Experimental Study on Path Planning Algorithms for Warehouse Mobile Robot Based on Reinforcement Learning

Planning in mobile robots is an essential task because it helps to achieve efficient movement in terms of time, computational resources, and safety. Warehouse robots, for example, are equipped with sensors and cameras to avoid obstacles and to move products around the warehouse pick and pack orders. In this project, we have used path-planning algorithms based on Reinforcement Learning (RL), which include Q-learning (QL), State-Action-Reward-State-Action (SARSA), and Expected SARSA (ESARSA). We evaluated these algorithms on a benchmark dataset with different sizes and obstacle densities. Our findings show that QL produces a more optimal path, but the path is risky since it is close to obstacles. On the other hand, SARSA produces a safer path. However, in terms of convergence speed, SARSA is slow, while ESARSA is faster and more stable but with extra computations.