Abstract
Pathfinding is a fundamental task in many applications including robotics, computer games, and vehicle navigation. Waypoint graph is often used for pathfinding due to its advantage in specifying the space and obstacles in a region. Currently the waypoint graph based pathfinding suffers from large computation overhead and hence long latency in dynamic environment, where the location of obstacles may change. In this paper, we propose a fast approach for waypoint graph based pathfinding in such scenario. We eliminate unnecessary waypoints and edges to make the graph sparse. And then we design a prediction-based local method to handle the dynamic change in the environment. Extensive simulation has been done and the results show that the proposed approach outperforms existing approaches.
Highlights
Pathfinding is a fundamental task in many applications including robotics, computer games, and vehicle navigation [1,2,3,4]
Pathfinding existing in dynamic environment needs a large amount of computation to reconstruct the waypoint graph and recompute the path based on updated graph
The grid based algorithm is revised to achieve the real shortest path. These methods are designed based on the static environment, which cannot be directly used in dynamic environment due to their large computation overhead when updating the waypoint graph and lack of the motion prediction
Summary
Pathfinding is a fundamental task in many applications including robotics, computer games, and vehicle navigation [1,2,3,4]. Pathfinding existing in dynamic environment needs a large amount of computation to reconstruct the waypoint graph and recompute the path based on updated graph. (i) We improved the waypoint graph model to reduce the computation overhead of pathfinding It generates the sparse graph by eliminating unnecessary nodes and edges with bounded accuracy loss. This paper is based on our previous conference paper [7] In this version, we extend the work to give more detailed waypoint graph model, propose the predictionbased handling of multiple moving obstacles, illustrate the local pathfinding algorithm, and provide more analysis details and simulation results
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