Robot navigation with predictive capabilities using graph learning and Monte Carlo tree search

This article develops a prediction and path planning system based on the graph neural network to navigate a robot in a complex dynamic environment. In particular, the core of this method is to predict those aspects of the future that are directly relevant for planning, including their value, state, and policy. A graph neural network-based method is introduced to encode the interaction between the robot and the surrounding environment. Then, the dynamic model of the environment is learned through the model-based reinforcement learning, and the path is planned using the Monte Carlo tree search method according to the learned model. Finally, simulation studies are given to evaluate the validity and advantage of the obtained algorithm compared with the most recent methods. It has been shown that the proposed method achieves a higher success rate within a less time. Meantime, the oscillatory and freezing problems caused by the short-sightedness of the robot are avoided.