skip to main content


Title: Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks [Position Paper]
The MAPF problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. Applications of MAPF include automated warehouses and autonomous vehicles. Research on MAPF has been flourishing in the past couple of years. Different MAPF research papers make different assumptions, e.g., whether agents can traverse the same road at the same time, and have different objective functions, e.g., minimize makespan or sum of agents' actions costs. These assumptions and objectives are sometimes implicitly assumed or described informally. This makes it difficult to establish appropriate baselines for comparison in research papers, as well as making it difficult for practitioners to find the papers relevant to their concrete application. This paper aims to fill this gap and support researchers and practitioners by providing a unifying terminology for describing common MAPF assumptions and objectives. In addition, we also provide pointers to two MAPF benchmarks. In particular, we introduce a new grid-based benchmark for MAPF, and demonstrate experimentally that it poses a challenge to contemporary MAPF algorithms.  more » « less
Award ID(s):
1817189
NSF-PAR ID:
10099139
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the Symposium on Combinatorial Search (SoCS)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. The Multi-Agent Pathfinding (MAPF) problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. Applications of MAPF include automated warehouses and autonomous vehicles. Research on MAPF has been flourishing in the past couple of years. Different MAPF research papers make different assumptions, e.g., whether agents can traverse the same road at the same time, and have different objective functions, e.g., minimize makespan or sum of agents’ actions costs. These assumptions and objectives are sometimes implicitly assumed or described informally. This makes it difficult to establish appropriate baselines for comparison in research papers, as well as making it difficult for practitioners to find the papers relevant to their concrete application. This paper aims to fill this gap and support researchers and practitioners by providing a unifying terminology for describing common MAPF assumptions and objectives. In addition, we also provide pointers to two MAPF benchmarks. In particular, we introduce a new grid-based benchmark for MAPF, and demonstrate experimentally that it poses a challenge to contemporary MAPF algorithms. 
    more » « less
  2. null (Ed.)
    In many real-world scenarios, the time it takes for a mobile agent, e.g., a robot, to move from one location to another may vary due to exogenous events and be difficult to predict accurately. Planning in such scenarios is challenging, especially in the context of Multi-Agent Pathfinding (MAPF), where the goal is to find paths to multiple agents and temporal coordination is necessary to avoid collisions. In this work, we consider a MAPF problem with this form of time uncertainty, where we are only given upper and lower bounds on the time it takes each agent to move. The objective is to find a safe solution, which is a solution that can be executed by all agents and is guaranteed to avoid collisions. We propose two complete and optimal algorithms for finding safe solutions based on well-known MAPF algorithms, namely, A* with Operator Decomposition (A* + OD) and Conflict-Based Search (CBS). Experimentally, we observe that on several standard MAPF grids the CBS-based algorithm performs better. We also explore the option of online replanning in this context, i.e., modifying the agents' plans during execution, to reduce the overall execution cost. We consider two online settings: (a) when an agent can sense the current time and its current location, and (b) when the agents can also communicate seamlessly during execution. For each setting, we propose a replanning algorithm and analyze its behavior theoretically and empirically. Our experimental evaluation confirms that indeed online replanning in both settings can significantly reduce solution cost. 
    more » « less
  3. The 2D Multi-Agent Path Finding (MAPF) problem aims at finding collision-free paths for a number of agents, from a set of start locations to a set of goal locations in a known 2D environment. MAPF has been studied in theoretical computer science, robotics, and artificial intelligence over several decades, due to its importance for robot navigation. It is currently experiencing significant scientific progress due to its relevance for automated warehouses (such as those operated by Amazon) and other important application areas. In this paper, we demonstrate that some recently developed MAPF algorithms apply more broadly than currently believed in the MAPF research community. In particular, we describe the 3D Pipe Routing (PR) problem, which aims at placing collision-free pipes from given start locations to given goal locations in a known 3D environment. The MAPF and PR problems are similar: a solution to a MAPF instance is a set of blocked cells in x-y-t space, while a solution to the corresponding PR instance is a set of blocked cells in x-y-z space. We show how to use this similarity to apply several recently developed MAPF algorithms to the PR problem, and discuss their performance on real-world PR instances. This opens up a new direction of industrial relevance for the MAPF research community. 
    more » « less
  4. NA (Ed.)
    Conventional Multi-Agent Path Finding (MAPF) problems aim to compute an ensemble of collision-free paths for multiple agents from their respective starting locations to pre-allocated destinations. This work considers a generalized version of MAPF called Multi-Agent Combinatorial Path Finding (MCPF) where agents must collectively visit a large number of intermediate target locations along their paths before arriving at destinations. This problem involves not only planning collisionfree paths for multiple agents but also assigning targets and specifying the visiting order for each agent (i.e. multi-target sequencing). To solve the problem, we leverage the well-known Conflict-Based Search (CBS) for MAPF and propose a novel framework called Conflict-Based Steiner Search (CBSS). CBSS interleaves (1) the conflict resolving strategy in CBS to bypass the curse of dimensionality in MAPF and (2) multiple traveling salesman algorithms to handle the combinatorics in multi-target sequencing, to compute optimal or bounded sub-optimal paths for agents while visiting all the targets. Our extensive tests verify the advantage of CBSS over baseline approaches in terms of computing shorter paths and improving success rates within a runtime limit for up to 20 agents and 50 targets. We also evaluate CBSS with several MCPF variants, which demonstrates the generality of our problem formulation and the CBSS framework. 
    more » « less
  5. Conventional Multi-Agent Path Finding (MAPF) problems aim to compute an ensemble of collision-free paths for multiple agents from their respective starting locations to pre-allocated destinations. This work considers a generalized version of MAPF called Multi-Agent Combinatorial Path Finding (MCPF) where agents must collectively visit a large number of intermediate target locations along their paths before arriving at destinations. This problem involves not only planning collision-free paths for multiple agents but also assigning targets and specifying the visiting order for each agent (i.e., target sequencing). To solve the problem, we leverage Conflict-Based Search (CBS) for MAPF and propose a novel approach called Conflict-Based Steiner Search (CBSS). CBSS interleaves (1) the collision resolution strategy in CBS to bypass the curse of dimensionality in MAPF and (2) multiple traveling salesman algorithms to handle the combinatorics in target sequencing, to compute optimal or bounded sub-optimal paths for agents while visiting all the targets. We also develop two variants of CBSS that trade off runtime against solution optimality. Our test results verify the advantage of CBSS over the baselines in terms of computing cheaper paths and improving success rates within a runtime limit for up to 20 agents and 50 targets. Finally, we run both Gazebo simulation and physical robot tests to validate that the planned paths are executable 
    more » « less