Goal-Oriented Quadruped Navigation in Dynamic Environments Using Reinforcement Learning

Navigating complex and dynamic environments remains a significant challenge for quadruped robots due to unpredictable obstacles, diverse terrains, and the need for realtime adaptability. Traditional methods often rely on model-based control strategies, which struggle to provide the flexibility required for robust navigation in such environments. In this paper, we propose a novel reinforcement learning-based autonomous navigation framework designed to address these limitations. Our method leverages continuous decision-making capabilities, advanced state-space representations, and goal-directed strategies to enable quadruped robots to navigate complex and dynamic environments efficiently and safely. Compared to existing approaches, the proposed method achieves superior obstacle avoidance, terrain adaptability, and precision in goal-oriented tasks by dynamically optimizing robot's navigation policies in real time. Rigorous evaluations on various metrics highlight the effectiveness and robustness of this framework, setting a new standard for the navigation of quadruped robots in dynamic environments.