Autonomous Navigation and Obstacle Avoidance for Mobile Robots in Dynamic Environments
Keywords:
Mobile Robotics, Autonomous Navigation, Reinforcement Learning, Obstacle Avoidance, Sensor FusionAbstract
The deployment of mobile robots in highly dynamic environments introduces significant challenges regarding real-time autonomous navigation and safe obstacle avoidance. Traditional navigation algorithms often fail to account for the unpredictable nature of moving obstacles, resulting in sub-optimal path planning, elevated collision rates, and computational bottlenecks. This paper presents a comprehensive algorithmic framework that integrates traditional local planning techniques with advanced deep reinforcement learning strategies to enable robust autonomous navigation in environments populated by static and dynamic obstacles. By reformulating the navigation problem as a Partially Observable Markov Decision Process, the proposed system leverages spatial-temporal sensor fusion from LiDAR and RGB-D cameras to continuously update the state representation. A hybrid architecture combining the Dynamic Window Approach with a Proximal Policy Optimization network is introduced, facilitating continuous action space control while guaranteeing kinematic feasibility. Extensive simulations conducted in Robot Operating System environments demonstrate that our hybrid approach significantly outperforms baseline algorithms in terms of trajectory smoothness, success rate, and collision avoidance in high-density dynamic scenarios. The findings indicate a paradigm shift in how control architectures can be synthesized to balance the interpretability of classical mechanics with the adaptive capabilities of machine learning.
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