Parallel Dynamics Computation Using Prefix Sum Operations

Yajue Yang, Yuanqing Wu, Jia Pan

Abstract: A new parallel framework for fast computation of inverse and forward dynamics of articulated robots based on prefix sums (scans) is proposed. We first re-investigate the well-known recursive Newton-Euler formulation for robot dynamics and show that the forward-backward propagation process for robot inverse dynamics is equivalent to two scan operations on certain semigroups. Then, we showed that state-of-the-art forward dynamic algorithms can also be cast into a sequence of scan operations almost completely, 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 computational acceleration is demonstrated.