Abhishek Mathur - MRSD Wiki Individual contribution

Added method to incorporate CHOMP planning in Moveit  1 with ROS1

wiki/planning/chomp-planning.md

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+---
+date: 2025-04-30
+title: CHOMP Path Planning for xArm in ROS
+---
+
+CHOMP (Covariant Hamiltonian Optimization for Motion Planning) is a powerful trajectory optimization algorithm that is well-suited for smooth, continuous path planning in robotic manipulators. In the `xarm_ros` framework, CHOMP serves as a plugin within MoveIt! to optimize planned trajectories for xArm robots. It minimizes a cost function that penalizes collisions and trajectory acceleration, allowing for fluid, human-like movements. This article explains how CHOMP works, how it is used in `xarm_ros`, and the practical dos and don'ts when configuring or deploying it.
+
+This article will help you understand the internals of CHOMP, how to enable and configure it for xArm in a MoveIt! planning context, and how to troubleshoot or tune it for improved performance. It is intended for intermediate ROS users who are integrating motion planning with industrial robotic arms.
+
+## Introduction to CHOMP
+
+CHOMP is a trajectory optimization-based planner introduced as an alternative to sampling-based methods like RRT and PRM. While sampling-based methods are probabilistically complete and effective in high-dimensional configuration spaces, they often yield jerky or suboptimal trajectories. CHOMP addresses this by performing gradient-based optimization over an initial trajectory to minimize a cost function that balances smoothness and obstacle avoidance.
+
+In `xarm_ros`, CHOMP can be selected as the motion planner within the MoveIt! planning pipeline. It operates in continuous space and iteratively refines trajectories by considering velocity and acceleration costs, as well as signed distance fields from the planning scene.
+
+### How CHOMP Works
+
+At its core, CHOMP minimizes a functional:
+$$
+\mathcal{F}(\xi) = \mathcal{F}_{\text{smooth}}(\xi) + \lambda \mathcal{F}_{\text{obstacle}}(\xi)
+$$
+Where:
+- $\mathcal{F}_{\text{smooth}}$ penalizes high accelerations,
+- $\mathcal{F}_{\text{obstacle}}$ penalizes configurations close to obstacles using signed distance fields (SDF),
+- $\lambda$ is a weighting parameter that controls the trade-off between smoothness and collision avoidance.
+
+CHOMP uses precomputed gradient information and operates on a discretized version of the trajectory (a sequence of waypoints in time). The optimization iteratively updates the trajectory to reduce total cost using gradient descent.
+
+### Enabling CHOMP in xarm_ros
+
+In MoveIt!, CHOMP is integrated as a planning plugin. To use CHOMP in your xArm robot configuration:
+
+1. Ensure `moveit_chomp_optimizer_adapter` is installed.
+2. Update your `ompl_planning.yaml`:
+
+
+3. Set CHOMP as your default planner in `moveit_config`'s `planning_pipeline_config`:
+
+
+> This is a note. CHOMP relies on well-defined signed distance fields. Use OctoMap or preprocessed collision geometries for reliable performance.
+
+## Pros and Cons
+
+**Strengths**
+- Produces smooth, natural paths.
+- Gradient-based and efficient for medium-complexity scenes.
+- Deterministic: same result for same input.
+
+**Weaknesses**
+- Local optimizer: may get stuck in local minima.
+- Requires a good initial guess.
+- Computationally heavier than RRT for very high DOF or highly constrained spaces.
+
+## Dos and Don’ts
+
+### ✅ DOs
+- **Provide a valid seed trajectory**: Use a straight-line interpolation or a quick sample-based plan.
+- **Tune weights**: Balance between obstacle avoidance and smoothness.
+- **Use OctoMap or accurate collision models**: CHOMP depends on distance fields.
+- **Use for precision tasks**: Ideal when smooth, safe motion is critical.
+- **Combine with Rviz visualizations**: Observe the planned trajectory before execution.
+
+### ❌ DON’Ts
+- **Don’t use with highly dynamic environments**: CHOMP does not replan quickly in real-time.
+- **Avoid poor initial guesses**: CHOMP needs a decent seed to converge well.
+- **Don’t ignore planning time**: Large trajectories and high-resolution scenes take time.
+- **Don’t expect global optimality**: CHOMP is a local method.
+- **Avoid using CHOMP alone for dynamic motion**: It’s not ideal for high-speed, reactive tasks.
+
+## Practical Tips for Using CHOMP with xArm
+
+- **Resolution**: More waypoints yield smoother results but increase compute time.
+- **Visualization**: Always simulate in RViz before executing on hardware.
+- **Hybrid planning**: Use RRTConnect to find a path, then refine with CHOMP.
+- **Benchmark**: Use MoveIt benchmarking tools to compare planners.
+- **Constraint tuning**: Avoid overly tight constraints unless absolutely necessary.
+
+## Summary
+
+CHOMP is a highly effective local trajectory optimizer that enhances motion planning for xArm by generating smooth and safe paths. Its deterministic and gradient-based nature makes it ideal for precision applications. However, it must be used with good initial trajectories and accurate environment models to work well. By combining CHOMP with robust environment sensing and hybrid planning strategies, it becomes a valuable tool for both industrial and research robotics.
+
+## See Also:
+- [MoveIt! Planning Pipelines](/moveit/planning_pipelines/)
+- [xarm_ros Setup Guide](/xarm/setup/)
+- [ROS Navigation Planning Concepts](/ros/navigation/)
+
+## Further Reading
+- [CHOMP: Gradient Optimization Techniques for Efficient Motion Planning](https://homes.cs.washington.edu/~joschu/docs/chomp.pdf)
+- [MoveIt CHOMP Plugin Documentation](https://moveit.picknik.ai/humble/doc/chomp_planner/chomp_planner_tutorial.html)
+- [xArm GitHub Repository](https://github.com/xArm-Developer/xarm_ros)
+
+## References
+- M. Kalakrishnan, S. Chitta, E. Theodorou, P. Pastor, and S. Schaal, “STOMP: Stochastic trajectory optimization for motion planning,” *IEEE ICRA*, 2011.
+- N. Ratliff, M. Zucker, J. A. Bagnell, and S. Srinivasa, “CHOMP: Gradient optimization techniques for efficient motion planning,” *IEEE ICRA*, 2009.
+- MoveIt! Documentation: https://moveit.picknik.ai
+- xArm SDK & ROS support: https://github.com/xArm-Developer/xarm_ros