A Novel Chaos-Focused D* Algorithm for Robust Path Planning of a 3R Robotic Arm in Extreme Obstacle Environments

In the robotic Field, Manipulator obstacle avoidance is one of the most highlighted aspects where robots can ensure secure and reliable movement in inaccessible environments. Including the development of algorithms that might assist the manipulators in generating a collision-free trajectory while co-operating with their kinematic constraints and working space. Research in this area explores various techniques for path planning, proximity sensing, reactive control and static and dynamic obstacle avoidance. Previously, several path planning algorithms have been proposed; among them is a novel path planner developed in this paper called Chaos-Focused D*, which has been tackled for improving reliability and versatility of path planning with 3R robotic arms subjected to a high-density obstacle environment. The proposed strategy is based on the deterministic computational efficiency of the Focused D* algorithm and the adaptive exploration ability of chaotic sequences to ensure that the planner can escape local traps and traverse alternative candidate paths without losing sight of the goal. A combined workspace classification scheme is used to define and identify areas as reachable, singular, obstructed or unreachable. Obstacle avoidance is achieved via a link-segmentation method in which each of the three links that the manipulator has is searched against geometric limits of obstacles with added buffer space in order to ensure safe passage, even around irregular geometries. The proposed algorithm has been evaluated on three increasingly difficult conditions and compared to the classical Focused D*. It is found that Chaos-Focused D* reaches higher success rates, especially when dense static obstacle scenarios are used, and the joint motions remain smooth and efficient. The current results suggest that it has a high potential for actual robotic navigation tasks that need to be adaptable as well as efficient.