Reinforcement Learning-Based Optimal Path Planning for Mobile Robot with Obstacles Avoidance

Abstract

This paper presents a deep reinforcement learning (RL) approach for training mobile robots to navigate complex environments using the Twin Delayed Deep Deterministic Policy Gradient (TD3) method, which is known for its stability in continuous control tasks. The robot model simulates realworld bicycle kinematics with nonholonomic constraints and tackles three key navigation tasks: point tracking with obstacle avoidance, linear path following, and circular path tracking. The study focuses on enhancing tasks like point tracking, linear path following, and circular path tracking, aiming to reduce the distance to the goal, minimize tracking errors, and lower control effort over time. This approach replaces traditional methods and significantly improves upon them, enabling the system to reach targets even at points it hasn’t been trained on before, thereby boosting efficiency and adaptability. Synthetic environments with obstacles are created using the MATLAB® Reinforcement Learning toolbox for realistic simulations. The system employs an actor-critic neural network that processes occupancy map data and outputs continuous velocity commands. Evaluations show the approach’s effectiveness in teaching robots collision-free navigation, achieving humanlevel competency in complex environments through iterative learning. This work demonstrates the potential of model-free deep RL for real-world mobile robot navigation.