A parallel processing scheme is described for robot control computation on MIMD shared memory multi-processor model. Since dynamic control law of robot arm usually contains a large amount of operations compared with the number of available processors, it is important to consider not only the effect of parallel processing but also the efficiency of serial processing in each of the processors. In order to obtain such a desirable solution for the complex scheduling problem, optimization and quasi-optimization algorithms are proposed. The excellent optimization algorithm is based on a branch-and-bound method. On the other hand, the practical quasi-optimization algorithm is based on a partial enumeration method which effectively combines the optimization algorithm and the approximation algorithm (GCF/LPT) ; that was recently developed by authors. The proposed scheduling algorithms are applicable to parallel processing of any kinds of control laws represented by sum of products. The test results on the dynamic control computation of robot arms demonstrate the usefulness of the algorithms.
