A Task Streamlining Approach for Parallel Processing of the Inverse Dynamic Equations With Friction

Abstract

Real-time computation of the inverse dynamics of the robotic manipulators is required for ensuring robust control. This paper presents a modified Newton-Euler algorithm which makes use of symbolic programming for improved computational efficiency. Also, friction is incorporated in the dynamic model for more accurate prediction of the torques. The algorithm is parallelized using a “Task Streamlining Approach,”—a systematic mapping scheme using layered task graphs to create the list schedule and a simplified bin-packing heuristic algorithm to schedule the computations on a multiprocessor. The resulting computational load is only 12n + 9 flops (n = number of links in the manipulator), indicating a promise for application to precision robot control employing a high sampling rate. The optimal scheduling of tasks is carried out by minimizing the number of layers where tasks are performed sequentially.