Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation

Autor:	Taheri, Hamid, Hosseini, Seyed Rasoul, Nekoui, Mohammad Ali
Rok vydání:	2024
Předmět:	Computer Science - Robotics Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	Collision-free motion is essential for mobile robots. Most approaches to collision-free and efficient navigation with wheeled robots require parameter tuning by experts to obtain good navigation behavior. This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. The robot utilizes LiDAR sensor data and a deep neural network to generate control signals guiding it toward a specified target while avoiding obstacles. We employ two reinforcement learning algorithms in the Gazebo simulation environment: Deep Deterministic Policy Gradient and proximal policy optimization. The study introduces an enhanced neural network structure in the Proximal Policy Optimization algorithm to boost performance, accompanied by a well-designed reward function to improve algorithm efficacy. Experimental results conducted in both obstacle and obstacle-free environments underscore the effectiveness of the proposed approach. This research significantly contributes to the advancement of autonomous robotics in complex environments through the application of deep reinforcement learning. Comment: This paper is under review by Int. J. of Intelligent Machines and Robotics
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.16266 Zobrazit plný text záznamu View this record from Arxiv