The obstacle avoidance control system using deep learning

The obstacle avoidance control using deep learning employs the deep deterministic policy gradient (DDPG) as the deep reinforcement learning algorithm. This algorithm involves two neural networks, including the decision network and the evaluation network, interacting with the environment. The system is utilized the potential field algorithm based on the information of surrounding obstacles and the target position to construct a virtual potential field. Then, the algorithm calculates the acceleration vector at that location as the direction of movement and speed judgment for the unmanned vehicle. By incorporating a small amount of expert operation information as guidance, the algorithm is encouraged to develop better strategies while mimicking the expert. This approach is known as “from demonstration,” imitating and refining strategies more effectively from demonstrated policies.