Decoupled parallel recursive Newton-Euler algorithm for inverse dynamics

An efficient parallel implementation of the robot inverse dynamics based on the recursive Newton-Euler formulation is considered. The algorithm basically partitions the computations related to a manipulator link among separate processors, resulting in a parallel architecture in which the number of processors equals the number of degrees of freedom of the manipulator. This has considerably low inherent parallelism if the forward propagation of velocities and accelerations and the backward propagation of forces and torques are synchronized. Therefore, in order to maximize the parallelism, the synchronization is completely removed, leaving each processor on the most recent values of the propagated variables. Since the mathematical approach for error analysis is too complex and the simulation approach is incomplete and unreliable, the algorithm is investigated on a real, multiprocessor machine, KUMARAN. A series of experiments with the model of the PUMA 560 manipulator and randomly generated inputs has shown surprisingly small errors. Some improvements of the algorithm which do not impair the time efficiency but provide further reduction of the approximation error are presented