A Motion Planning Algorithm for Redundant Manipulators Using Rapidly Exploring Randomized Trees and Artificial Potential Fields

This paper presents a novel motion planner for redundant robotic manipulators by utilizing rapidly exploring randomized trees and artificial potential fields. Rapidly exploring randomized trees and artificial potential fields are two well-known navigational strategies in the robotics industry; however, their potential benefits and synergy when implemented together has been largely unexplored. In the proposed motion planner, rapidly exploring randomized trees is first used to determine a suitable path for the end-effector to follow that maneuvers around all obstacles in the robot’s workspace. Once a path has been determined, attractive and repulsive potential fields are implemented at all points along the path and are used in a gradient optimization algorithm to determine joint trajectories to reach the desired location. To supplement the attractive and repulsive potential fields, an orientation field is proposed to minimize the error between the actual end-effector orientation and the desired orientation during joint trajectory planning. The motion planner is examined through both analytical and experimental evaluation using the 7 degrees of freedom Kinova JACO and Kinova Gen3 robotic arms. The conclusions drawn from this work substantiate the efficacy and superiority of the proposed two-stage motion planner for the control of redundant manipulators in obstacle-ridden environments.