  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.
