📢 Project Archive Notice

This repository has been archived and represents the code from the original GRiD paper. Please do not open issues or pull requests here. Active maintenance, updates, and discussions have moved to the new official repository: A2R-Lab/RBDReference

RBDReference

A Python reference implementation of rigid body dynamics algorithms.

This package is designed to enable rapid prototyping and testing of new algorithms and algorithmic optimizations. If your favorite rigid body dynamics algorithm is not yet implemented please submit a PR with the implementation. We'll then try to get a GPU, FPGA, and/or accelerator implementation designed as soon as possible.

Usage and API:

This package relies on an already parsed robot object from our URDFParser package.

RBDReference = RBDReference(robot)
outputs = RBDReference.ALGORITHM(inputs)

Currently implemented algorithms include the:

• Recursive Newton Euler Algorithm (RNEA): (c,v,a,f) = rbdReference.rnea(q, qd, qdd = None, GRAVITY = -9.81)
• The Gradient of the RNEA: dc_du = rnea_grad(q, qd, qdd = None, GRAVITY = -9.81) where dc_du = np.hstack((dc_dq,dc_dqd))
• The Direct Inverse of the Mass Matrix Algorithm: Minv = rbdReference.minv(q, output_dense = True)

We also include functions that break these algorithms down into there different passes and by their output types (dq vs dqd) to enable easier testing of downstream GPU, FPGA, and accelerator implementations.

Instalation Instructions::

The only external dependency is numpy which can be automatically installed by running:

pip3 install -r requirements.txt

This package also depends on our URDFParser package.