A Novel GPU-based Parallel Implementation Scheme and Performance Analysis of Robot Forward Dynamics Algorithms

TL;DR

This paper introduces a GPU-based parallel implementation scheme for articulated robot forward dynamics algorithms, unifying various methods through Lie group notation and efficient parallel solvers, and compares their performance on Nvidia CUDA GPUs.

Contribution

It presents a novel unifying scheme for parallel robot dynamics algorithms using Lie group notation and efficient parallel solvers, enabling comparative performance analysis.

Findings

The scheme effectively unifies different forward dynamics algorithms.

Parallel solvers significantly improve computational efficiency.

Performance analysis on GPU demonstrates the scheme's advantages.

Abstract

We propose a novel unifying scheme for parallel implementation of articulated robot dynamics algorithms. It is based on a unified Lie group notation for deriving the equations of motion of articulated robots, where various well-known forward algorithms differ only by their joint inertia matrix inversion strategies. This new scheme leads to a unified abstraction of state-of-the-art forward dynamics algorithms into combinations of block bi-diagonal and/or block tri-diagonal systems, which may be efficiently solved by parallel all-prefix-sum operations (scan) and parallel odd-even elimination (OEE) respectively. We implement the proposed scheme on a Nvidia CUDA GPU platform for the comparative study of three algorithms, namely the hybrid articulated-body inertia algorithm (ABIA), the parallel joint space inertia inversion algorithm (JSIIA) and the constrained force algorithm (CFA), and the performances are analyzed.