Improving Pruning and Compression Techniques in Path Planning

Path planning is a well-studied problem in AI. In many scenarios, such as computer games and trip planning, the environments are two dimensional Euclidean planes with traversability constraints. In many scenarios, computing high quality path (optimal or near optimal) is an important and fundamental task and its performance is critical. In this research we focus on improving two state-of-the-art optimal methods, the purely online method Jump Point Search (JPS), and an offline method Compressed Path Databases (CPDs), and yields several state-of-the-art methods.