Parallel computation of robot inverse dynamics for high speed motions

The computation of the highly coupled dynamic equations has always posed a bottleneck in real-time dynamic control of robot manipulators. Recent advances in VLSI technology make it possible to implement new algorithms that compute these equations and meet real-time constraints. Parallel processing techniques can now be used to reduce the computation time for models of a highly mathematical nature such as the dynamical modelling of robot manipulators. In this paper an attempt is made to review the subject of the parallel computation of robot dynamics. Moreover, a new and highly efficient technique is introduced to solve this problem. A simplified form of the Lagrange-Euler is divided into subtasks and distributed on to a parallel processing system. The development (parallel processor) system employs several INMOS transputers running the OCCAM concurrent programming language. Further, the system is used to introduce parallelism to robot dynamics through different task allocation strategies. These strategies flow naturally from the Lagrange-Euler formulation. The cost effectiveness and speed of the algorithm are demonstrated by a case study (Stanford arm). Comparisons are made between uniprocessor (von Neumann) and parallel implementations of the algorithm. Several measures such as utilisation, efficiency, and speed up are used to evaluate the performance of the employed networks and task allocations.