Constraint-based multi-robot path planning

Ryan, Malcolm

doi:10.1109/robot.2010.5509582

Cited by 45 publications

(35 citation statements)

References 7 publications

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“…[1], [11]) and Constraint Programming (e.g. [8]). Approximation algorithms are solutions that provide a trade-off between the fast heuristics and the optimal algorithms, as they can solve the problem in a reasonable amount of time while maintaining high solution quality.…”

Section: A Conventional Solution Approachesmentioning

confidence: 99%

“…Change in distance D j resulted by pursuing direction j is included as a normalized value that corresponds to the change in meters towards the goal destination. The normalized values are defined as follows: From the example can be observed that the second direction that can be pursued (1,8) results in a positive change in the distance towards the goal destination, and when choosing the corresponding action, the AGV can take 8 consecutive greedy steps towards its goal. The third direction (-1,10) is also promising, as the AGV can continue its greedy path to the goal for 10 consecutive steps by taking a small detour.…”

Section: B Improving Whca*s Using Drl (Whca*s-rl)mentioning

confidence: 99%

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Combining Deep Reinforcement Learning with Search Heuristics for Solving Multi-Agent Path Finding in Segment-based Layouts

Reijnen

Zhang

Nuijten

et al. 2020

2020 IEEE Symposium Series on Computational Intelligence (SSCI)

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DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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Section: A Conventional Solution Approachesmentioning

confidence: 99%

Section: B Improving Whca*s Using Drl (Whca*s-rl)mentioning

confidence: 99%

Combining Deep Reinforcement Learning with Search Heuristics for Solving Multi-Agent Path Finding in Segment-based Layouts

Reijnen

Zhang

Nuijten

et al. 2020

2020 IEEE Symposium Series on Computational Intelligence (SSCI)

View full text Add to dashboard Cite

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“…(2) Reduction-based solvers. By contrast, many recent optimal solvers reduce MAPF to known problems such as CSP [20], SAT [21], Inductive Logic Programming [22] and Answer Set Programming [23].…”

Section: Related Workmentioning

confidence: 99%

Finding Optimal Solutions to Token Swapping by Conflict-Based Search and Reduction to SAT

Surynek

2018

2018 IEEE 30th International Conference on Tools With Artificial Intelligence (ICTAI)

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We study practical approaches to solving the token swapping (TSWAP) problem optimally in this short paper. In TSWAP, we are given an undirected graph with colored vertices. A colored token is placed in each vertex. A pair of tokens can be swapped between adjacent vertices. The goal is to perform a sequence of swaps so that token and vertex colors agree across the graph. The minimum number of swaps is required in the optimization variant of the problem. We observed similarities between the TSWAP problem and multi-agent path finding (MAPF) where instead of tokens we have multiple agents that need to be moved from their current vertices to given unique target vertices. The difference between both problems consists in local conditions that state transitions (swaps/moves) must satisfy. We developed two algorithms for solving TSWAP optimally by adapting two different approaches to MAPF -CBS and MDD-SAT. This constitutes the first attempt to design optimal solving algorithms for TSWAP. Experimental evaluation on various types of graphs shows that the reduction to SAT scales better than CBS in optimal TSWAP solving.

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“…The solution obtained by this method represent a near optimal because of the randomly generation of the graph. Decomposing the map of the multi-robot path planning into subgraphs of particular known structure (cliques, halls, and rings) [21,22,23], which place constraints on which robots can enter or leave at a particular time. It made possible to plan hierarchically which can provide a significant improvement in planning time over a non-hierarchical planner.…”

Section: Previous Workmentioning

confidence: 99%

Probabilistic Multi Robot Path Planning in Dynamic Environments: A Comparison between A* and DFS

Shwail

Karim

Turner

2013

IJCA

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In this paper, a probabilistic roadmap planner algorithm with the multi robot path planning problem have been proposed by using the A* search algorithm in a dynamic environment. The whole process consists of two phases. In the first phase: Preprocessing phase, the work space is converted into the configuration space, constructing a probabilistic roadmap graph in the free space, and finding the optimal path for each robot using a global planner that avoids the collision with the static obstacles. The second phase: Moving phase, moves each robot in a prioritized manner from its starting point to its ending point through a near optimal path with avoiding collision with the moving obstacles and the other robots. A comparison has been done with the depth first algorithm to see the difference. The simulation results shows that choosing A* search algorithm affect positively the speed of the two phases together in comparison to the depth first search algorithm. General TermsArtificial intelligence, Robot path planning.

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Constraint-based multi-robot path planning

Cited by 45 publications

References 7 publications

Combining Deep Reinforcement Learning with Search Heuristics for Solving Multi-Agent Path Finding in Segment-based Layouts

Combining Deep Reinforcement Learning with Search Heuristics for Solving Multi-Agent Path Finding in Segment-based Layouts

Finding Optimal Solutions to Token Swapping by Conflict-Based Search and Reduction to SAT

Probabilistic Multi Robot Path Planning in Dynamic Environments: A Comparison between A* and DFS

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