MAPP: a Scalable Multi-Agent Path Planning Algorithm with Tractability and Completeness Guarantees

by   Ko-Hsin Cindy Wang, et al.

Multi-agent path planning is a challenging problem with numerous real-life applications. Running a centralized search such as A* in the combined state space of all units is complete and cost-optimal, but scales poorly, as the state space size is exponential in the number of mobile units. Traditional decentralized approaches, such as FAR and WHCA*, are faster and more scalable, being based on problem decomposition. However, such methods are incomplete and provide no guarantees with respect to the running time or the solution quality. They are not necessarily able to tell in a reasonable time whether they would succeed in finding a solution to a given instance. We introduce MAPP, a tractable algorithm for multi-agent path planning on undirected graphs. We present a basic version and several extensions. They have low-polynomial worst-case upper bounds for the running time, the memory requirements, and the length of solutions. Even though all algorithmic versions are incomplete in the general case, each provides formal guarantees on problems it can solve. For each version, we discuss the algorithms completeness with respect to clearly defined subclasses of instances. Experiments were run on realistic game grid maps. MAPP solved 99.86 percentage of FAR and WHCA*. MAPP marked 98.82 solvable during the first stage of plan computation. Parts of MAPPs computation can be re-used across instances on the same map. Speed-wise, MAPP is competitive or significantly faster than WHCA*, depending on whether MAPP performs all computations from scratch. When data that MAPP can re-use are preprocessed offline and readily available, MAPP is slower than the very fast FAR algorithm by a factor of 2.18 on average. MAPPs solutions are on average 20


Solving Simultaneous Target Assignment and Path Planning Efficiently with Time-Independent Execution

Real-time planning for a combined problem of target assignment and path ...

SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems

Solving high-dimensional optimal control problems in real-time is an imp...

Revisiting the Complexity Analysis of Conflict-Based Search: New Computational Techniques and Improved Bounds

The problem of Multi-Agent Path Finding (MAPF) calls for finding a set o...

Exploiting Subgraph Structure in Multi-Robot Path Planning

Multi-robot path planning is difficult due to the combinatorial explosio...

Team Coordination on Graphs with State-Dependent Edge Cost

This paper studies a team coordination problem in a graph environment. S...

Multi-agent Planning for thermalling gliders using multi level graph-search

This paper solves a path planning problem for a group of gliders. The gl...

Solving Witness-type Triangle Puzzles Faster with an Automatically Learned Human-Explainable Predicate

Automatically solving puzzle instances in the game The Witness can guide...

Please sign up or login with your details

Forgot password? Click here to reset