A Novel GPU-based Parallel Implementation Scheme and Performance Analysis of Robot Forward Dynamics Algorithms
This work addresses the computational bottleneck in robot dynamics simulation for robotics researchers, but it is incremental as it builds on existing algorithms with a new parallelization approach.
The authors tackled the problem of efficiently computing forward dynamics for articulated robots by proposing a unifying parallel implementation scheme based on Lie group notation, which abstracted algorithms into block systems solvable with parallel operations, and they implemented it on a GPU to analyze three algorithms, achieving performance improvements but without specifying concrete numerical gains.
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.