Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation

• URL: http://arxiv.org/abs/2405.16266v2
• Date: Tue, 6 Aug 2024 21:26:31 GMT
• Title: Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation
• Authors: Hamid Taheri, Seyed Rasoul Hosseini, Mohammad Ali Nekoui,
• Abstract summary: 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.
• Score: 0.6554326244334868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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