Global path and action planning for mobile robot using a spatiotemporal graph in environments with predictable moving obstacles

Abstract

Global path planning is often used for initial path planning, as it allows robots to search for the feasible paths. However, it is very difficult to perform global path planning in a dynamic environment because it is hard to predict the behavior of moving obstacles. In this paper, we propose a method to perform a global path plan by using a spatiotemporal graph for environments with moving obstacles that perform predictable behavior. We provide a waiting path in a spatiotemporal graph and setting the waiting cost on it. Then, we show that it is possible to adjust the robot's behavior by using A* algorithm from the balance of moving cost and waiting cost. We also show that the global path from the current position to the goal position can be easily updated by using a spatiotemporal graph, even when moving obstacles change from the scheduled behavior. In order to validate the effectiveness of the proposed method, we apply it for return-path planning of an autonomous tour guide robot in a virtual museum. Finally, we show the simulation results and discuss the applicability of the proposed method in a dynamic environment.