Many motion planning methods use Configuration Space to represent a ro
bot manipulator's range of motion and the obstacles which exist in its
environment. The Cartesian to Configuration Space mapping is computat
ionally intensive and this paper describes how the execution time can
be decreased by using parallel processing. The natural tree structure
of the algorithm is exploited to partition the computation into parall
el tasks. An implementation programmed in the occam2 parallel computer
language running on a network of INMOS transputers is described. The
benefits of dynamically scheduling the tasks onto the processors are e
xplained and verified by means of measured execution times on various
processor network topologies. It is concluded that excellent speed-up
and efficiency can be achieved provided that proper account is taken o
f the variable task lengths in the computation.