Maximizing Robot Manipulator’s Functional Redundancy via Sequential Informed Optimization

This paper proposes Sequential Informed Optimization (SIO) method, a computationally-efficient approach for a globally-optimal task-constrained motion planning. It builds upon the idea of using the knowledge of the preceding robot's task and globally-optimum configuration as the intellectual initial guess to guide and plan for the subsequent robot's motion. The proposed approach can be used with any standard constrained nonlinear optimization method. The abilities of the method to plan the globally-optimum motion for a functionally-redundant manipulator, i.e., robot executing `soft' task constraints, are demonstrated in the numerical simulations.