More Flexible Proximity Wildcards Path Planning with Compressed Path Databases

Xi Chen, Yue Zhang, Yonggang Zhang

Keywords: intelligent planning, path planning, compressed path database, proximity wildcards

Abstract: Grid-based path planning is one of the familiar issues in AI, and a popular topic in application areas such as computer games and robotics. Compressed Path Databases (CPDs) are recognized as a state-of-the-art method for grid-based path planning. It is able to find an optimal path extremely fast without a state-space search. In recent years, researchers tend to focus on improving CPDs from reducing CPD size or improving lookup performance. Among various methods, proximity wildcards is one of the most proven improvements in reducing the size of CPD. However, its proximity area is significantly restricted by complex terrain, which has more significant impacts on pathfinding efficiency and generates more additional costs. In this paper we enhance CPDs from the perspective of improving search efficiency and reducing search costs. Our work is to break the limitation between length and width of the proximity area, and adopt more flexible approaches to avoid obstacles, so as to reduce its impact on the proximity area and improve the search efficiency. Experiments performed on the benchmarks from Grid-Based Path Planning Competition (GPPC) demonstrate that the two proposed methods can effectively improve search efficiency and reduce the search costs by 2-3 orders of magnitude. Remarkably, our methods can further reduce storage costs, and improve compression capability of CPDs simultaneously.

Category: Long

Student: Graduate

Submission Number: 6