Optimization of the computational load of a hypercube supercomputer onboard a mobile robot

Abstract

A combinatorial optimization methodology is developed, which enables the efficient use of hypercube multiprocessors onboard mobile intelligent robots dedicated to time-critical missions. The methodology is implemented in terms of large-scale concurrent algorithms based either on fast simulated annealing, or on nonlinear asynchronous neural networks. In particular, analytic expressions are given for the effect of singleneuron perturbations on the systems' configuration energy. Compact neuromorphic data structures are used to model effects such as prec xdence constraints, processor idling times, and task-schedule overlaps. Results for a typical robot-dynamics benchmark are presented.