Multi-agent path finding on real robots

Barták, Roman; Švancara, Jiří; Škopková, Věra; Nohejl, David; Krasičenko, Ivan

doi:10.3233/aic-190621

Cited by 14 publications

(10 citation statements)

References 12 publications

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“…A known research field that considers the synchronization challenge is Multi-Agent Path Finding problem (MAPF) (Pallottino et al 2007;Erdem et al 2013;Surynek et al 2016;Švancara et al 2019;Barták et al 2019;Barták, Švancara, and Krasičenko 2020;Li, Ruml, and Koenig 2021). MAPF describes the problem of moving agents to destinations while avoiding collisions.…”

Section: Planing and Coordination Faultsmentioning

confidence: 99%

Model-Based Diagnosis of Multi-Agent Systems: A Survey

Kalech

Natan

2022

AAAI

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As systems involving multiple agents are increasingly deployed, there is a growing need to diagnose failures in such systems. Model-Based Diagnosis (MBD) is a well-known AI technique to diagnose faults in systems. In this approach, a model of the diagnosed system is given, and the real system is observed. A failure is announced when the real system's output contradicts the model's expected output. The model is then used to deduce the defective components that explain the unexpected observation. MBD has been increasingly being deployed in distributed and multi-agent systems. In this survey, we summarize twenty years of research in the field of model-based diagnosis algorithms for MAS diagnosis. We depict three attributes that should be considered when examining MAS diagnosis: (1) The objective of the diagnosis. Either diagnosing faults in the MAS plans or diagnosing coordination faults. (2) Centralized vs. distributed. The diagnosis method could be applied either by a centralized agent or by the agents in a distributed manner. (3) Temporal vs. non-temporal. Temporal diagnosis is used to diagnose the MAS's temporal behaviors, whereas non-temporal diagnosis is used to diagnose the conduct based on a single observation. We survey diverse studies in MBD of MAS based on these attributes, and provide novel research challenges in this field for the AI community.

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Section: Planing and Coordination Faultsmentioning

confidence: 99%

Model-Based Diagnosis of Multi-Agent Systems: A Survey

Kalech

Natan

2022

AAAI

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“…This problem occurs in many industrial applications that involve agents that have to reach destinations without colliding with each other [16]. For instance, in automated warehouses [15], autonomous vehicles, and robotics [2]. Well-studied approaches include search-based solvers (for instance: HCA* [18]) that make plans agent by agent according to a predefined agent order.…”

Section: Literature Reviewmentioning

confidence: 99%

Shadoks Approach to Low-Makespan Coordinated Motion Planning

Crombez,

da Fonseca,

Gerard

et al. 2021

Preprint

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This paper describes the heuristics used by the Shadoks 1 team for the CG:SHOP 2021 challenge. This year's problem is to coordinate the motion of multiple robots in order to reach their targets without collisions and minimizing the makespan. Using the heuristics outlined in this paper, our team won first place with the best solution to 202 out of 203 instances and optimal solutions to at least 105 of them.

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“…The final example of Colored MAPF is from the area of computer art using mobile robots and drones. Recently, it became popular to use sets of robots to display a picture and to morph it into another picture by moving the robots (Barták and Mestek 2021). Robots may glow different colors, which naturally define sub-groups of robots.…”

Section: Introductionmentioning

confidence: 99%

Colored Multi-Agent Path Finding: Solving Approaches

Barták

Ivanová

Švancara

2021

FLAIRS

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Multi-Agent Path Finding (MAPF) deals with the problem of finding collision-free paths for a set of agents moving in a shared environment while each agent has specified its destination. Colored MAPF generalizes MAPF by defining groups of agents that share a set of destination locations. In the paper, we evaluate several approaches to optimally solve the colored MAPF problem, namely, a method based on network flows, an extended version of conflict-based search, and two models using Boolean satisfiability. We also investigate methods for obtaining lower bounds on optimal solutions based on constraint and continuous relaxation techniques.

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Multi-agent path finding on real robots

Cited by 14 publications

References 12 publications

Model-Based Diagnosis of Multi-Agent Systems: A Survey

Model-Based Diagnosis of Multi-Agent Systems: A Survey

Shadoks Approach to Low-Makespan Coordinated Motion Planning

Colored Multi-Agent Path Finding: Solving Approaches

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