F-RRT: An Efficient Algorithm for Semi-Constrained Path Planning Problems

Abstract

This paper addresses the challenging problem of Semi-Constrained End-Effector Path Planning for robotic manipulators. This problem arises when complex specifications restrict the end-effector's motion during the execution of industrial tasks. Traditional path planning algorithms often struggle with such problems due to the difficulty of exploring the robot's valid configuration space, or constrained manifold, under these conditions. In this work, we propose a novel sampling-based approach that efficiently navigates the constrained manifold by exploring an alternative space representing the end-effector's degrees of freedom, such as process-related tolerances, throughout the task. This method retains the simplicity of samplingbased techniques. Building on this approach, we introduce the F-RRT algorithm, an adaptation of the renowned RRT planner. F-RRT demonstrates enhanced speed and robustness compared to existing solutions, particularly in complex and cluttered environments.