Motion Planning for Kinematically Redundant Mobile Manipulators with Genetic Algorithm, Pose Interpolation, and Inverse Kinematics

Abstract

Motion planning for kinematic redundancy is an area of great importance for maximizing the mobility of robotic systems. However, generating optimized motions for this type of system is a challenging task given the large search space of possible configurations. Previously proposed methods do not address path following tasks with constrained end-effector position and orientation for a mobile manipulator system with more than 6 degrees of freedom (DoF). This paper presents a novel computational method for simultaneous optimization of base and manipulator robotic system with 8 DoF for welding tasks, constraining both end-effector position and orientation. The mobile manipulator consists of a 2 DoF non-holonomic base and a 6 DoF manipulator. The proposed method applies a Genetic Algorithm (GA) to solve for optimized configurations for the base and manipulator for strategically sampled end-effector waypoints. The base configurations and end-effector orientations are interpolated between the GA solutions and used as inputs for an inverse kinematics solver to find the optimal manipulator pose. The experiment results show that the proposed methods create optimized smooth and continuous motions for both the base and manipulator while constraining the end-effector position and orientation. The proposed method is a novel application of GA optimization, with improved results for path following motion planning by including sampling, interpolation, and inverse kinematics steps within the methodology.