Motion and Path Planning of Mobile Robot for Autonomous Navigation with A* and Q-learning Algorithms

Abstract

Mobile manipulators are widely used in manufacturing industries. This paper introduces a hybrid navigation method combining A* and Q-learning algorithms for a mobile robot equipped with a 6-DOF manipulator arm for pick and place operation. The robot autonomously navigates in the environments by scanning surroundings with laser scanners and mapping in ROS, enabling obstacle avoidance and trajectory optimization. The A* algorithm handles global path planning, generating optimal routes, while Q-learning manages local planning by adapting to real-time changes. Results demonstrates environment mapping in RViz with the robot's ability to navigate from start to goal state and the advantages of the hybrid approach in partially or fully known static environments.