Pathfinding in Games

Commercial games can be an excellent testbed to artificial intelligence (AI) research, being a middle ground between synthetic, highly abstracted academic benchmarks, and more intricate problems from real life. Among the many AI techniques and problems relevant to games, such as learning, planning, and natural language processing, pathfinding stands out as one of the most common applications of AI research to games. In this document we survey recent work in pathfinding in games. Then we identify some challenges and potential directions for future work. This chapter summarizes the discussions held in the pathfinding workgroup

Rectangle expansion A∗ pathfinding for grid maps

AbstractSearch speed, quality of resulting paths and the cost of pre-processing are the principle evaluation metrics of a pathfinding algorithm. In this paper, a new algorithm for grid-based maps, rectangle expansion A∗ (REA∗), is presented that improves the performance of A∗ significantly. REA∗ explores maps in units of unblocked rectangles. All unnecessary points inside the rectangles are pruned and boundaries of the rectangles (instead of individual points within those boundaries) are used as search nodes. This makes the algorithm plot fewer points and have a much shorter open list than A∗. REA∗ returns jump and grid-optimal path points, but since the line of sight between jump points is protected by the unblocked rectangles, the resulting path of REA∗ is usually better than grid-optimal. The algorithm is entirely online and requires no offline pre-processing. Experimental results for typical benchmark problem sets show that REA∗ can speed up a highly optimized A∗ by an order of magnitude and more while preserving completeness and optimality. This new algorithm is competitive with other highly successful variants of A∗

NafisNav: an Indoor Navigation Algorithm for Embedded Systems and based on Grid Maps

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. N. O. Eraghi, F. López-Colino, A. de Castro and J. Garrido, "NafisNav: An indoor navigation algorithm for embedded systems and based on grid maps," 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, 2015, pp. 345-350. doi: 10.1109/ICIT.2015.7125122An important goal in navigation of low cost robots is low memory usage. In this paper, we propose a novel navigation algorithm (NafisNav) suitable for embedded systems with low resources, mainly memory. The proposed path finding algorithm is designed and implemented in grid maps. Unlike existing algorithms, that mainly focus on obtaining the shortest possible path for navigation, the proposed algorithm focuses on reducing memory consumption, even at the cost of not always obtaining the best path. Experimental results show the trade-off between path length and memory consumption that is obtained, comparing it with typical algorithms such as Dijkstra or A*.This work has been supported by the Spanish Ministerio de Ciencia e Innovacion under project TEC2009-09871

Enhancements to Hierarchical Pathfinding Algorithms

In this thesis we study the problem of pathfinding in static grid-based maps. We apply the approach of abstraction and refinement. We abstract the grid map into a graph representation, and use the classic A* algorithm to search for a path in the abstract space, and then refine it into low-level path. We started with a 2013 entry program to the Grid-based Path Planning Competition, and implemented several enhancements to experiment with the tradeoff between memory usage and search speed. Our program returns the refined low-level path incrementally, therefore reduces the first-move lag in large maps. We cache the low-level edge paths during runtime to avoid repeatedly refining the same abstract edge. In the precomputation step we calculate the low-level paths for all of the edges in the abstraction and directly access the data during online search. We also applied the weighted A* algorithm for online abstract pathfinding and show that the search speed can be further increased by sacrificing path optimality. We ran our program with 132 maps and 1,739,340 queries. Results show that caching edge paths increases the search speed by a factor of 4.20 in comparison to returning the path incrementally but without caching. With precomputation, the search speed increases by a factor of 1.00 in comparison to caching edge paths. We show that online pathfinding speed can be increased by using more memory and/or offline storage