Robust Multi-Agent Path Finding

Atzmon, Dor; Stern, Roni; Felner, Ariel; Wagner, Glenn; Barták, Roman; Zhou, Neng-Fa

doi:10.1609/socs.v9i1.18445

Cited by 17 publications

(23 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These are rules designed to ensure that a MAPF solution considers inadvertent delays in execution. A k-robust MAPF plan builds in a sufficient buffer for agents to be delayed up to k time steps without resulting in a conflict (Atzmon et al 2018). When the probability of future delays is known, robustness rules can require that the probability an agent will conflict during execution is lower than a given bound (Wagner and Choset 2017) or be combined with execution policies to guarantee a conflict-free execution .…”

Section: Feasibility Rulesmentioning

confidence: 99%

Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

Stern

Sturtevant

Felner

et al. 2021

SOCS

231

157

View full text Add to dashboard Cite

The multi-agent pathfinding problem (MAPF) 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, autonomous vehicles, and robotics. Research on MAPF has been flourishing in the past couple of years. Different MAPF research papers assume different sets of 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 for establishing 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 facilitate future research and practitioners by providing a unifying terminology for describing the 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.

show abstract

Section: Feasibility Rulesmentioning

confidence: 99%

Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

Stern

Sturtevant

Felner

et al. 2021

SOCS

231

157

View full text Add to dashboard Cite

show abstract

“…In k-robust plans (Atzmon et al 2018) each agent can be delayed up to k times and no collision will occur. To avoid collisions that might occur from delays, agents cannot cross the same location in two closer than k times.…”

Section: Different Types Of Agentsmentioning

confidence: 99%

“…In these cases, agents keep their distance from each other as their plan execution is inaccurate, and hence, they occupy multiple locations. In a k-robust plan (Atzmon et al 2018), each agent can be delayed up to k times and no collision will occur. To avoid collisions that might occur from delays, agents cannot cross the same location in two closer than k times.…”

Section: Introductionmentioning

confidence: 99%

Multi-Train Path Finding

Atzmon

Diei

Rave

2021

SOCS

View full text Add to dashboard Cite

Multi-agent path finding (MAPF) is the problem of moving a set of agents from their individual start locations to their individual goal locations, without collisions. This problem has practical applications in video games, traffic control, robotics, and more. In MAPF we assume that agents occupy one location each time step. However, in real life some agents have different size or shape. Hence, a standard MAPF solution may be not suited in practice for some applications. In this paper, we describe a novel algorithm, based on the CBS algorithm, that finds a plan for moving a set of train-agents, i.e., agents that occupy a sequence of two or more locations, such as trains, buses, planes, or even snakes. We prove that our solution is optimal and show experimentally that indeed such a solution can be found. Finally, we explain how our solution can also apply to agents with any geometric shape.

show abstract

“…where k is the number of agents. f (n) is indeed an upper bound of all goals under n because an abnormal action can be repair by having all non-delayed agents wait one time step (Atzmon et al 2018). In every iteration, A* for MDR expands the state n with the highest f (n) value in the open list.…”

Section: Mdr As a Search Problemmentioning

confidence: 99%

Measuring the Vulnerability of a Multi-Agent Pathfinding Solution

Yoeli

Stern

Atzmon

2021

SOCS

View full text Add to dashboard Cite

Multi-agent pathfinding is the problem of finding a non-interfering paths for a set of agents, such that if the agents follow these paths then each agent will reach its desired destination. Recent years have shown tremendous advances in this field, with optimal and suboptimal algorithms that are able to plan paths for over 100 agents in reasonable time. However, autonomous mobile agents are prime targets for cyber-security attacks, where an adversary may take control over an agent to disrupt the agents execution of their plan. This threat raises two questions. The first question is how much damage can an agent do if it does not follow its plan. The second question is how can one plan a-priori to be as robust as possible to such cyber-attacks. In this work, We provide an answer to both questions. To compute the maximal amount of damage that an adversary agent can do, we define a corresponding graph search problem and solve this problem with A*. Then, we provide a very simple method to choose a solution that is robust to such damages. We demonstrate both algorithms in simulation over standard multi-agent pathfinding domains.

show abstract

Robust Multi-Agent Path Finding

Cited by 17 publications

References 18 publications

Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

Multi-Train Path Finding

Measuring the Vulnerability of a Multi-Agent Pathfinding Solution

Contact Info

Product

Resources

About