Path planning for redundant robot manipulators: a global optimization approach using evolutionary search

Global optimization approaches to path planning for kinematically redundant manipulators, involving the optimization of integral cost criteria have been shown to exhibit certain advantages compared with local optimizers based on instantaneous criteria. We present some initial results of an approximation approach based on the direct search evolution strategy paradigm with specific modifications to encourage discovery of global optima and to explore in spaces with non-simply-connected topologies. In our problem formulation the path of the end effector is unconstrained, except at the end points, and consequently there exist an infinite number of possible hand paths, from which we seek that which gives the global minimum of an integral squared jerk cost functional.