Exploring feasibility maps for trajectory planning of redundant manipulators using RRT

Abstract

Redundant manipulators offer several advantages, including improved manipulability, singularity avoidance, and obstacle evasion. However, kinematic redundancy also introduces additional challenges, such as the need to solve an underdetermined inverse kinematic problem to control the manipulator. This paper introduces a novel approach for motion planning of redundant manipulators, based on the exploration of feasibility maps. The proposed method is an extension of the RRT algorithm, modified to explore the redundant space in order to find a suboptimal feasible path in the joint space, sacrificing optimality for scalability to higher degrees of redundancy. The method is able to follow a given task trajectory while considering other constraints, such as joint limits, self-collisions, and obstacles.