動的障害物とロボットの相互作用を考慮した将来予測に関する研究

抄録

For autonomous mobile robots to act in society, it is important to predict the action of dynamic obstacles. In this paper, we use state representation learning, a form of deep learning, to obtain a predictive model from 2D-LiDAR and the robot’s action that considers the interaction between the robot and dynamic obstacles. This predicts the future for possible actions taken by the robot. The prediction results are compared with GRU, a form of representation learning. We also describe how to use the prediction results for conventional motion planning. As for as we know, there are few cases where prediction results are reflected in motion planning.