 3)}, and time: {round(GCS_time, 3)} s\"\n", + ")\n", "\n", - "PRM_path, PRM_time = prm.getPath(execute_demo, verbose = True)\n", - "prm_traj = make_traj(PRM_path, speed = 2)\n", - "print(f\"PRM length: {round(get_traj_length(prm_traj), 3)}, and time: {round(PRM_time, 3)} s\")\n", + "PRM_path, PRM_time = prm.plan(execute_task, query_parameters)\n", + "prm_traj = make_traj(PRM_path, speed=2)\n", + "print(\n", + " f\"PRM length: {round(get_traj_length(prm_traj), 3)}, and time: {round(PRM_time, 3)} s\"\n", + ")\n", "\n", - "sPRM_path, sPRM_time = prm.getPath(execute_demo, path_processing = shortcut)\n", - "sprm_traj = make_traj(sPRM_path, speed = 2)\n", - "print(f\"Smoothed PRM length: {round(get_traj_length(sprm_traj), 3)}, and time: {round(sPRM_time, 3)} s\")\n" + "sPRM_path, sPRM_time = prm.plan(execute_task, query_parameters,\n", + " postprocessing_parameters)\n", + "sprm_traj = make_traj(sPRM_path, speed=2)\n", + "print(\n", + " f\"Smoothed PRM length: {round(get_traj_length(sprm_traj), 3)}, and time: {round(sPRM_time, 3)} s\"\n", + ")\n" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, + "outputs": [], + "source": [ + "visualize_trajectory(meshcat, [linear_gcs_traj, prm_traj, sprm_traj],\n", + " show_path=True,\n", + " robot_configurations=execute_task,\n", + " transparency=0.3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ - "is_traj_confined(sprm_traj, regions)" + "with open (\"GCS_PRM10k_comparison.html\", \"w\") as f:\n", + " f.write(meshcat.StaticHtml())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison Table for Paper\n" ] }, { @@ -487,12 +582,7 @@ "metadata": {}, "outputs": [], "source": [ - "visualize_trajectory(meshcat,\n", - " [linear_gcs_traj,prm_traj,sprm_traj],\n", - " show_line = True,\n", - " iiwa_ghosts = execute_demo,\n", - " alpha = 0.3,\n", - " regions = [])\n" + "ChangeOmpNumThreadsWrapper(1)" ] }, { @@ -501,15 +591,129 @@ "metadata": {}, "outputs": [], "source": [ - "with open (\"GCS_PRM15k_comparison.html\", \"w\") as f:\n", - " f.write(meshcat.StaticHtml())" + "tasks_for_paper = {\n", + " \"AS->TS\": task_1,\n", + " \"TS->CS\": task_2,\n", + " \"BS->LB\": task_3,\n", + " \"LB->RB\": task_4,\n", + " \"RB->TS\": task_5\n", + "}\n", + "\n", + "gcs_data = {\n", + " task: {\n", + " \"Path Length (rad)\": [],\n", + " \"Time (ms)\": []\n", + " }\n", + " for task, _ in tasks_for_paper.items()\n", + "}\n", + "prm_data = {\n", + " task: {\n", + " \"Path Length (rad)\": [],\n", + " \"Time (ms)\": []\n", + " }\n", + " for task, _ in tasks_for_paper.items()\n", + "}\n", + "sprm_data = {\n", + " task: {\n", + " \"Path Length (rad)\": [],\n", + " \"Time (ms)\": []\n", + " }\n", + " for task, _ in tasks_for_paper.items()\n", + "}\n", + "\n", + "N = 5 # We ran this 100 in the paper\n", + "for _ in range(N):\n", + " for task_name, task in tasks_for_paper.items():\n", + " traj, runtime = getGCSPath(task)[:2]\n", + " traj_len = get_traj_length(make_traj(traj))\n", + " gcs_data[task_name][\"Path Length (rad)\"].append(traj_len)\n", + " gcs_data[task_name][\"Time (ms)\"].append(runtime * 1000)\n", + "\n", + " traj, runtime = prm.plan(task, query_parameters)\n", + " traj_len = get_traj_length(make_traj(traj))\n", + " prm_data[task_name][\"Path Length (rad)\"].append(traj_len)\n", + " prm_data[task_name][\"Time (ms)\"].append(runtime * 1000)\n", + "\n", + " traj, runtime = prm.plan(task, query_parameters,\n", + " postprocessing_parameters)\n", + " traj_len = get_traj_length(make_traj(traj))\n", + " sprm_data[task_name][\"Path Length (rad)\"].append(traj_len)\n", + " sprm_data[task_name][\"Time (ms)\"].append(runtime * 1000)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_results(gcs_data, prm_data, sprm_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "make_result_table(gcs_data, prm_data, sprm_data).round(2)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Comparison Table for Paper\n" + "## Random Configurations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def