Multi-agent pathfinding with continuous time

Andreychuk, Anton; Yakovlev, Konstantin; Surynek, Pavel; Atzmon, Dor; Stern, Roni

doi:10.1016/j.artint.2022.103662

Cited by 62 publications

(70 citation statements)

References 44 publications

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“…CBS has been improved and extended in many ways [2], [3], [4], [10]. Among those variants, continuous-time CBS (CCBS) [1], which uses SIPP [13] as the low level planner, is applicable to MAPF-AA.…”

Section: Prior Workmentioning

confidence: 99%

“…Step (1) The minimum timestamp t min (s k ) and the second minimum timestamps among all agents within s k are computed:…”

Section: Neighbor Generationmentioning

confidence: 99%

“…In this work, we also consider extending MA-CBS for MAPF-AA by combining the proposed LSS approaches, such as LS-rM*, with the continuous-time CBS (CCBS) algorithm [1], which extends CBS and handles MAPF-AA by using SIPP [13] algorithm as the low level planner in CBS. With B ∈ [0, ∞], MA-CBS algorithm varies between LS-rM* and CCBS.…”

Section: E Extension With Ma-cbsmentioning

confidence: 99%

“…The inflated heuristics improves the computational efficiency further by trading off bounded sub-optimality. • The extension of MA-CBS by leveraging LS-rM* improves the success rates and run time, on average, compared to the existing CBS-based algorithm [1], which is, to our limited knowledge, the state-of-the-art search-based planner that can solve MAPF-AA. Finally, to verify the applicability of our approach to real robots, we have also executed the paths computed by our planner (LS-rM*) in the Robotarium [23], a remotely accessible swarm robotics research platform.…”

Section: Introductionmentioning

confidence: 95%

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Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions

Ren,

Rathinam,

Choset

2021

Preprint

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Multi-agent path finding (MAPF) determines an ensemble of collision-free paths for multiple agents between their respective start and goal locations. Among the available MAPF planners for workspaces modeled as a graph, A*based approaches have been widely investigated and have demonstrated their efficiency in numerous scenarios. However, almost all of these A*-based approaches assume that each agent executes an action concurrently in that all agents start and stop together. This article presents a natural generalization of MAPF with asynchronous actions (MAPF-AA) where agents do not necessarily start and stop concurrently. The main contribution of the work is a proposed approach called Loosely Synchronized Search (LSS) that extends A*-based MAPF planners to handle asynchronous actions. We show LSS is complete and finds an optimal solution if one exists. We also combine LSS with other existing MAPF methods that aims to tradeoff optimality for computational efficiency. Extensive numerical results are presented to corroborate the performance of the proposed approaches. Finally, we also verify the applicability of our method in the Robotarium, a remotely accessible swarm robotics research platform.

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Section: Prior Workmentioning

confidence: 99%

“…Step (1) The minimum timestamp t min (s k ) and the second minimum timestamps among all agents within s k are computed:…”

Section: Neighbor Generationmentioning

confidence: 99%

Section: E Extension With Ma-cbsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

See 2 more Smart Citations

Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions

Ren,

Rathinam,

Choset

2021

Preprint

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“…Finding this set of solutions while ensuring collision-free paths for agents in each solution is quite challenging: even though there are many multi-objective single-agent search algorithms [14], [19], [24] that can compute all Pareto-optimal solutions, a naive application of such algorithms to the MOMAPF problem may prove to be inefficient as the size of the Paretooptimal set grows exponentially with respect to the number of agents and the dimension of the search space [20], [31]. 1 Zhongqiang Ren and Howie Choset are with Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Conflict-based Search for Multi-agent Path Finding

Ren

Rathinam

Choset

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Conventional multi-agent path planners typically compute an ensemble of paths while optimizing a single objective, such as path length. However, many applications may require multiple objectives, say fuel consumption and completion time, to be simultaneously optimized during planning and these criteria may not be readily compared and sometimes lie in competition with each other. Naively applying existing multi-objective search algorithms to multi-agent path finding may prove to be inefficient as the size of the space of possible solutions, i.e., the Pareto-optimal set, can grow exponentially with the number of agents (the dimension of the search space). This article presents an approach named Multi-objective Conflict-based Search (MO-CBS) that bypasses this so-called curse of dimensionality by leveraging prior Conflict-based Search (CBS), a well-known algorithm for singleobjective multi-agent path finding, and principles of dominance from multi-objective optimization literature. We prove that MO-CBS is able to compute the entire Pareto-optimal set. Our results show that MO-CBS can solve problem instances with hundreds of Pareto-optimal solutions which the standard multiobjective A* algorithms could not find within a bounded time.

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A Combination of Theta*, ORCA and Push and Rotate for Multi-agent Navigation

Dergachev

Yakovlev

Prakapovich

2020

Lecture Notes in Computer Science

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We study the problem of multi-agent navigation in static environments when no centralized controller is present. Each agent is controlled individually and relies on three algorithmic components to achieve its goal while avoiding collisions with the other agents and the obstacles: i) individual path planning which is done by Theta* algorithm; ii) collision avoidance while path following which is performed by ORCA* algorithm; iii) locally-confined multi-agent path planning done by Push and Rotate algorithm. The latter component is crucial to avoid deadlocks in confined areas, such as narrow passages or doors. We describe how the suggested components interact and form a coherent navigation pipeline. We carry out an extensive empirical evaluation of this pipeline in simulation. The obtained results clearly demonstrate that the number of occurring deadlocks significantly decreases enabling more agents to reach their goals compared to techniques that rely on collision-avoidance only and do not include multi-agent path planning component .

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Multi-agent pathfinding with continuous time

Cited by 62 publications

References 44 publications

Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions

Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions

Multi-objective Conflict-based Search for Multi-agent Path Finding

A Combination of Theta*, ORCA and Push and Rotate for Multi-agent Navigation

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