Offline Time-Independent Multiagent Path Planning

Okumura, Keisuke; Bonnet, François; Tamura, Yasumasa; Défago, Xavier

doi:10.1109/tro.2023.3258690

Cited by 5 publications

(1 citation statement)

References 48 publications

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“…The Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24) to unnecessary waits. One way to reduce such unnecessary waits is to find MAPF plans that explicitly minimize coordination, such as SL-CBS (Wagner, Veerapaneni, and Likhachev 2022) and DBS (Okumura et al 2023). However, these models usually impose overly strict coordination constraints, making them more challenging to solve than regular MAPF and resulting in longer execution times.…”

Section: Related Workmentioning

confidence: 99%

Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution

Su,

Veerapaneni,

2024

AAAI

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The Multi-Agent Path Finding (MAPF) problem involves planning collision-free paths for multiple agents in a shared environment. The majority of MAPF solvers rely on the assumption that an agent can arrive at a specific location at a specific timestep. However, real-world execution uncertainties can cause agents to deviate from this assumption, leading to collisions and deadlocks. Prior research solves this problem by having agents follow a Temporal Plan Graph (TPG), enforcing a consistent passing order at every location as defined in the MAPF plan. However, we show that TPGs are overly strict because, in some circumstances, satisfying the passing order requires agents to wait unnecessarily, leading to longer execution time. To overcome this issue, we introduce a new graphical representation called a Bidirectional Temporal Plan Graph (BTPG), which allows switching passing orders during execution to avoid unnecessary waiting time. We design two anytime algorithms for constructing a BTPG: BTPG-naïve and BTPG-optimized. Experimental results show that following BTPGs consistently outperforms following TPGs, reducing unnecessary waits by 8-20%.

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Section: Related Workmentioning

confidence: 99%

Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution

Su,

Veerapaneni,

2024

AAAI

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Learning team-based navigation: a review of deep reinforcement learning techniques for multi-agent pathfinding

Chung,

Fayyad,

Younes

et al. 2024

Artif Intell Rev

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Multi-agent pathfinding (MAPF) is a critical field in many large-scale robotic applications, often being the fundamental step in multi-agent systems. The increasing complexity of MAPF in complex and crowded environments, however, critically diminishes the effectiveness of existing solutions. In contrast to other studies that have either presented a general overview of the recent advancements in MAPF or extensively reviewed Deep Reinforcement Learning (DRL) within multi-agent system settings independently, our work presented in this review paper focuses on highlighting the integration of DRL-based approaches in MAPF. Moreover, we aim to bridge the current gap in evaluating MAPF solutions by addressing the lack of unified evaluation indicators and providing comprehensive clarification on these indicators. Finally, our paper discusses the potential of model-based DRL as a promising future direction and provides its required foundational understanding to address current challenges in MAPF. Our objective is to assist readers in gaining insight into the current research direction, providing unified indicators for comparing different MAPF algorithms and expanding their knowledge of model-based DRL to address the existing challenges in MAPF.

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Solving simultaneous target assignment and path planning efficiently with time-independent execution

Okumura

Défago

2023

Artificial Intelligence

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Offline Time-Independent Multiagent Path Planning

Cited by 5 publications

References 48 publications

Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution

Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution

Learning team-based navigation: a review of deep reinforcement learning techniques for multi-agent pathfinding

Solving simultaneous target assignment and path planning efficiently with time-independent execution

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