Convergence properties of gradient-based numerical motion-optimizations for manipulator arms amid static or moving obstacles

Abstract

This paper demonstrates the convergence stability and the actual usefulness of the gradient-based motion optimizations for manipulator arms. An optimal motion-planning problem is converted into a finite-dimensional nonlinear programming problem that utilizes cubic or quintic B-splines as basis functions. This study shows that the numerically calculated gradient is a useful tool in finding minimum torque, minimum energy, minimum overload, and minimum time motions for manipulator arms in the presence of static or moving obstacles. A spatial 6-link manipulator is simulated without simplifying any of the kinematic, dynamic or geometric properties of the manipulator or obstacles.