Multi-agent Path Finding with Kinematic Constraints via Conflict Based Search

Andreychuk, Anton

doi:10.1007/978-3-030-59535-7_3

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…(3) Considering the robot dynamics to determine and use the exact execution times of actions during planning achieves the highest throughput when used in conjunction with windowing, rotation, and k-robustness. It is future work to incorporate higher-order dynamics models (Kou et al 2019;Andreychuk 2020) into our framework.…”

Section: Discussionmentioning

confidence: 99%

“…Unfortunately, they cannot be directly applied in the real world as they make many simplified and thus unrealistic assumptions on the robot dynamics and actuator uncertainty. Therefore, recent research has focused on more complicated MAPF models to close the gap, such as MAPF with non-unit travel times (Walker, Sturtevant, and Felner 2020;Walker et al 2021;Andreychuk et al 2021), kinematic constraints (Le and Plaku 2018;Cohen et al 2019;Andreychuk 2020;Solis et al 2021;Chen et al 2021a), and time uncertainty Atzmon et al 2020b,a;Shahar et al 2021). These MAPF models are more applica-ble in the real world, but they are also harder to solve.…”

Section: Introductionmentioning

confidence: 99%

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Which MAPF Model Works Best for Automated Warehousing?

Varambally

Koenig

2022

SOCS

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Multi-Agent Path Finding (MAPF) algorithms and their variants can find high-quality collision-free plans for automated warehousing under simplified assumptions about the robot dynamics. However, these simplifying assumptions pose challenging implementational issues as the robots cannot follow the plans precisely. Various robust execution frameworks, such as the Action Dependency Graph (ADG) framework, have been proposed to enable the real-world execution of MAPF plans. Under such a framework, it is unclear how the simplifying assumptions affect the performance of the robots. In this work, we first argue that the ADG framework provides the same robustness guarantees as the single-agent framework (where plans are generated independently for each robot and collisions are avoided through a reservation table), which is widely used in industry. We then improve the efficiency of the ADG framework by integrating it with the Rolling-Horizon Collision-Resolution framework to solve MAPF problems with a persistent stream of online tasks. Using the integrated framework, we compare the standard MAPF model with many of its more complex variants, such as MAPF with rotation, k-robust MAPF, and continuous-time MAPF (taking robot dynamics into account). We examine their effectiveness in improving throughput through realistic simulations of warehouse settings with the Gazebo simulator.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Which MAPF Model Works Best for Automated Warehousing?

Varambally

Koenig

2022

SOCS

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“…Many enhancements of CBS exist that significantly improve its performance while preserving the theoretical guarantees -ICBS [3], CBSH [6] etc. Other variants of CBS, such as the ones that take the kinematic constraints [1] into account, target bounded-suboptimal solutions [2] are also known. Another widespread approach to centralized multi-agent navigation is prioritized planning [20].…”

Section: Introductionmentioning

confidence: 99%

Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments

Davydov¹,

Skrynnik²,

Yakovlev³

et al. 2021

Preprint

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In this paper, we consider the problem of multi-agent navigation in partially observable grid environments. This problem is challenging for centralized planning approaches as they, typically, rely on the full knowledge of the environment. We suggest utilizing the reinforcement learning approach when the agents, first, learn the policies that map observations to actions and then follow these policies to reach their goals. To tackle the challenge associated with learning cooperative behavior, i.e. in many cases agents need to yield to each other to accomplish a mission, we use a mixing Q-network that complements learning individual policies. In the experimental evaluation, we show that such approach leads to plausible results and scales well to large number of agents.

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Prioritized SIPP for Multi-agent Path Finding with Kinematic Constraints

Ali

Yakovlev

2021

Lecture Notes in Computer Science

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Multi-agent Path Finding with Kinematic Constraints via Conflict Based Search

Cited by 4 publications

References 17 publications

Which MAPF Model Works Best for Automated Warehousing?

Which MAPF Model Works Best for Automated Warehousing?

Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments

Prioritized SIPP for Multi-agent Path Finding with Kinematic Constraints

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