Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation
<|reference_start|>Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation: 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.<|reference_end|>
@article{taheri2024deep,
    title={Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot
  Navigation},
    author={Hamid Taheri, Seyed Rasoul Hosseini, Mohammad Ali Nekoui},
    journal={arXiv preprint arXiv:2405.16266},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16266},
    primaryClass={cs.RO cs.LG cs.SY eess.SY}
}