Abstract
AbstractMany existing multi-agent path finding (MAPF) solvers focus on completeness, speed, or optimization. However, completeness and rapidity are usually in conflict with each other, which makes these algorithms far from satisfactory in practical applications. Motivated by this realistic requirement, we propose an efficient decoupling method to accelerate the solution of large MAPF problems. First, we define the concept of ‘non-essential vertex’-vertices which are not needed to solve a MAPF problem, and a scheme to identify them. Then, a decoupling scheme based on ‘non-essential vertex’ is proposed, which will assign higher priorities to agents whose goal positions are non-essential vertices and lower priorities to agents whose start positions are non-essential vertices. That is, invoking our decoupling algorithm can decouple any given MAPF problem into three subproblems while maintaining the completeness of the solution. All three sub-MAPF problems can be solved sequentially by a complete solver (e.g., CBS or EECBS, etc.), and two of them can also be solved by a prioritized planning algorithm. We have conducted several experiments in different workspaces, and the statistical results show that the proposed decoupling method significantly improves the speed and success rate of existing MAPF solvers with almost no degradation in solution quality when solving problems with high agent density. In addition, the solving efficiency can be further improved if the prioritized planning algorithm is invoked to solve the first and third sub-MAPF problems.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Engineering (miscellaneous),Information Systems,Artificial Intelligence
Reference37 articles.
1. Trüg S, Hoffmann J, Nebel B (2004) Applying automatic planning techniques to airport ground-traffic control-a feasibility study
2. Wurman PR, D’Andrea R, Mountz M (2008) Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI Mag 29(1):9
3. Bennewitz M, Burgard W, Thrun S (2002) Finding and optimizing solvable priority schemes for decoupled path planning techniques for teams of mobile robots. Robot Auton Syst 41(2–3):89–99
4. Stern R, Sturtevant NR, Felner A, Koenig S, Ma H, Walker TT, Li J, Atzmon D, Cohen L, Kumar TS et al (2019) Multi-agent pathfinding: definitions, variants, and benchmarks. In: Twelfth annual symposium on combinatorial search
5. Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107