Parallel Dynamics Computation using Prefix Sum Operations

TL;DR

This paper introduces a parallel computation framework for robot dynamics using prefix sum operations, enabling faster inverse and forward dynamics calculations on GPUs with significant performance improvements.

Contribution

It reformulates robot dynamics computations as scan operations, enabling efficient parallel algorithms and hybrid approaches for systems with moderate links.

Findings

Significant acceleration over CPU-based algorithms

Reformulation of dynamics as scan operations

Effective GPU implementation with CUDA

Abstract

We propose a new parallel framework for fast computation of inverse and forward dynamics of articulated robots based on prefix sums (scans). We re-investigate the well-known recursive Newton-Euler formulation of robot dynamics and show that the forward-backward propagation process for robot inverse dynamics is equivalent to two scan operations on certain semigroups. We show that the state-of-the-art forward dynamics algorithms may almost completely be cast into a sequence of scan operations, with unscannable parts clearly identified. This suggests a serial-parallel hybrid approach for systems with a moderate number of links. We implement our scan based algorithms on Nvidia CUDA platform with performance compared with multithreading CPU-based recursive algorithms; a significant level of acceleration is demonstrated.