sample_demonstration_configurations():\n", + " shelve_y_bounds = (-0.12, 0.12)\n", + " shelve_x_bounds = (-0.02, 0.02)\n", + " shelve_z_bounds = (-0.01, 0.01)\n", + "\n", + " bin_x_bounds = (0.08, -0.08)\n", + " bin_y_bounds = (0.02, -0.02)\n", + " bin_z_bounds = (0.01, -0.01)\n", + "\n", + " random_demo = {\n", + " \"AS\": [[\n", + " 0.75 + np.random.uniform(*shelve_x_bounds),\n", + " np.random.uniform(*shelve_y_bounds),\n", + " 0.9 + np.random.uniform(*shelve_z_bounds)\n", + " ], [0, -np.pi, -np.pi / 2]],\n", + " \"TS\": [[\n", + " 0.75 + np.random.uniform(*shelve_x_bounds),\n", + " np.random.uniform(*shelve_y_bounds),\n", + " 0.67 + np.random.uniform(*shelve_z_bounds)\n", + " ], [0, -np.pi, -np.pi / 2]],\n", + " \"CS\": [[\n", + " 0.75 + np.random.uniform(*shelve_x_bounds),\n", + " np.random.uniform(*shelve_y_bounds),\n", + " 0.41 + np.random.uniform(*shelve_z_bounds)\n", + " ], [0, -np.pi, -np.pi / 2]],\n", + " \"LB\": [[\n", + " np.random.uniform(*bin_x_bounds),\n", + " 0.6 + np.random.uniform(*bin_y_bounds),\n", + " 0.22 + np.random.uniform(*bin_z_bounds)\n", + " ], [np.pi / 2, np.pi, 0]],\n", + " \"RB\": [[\n", + " np.random.uniform(*bin_x_bounds),\n", + " -0.6 + np.random.uniform(*bin_y_bounds),\n", + " 0.22 + np.random.uniform(*bin_z_bounds)\n", + " ], [np.pi / 2, np.pi, np.pi]]\n", + " }\n", + " q0 = [0, 0.3, 0, -1.8, 0, 1, 1.57]\n", + " return {\n", + " name: InverseKinematics(q0, trans, rot)\n", + " for name, (trans, rot) in random_demo.items()\n", + " }" ] }, { @@ -518,28 +722,77 @@ "metadata": {}, "outputs": [], "source": [ - "demos_for_paper = [demo_a, demo_b, demo_c, demo_d, demo_e]\n", + "random_gcs_data = {\n", + " task: {\n", + " \"Path Length (rad)\": [],\n", + " \"Time (ms)\": []\n", + " }\n", + " for task, _ in tasks_for_paper.items()\n", + "}\n", + "random_prm_data = {\n", + " task: {\n", + " \"Path Length (rad)\": [],\n", + " \"Time (ms)\": []\n", + " }\n", + " for task, _ in tasks_for_paper.items()\n", + "}\n", + "random_sprm_data = {\n", + " task: {\n", + " \"Path Length (rad)\": [],\n", + " \"Time (ms)\": []\n", + " }\n", + " for task, _ in tasks_for_paper.items()\n", + "}\n", + "\n", + "N = 1000\n", + "gcs_failed = 0\n", + "prm_failed = 0\n", + "sprm_failed = 0\n", + "for _ in range(N):\n", + " random_configuration = sample_demonstration_configurations()\n", + " random_task_1 = [random_configuration[\"AS\"], random_configuration[\"TS\"]]\n", "\n", - "proposed_planner_data = []\n", - "PRM_data = []\n", - "sPRM_data = []\n", + " random_task_2 = [random_configuration[\"TS\"], random_configuration[\"CS\"]]\n", "\n", - "N = 10 # We ran this 100 in the paper \n", - "for _ in range(N):\n", - " #extract trajectory and time \n", - " trajectories = map(lambda d:[getGCSPath(d),prm.getPath(d),prm.getPath(d, path_processing = shortcut)], demos_for_paper)\n", - " #get length of trajectory\n", - " length_time_list = map(lambda traj_time: [[get_traj_length(make_traj(trj)), t] for trj, t in traj_time], trajectories)\n", - " #flatten for pandas\n", - " length_time_list = np.array(list(length_time_list))\n", - " \n", - " proposed_planner_data.append(length_time_list[:,0].flatten())\n", - " PRM_data.append(length_time_list[:,1].flatten())\n", - " sPRM_data.append(length_time_list[:,2].flatten())\n", - " \n", - "proposed_planner_data = np.mean(proposed_planner_data, axis = 0)\n", - "PRM_data = np.mean(PRM_data, axis = 0)\n", - "sPRM_data = np.mean(sPRM_data, axis = 0)" + " random_task_3 = [random_configuration[\"CS\"], random_configuration[\"LB\"]]\n", + "\n", + " random_task_4 = [random_configuration[\"LB\"], random_configuration[\"RB\"]]\n", + "\n", + " random_task_5 = [random_configuration[\"RB\"], random_configuration[\"AS\"]]\n", + "\n", + " random_tasks = {\n", + " \"AS->TS\": random_task_1,\n", + " \"TS->CS\": random_task_2,\n", + " \"BS->LB\": random_task_3,\n", + " \"LB->RB\": random_task_4,\n", + " \"RB->TS\": random_task_5\n", + " }\n", + "\n", + " for task_name, task in random_tasks.items():\n", + " try:\n", + " traj, runtime = getGCSPath(task)[:2]\n", + " traj_len = get_traj_length(make_traj(traj))\n", + " random_gcs_data[task_name][\"Path Length (rad)\"].append(traj_len)\n", + " random_gcs_data[task_name][\"Time (ms)\"].append(runtime * 1000)\n", + " except:\n", + " gcs_failed += 1\n", + "\n", + " try:\n", + " traj, runtime = prm.plan(task, query_parameters)\n", + " traj_len = get_traj_length(make_traj(traj))\n", + " random_prm_data[task_name][\"Path Length (rad)\"].append(traj_len)\n", + " random_prm_data[task_name][\"Time (ms)\"].append(runtime * 1000)\n", + " except:\n", + " prm_failed += 1\n", + "\n", + " try:\n", + " traj, runtime = prm.plan(task, query_parameters,\n", + " postprocessing_parameters)\n", + " traj_len = get_traj_length(make_traj(traj))\n", + " random_sprm_data[task_name][\"Path Length (rad)\"].append(traj_len)\n", + " random_sprm_data[task_name][\"Time (ms)\"].append(runtime * 1000)\n", + " except:\n", + " sprm_failed += 1\n" ] }, { @@ -548,16 +801,28 @@ "metadata": {}, "outputs": [], "source": [ - "cols = {\"Proposed Planner\":proposed_planner_data,\n", - " \"Regular PRM\":PRM_data,\n", - " \"Shortcut PRM\":sPRM_data}\n", - "\n", - "index = pd.MultiIndex.from_tuples([('q1-q2', 'length (rad)'), ('q1-q2', 'runtime (s)'),\n", - " ('q2-q3', 'length (rad)'), ('q2-q3', 'runtime (s)'),\n", - " ('q3-q4', 'length (rad)'), ('q3-q4', 'runtime (s)'),\n", - " ('q4-q5', 'length (rad)'), ('q4-q5', 'runtime (s)'),\n", - " ('q5-q1', 'length (rad)'), ('q5-q1', 'runtime (s)'),], names=[\"Task\", \"\"])\n", - "df = pd.DataFrame(data = cols, index=index)" + "print(f\"GCS failed {gcs_failed} times\")\n", + "print(f\"PRM failed {prm_failed} times\")\n", + "print(f\"SPRM failed {sprm_failed} times\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_results(random_gcs_data, random_prm_data, random_sprm_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open('random_sprm_data', 'wb') as f:\n", + " pickle.dump(random_sprm_data, f)" ] }, { @@ -566,10 +831,11 @@ "metadata": {}, "outputs": [], "source": [ - "df.round(2)" + "make_result_table(random_gcs_data, random_prm_data, random_sprm_data)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -587,6 +853,30 @@ " print(k, regions[k].PointInSet(v), not prm.getPath([q0, v]) is None)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "execute_task = task_1\n", + "GCS_path, GCS_time, relaxed_results = getGCSPath(execute_task, verbose = True)\n", + "\n", + "def extractEdgeFlows(gcs):\n", + " return {e.name(): gcs.relaxed_result.GetSolution(e.phi()) for e in gcs.gcs.Edges()}\n", + "gcs = LinearGCS(regions.copy())\n", + "gcs.addSourceTarget(execute_task[0], execute_task[1])\n", + "gcs.setRoundingStrategy(randomForwardPathSearch, max_paths = 10, max_trials = 100, seed = SEED)\n", + "gcs.setSolver(MosekSolver())\n", + "start_time = time.time()\n", + "waypoints, results_dict = gcs.SolvePath(rounding=True, verbose=False, preprocessing=True)\n", + "task = extractEdgeFlows(gcs)\n", + "\n", + "task = {(k.split(', ')[0][1:],k.split(', ')[1][:-1]): v for k,v in task.items()}\n", + "task = {k: v for k,v in task.items() if not 'source' in k and not 'target' in k}\n", + "task" + ] + }, { "cell_type": "code", "execution_count": null, @@ -611,7 +901,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.6" + }, + "vscode": { + "interpreter": { + "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a" + } } }, "nbformat": 4, diff --git a/reproduction/uav/uav_generated_rooms.ipynb b/reproduction/uav/uav_generated_rooms.ipynb index f2671d6..ab319c9 100644 --- a/reproduction/uav/uav_generated_rooms.ipynb +++ b/reproduction/uav/uav_generated_rooms.ipynb @@ -32,8 +32,8 @@ "from reproduction.uav.helpers import *\n", "from reproduction.util import *\n", "\n", - "g_lic = GurobiSolver.AcquireLicense()\n", - "m_lic = MosekSolver.AcquireLicense()" + "gurobi_license = GurobiSolver.AcquireLicense()\n", + "mosek_license = MosekSolver.AcquireLicense()" ] }, {

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