A Practical Obstacle Avoidance Method Using Q-Learning with Local Information
Various methods have been proposed for solving the obstacle avoidance problem. However, many of them are based on information that might not be available for robots in real-world settings. We focus on the generalizability and the practical aspects of the problem instead of studying yet another obstacle avoidance method. We propose a simple but robust method based on reinforcement learning for obstacle avoidance using only local information that could be gathered by the sensors on the robot. We train the model with simple and random cases having only static obstacles in a simulated environment and deploy the trained model to an actual robot car. The robot successfully avoided the static and, surprisingly, dynamic obstacles and eventually reached the target.
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