Intractability of Optimal Multirobot Path Planning on Planar Graphs

Yu, Jingjin

doi:10.1109/lra.2015.2503143

Cited by 140 publications

(142 citation statements)

References 19 publications

(21 reference statements)

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“…The last unassigned task 0 is assigned to the first agent which leads to a solution in the left leaf node of the final level. for j do 12 children ← children + (s.τ j + t) 13 return children E. Goal Test algorithm 2 shows the algorithm to do the goal test that is performed on any evaluated node. It requires the node to have no collisions and all tasks to be assigned.…”

Section: Expandmentioning

confidence: 99%

An Optimal Algorithm to Solve the Combined Task Allocation and Path Finding Problem

Henkel

Abbenseth

Toussaintl

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We consider multi-agent transport task problems where, e.g. in a factory setting, items have to be delivered from a given start to a goal pose while the delivering robots need to avoid collisions with each other on the floor.We introduce a Task Conflict-Based Search (TCBS) Algorithm to solve the combined delivery task allocation and multiagent path planning problem optimally. The problem is known to be NP-hard and the optimal solver cannot scale. However, we introduce it as a baseline to evaluate the sub-optimality of other approaches. We show experimental results that compare our solver with different sub-optimal ones in terms of regret.

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Section: Expandmentioning

confidence: 99%

An Optimal Algorithm to Solve the Combined Task Allocation and Path Finding Problem

Henkel

Abbenseth

Toussaintl

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…One can be interested to constrain the movement graph to a planar graph or a 2D grid given the common usage of grid modelling of the environment. Given the intractability of MAPP on planar graphs [Yu, 2016] and on general 2D grid graphs [Banfi et al, 2017], it is likely that this problem is intractable as well. Furthermore, in [Tateo et al, 2018], the decision is proved to stay PSPACE-complete on planar graphs and grids as well.…”

Section: Related Workmentioning

confidence: 99%

Reachability and Coverage Planning for Connected Agents

Charrier

Queffelec

Sankur

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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Motivated by the increasing appeal of robots in information-gathering missions, we study multiagent path planning problems in which the agents must remain interconnected. We model an area by a topological graph specifying the movement and the connectivity constraints of the agents. We study the theoretical complexity of the reachability and the coverage problems of a fleet of connected agents on various classes of topological graphs. We establish the complexity of these problems on known classes, and introduce a new class called sight-moveable graphs which admit efficient algorithms. PreliminariesWe first present the topological graphs and the subclasses we consider on which we study the complexity of our problems.

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“…Labeled optimal multi-robot path planning (MPP) problems, despite their high associated computational complexity [1], have been actively studied for decades due to the problems' extensive applications. The general task is to efficiently plan high-quality, collision-free paths to route a set of robots from an initial configuration to a goal configuration.…”

Section: Introductionmentioning

confidence: 99%

DDM: Fast Near-Optimal Multi-Robot Path Planning Using Diversified-Path and Optimal Sub-Problem Solution Database Heuristics

Han

2020

IEEE Robot. Autom. Lett.

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We propose a novel centralized and decoupled algorithm, DDM, for solving multi-robot path planning problems in grid graphs, targeting on-demand and automated warehouselike settings. Two settings are studied: a traditional one whose objective is to move a set of robots from their respective initial vertices to the goal vertices as quickly as possible, and a dynamic one which requires frequent re-planning to accommodate for goal configuration adjustments. Among other techniques, DDM is mainly enabled through exploiting two innovative heuristics: path diversification and optimal sub-problem solution databases. The two heuristics attack two distinct phases of a decoupling-based planner: while path diversification allows the more effective use of the entire workspace for robot travel, optimal sub-problem solution databases facilitate the fast resolution of local path conflicts. Extensive evaluation demonstrates that DDM achieves high levels of scalability and high levels of solution optimality.

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Intractability of Optimal Multirobot Path Planning on Planar Graphs

Cited by 140 publications

References 19 publications

An Optimal Algorithm to Solve the Combined Task Allocation and Path Finding Problem

An Optimal Algorithm to Solve the Combined Task Allocation and Path Finding Problem

Reachability and Coverage Planning for Connected Agents

DDM: Fast Near-Optimal Multi-Robot Path Planning Using Diversified-Path and Optimal Sub-Problem Solution Database Heuristics

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