Path and Action Planning in Non-uniform Environments for Multi-agent Pickup and Delivery Tasks

Yamauchi, Tomoki; Miyashita, Y.; Sugawara, Toshiharu

doi:10.1007/978-3-030-82254-5_3

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…We propose a novel deadlock avoidance method, i.e., SBDA, to improve the transportation efficiency while allowing agents to conduct tasks having the same endpoints even in a maze-like environment. We introduce the status management token (SMT), which is an extension of a token [16] and a synchronized block of information [35], to manage the status of the planning, agents, and standby nodes and to detect conflicts with other agents, where we define a conflict as a situation in which multiple agents occupy the same node 𝑣 ∈ 𝑉 or cross the same edge simultaneously. SMT contains the reservation table (RT), the task execution status table (TEST), and the standby-node status table (SST).…”

Section: Standby-based Deadlock Avoidance 41 Status Management Tokenmentioning

confidence: 99%

Standby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Tasks

Yamauchi¹,

Miyashita²,

Sugawara³

2022

Preprint

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The multi-agent pickup and delivery (MAPD) problem, in which multiple agents iteratively carry materials without collisions, has received significant attention. However, many conventional MAPD algorithms assume a specifically designed grid-like environment, such as an automated warehouse. Therefore, they have many pickup and delivery locations where agents can stay for a lengthy period, as well as plentiful detours to avoid collisions owing to the freedom of movement in a grid. By contrast, because a maze-like environment such as a search-and-rescue or construction site has fewer pickup/delivery locations and their numbers may be unbalanced, many agents concentrate on such locations resulting in inefficient operations, often becoming stuck or deadlocked. Thus, to improve the transportation efficiency even in a maze-like restricted environment, we propose a deadlock avoidance method, called standbybased deadlock avoidance (SBDA). SBDA uses standby nodes determined in real-time using the articulation-point-finding algorithm, and the agent is guaranteed to stay there for a finite amount of time. We demonstrated that our proposed method outperforms a conventional approach. We also analyzed how the parameters used for selecting standby nodes affect the performance.

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Section: Standby-based Deadlock Avoidance 41 Status Management Tokenmentioning

confidence: 99%

Standby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Tasks

Yamauchi¹,

Miyashita²,

Sugawara³

2022

Preprint

Self Cite

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“…However, the technique is limited to discrete settings, and only considers obstacle removal (in contrast to general re-configuration). The problem of multi-agent pick-up and delivery also explores the relationship between agents and environment [43]- [45]. However, it considers environment changes as pre-defined tasks and assigns agents to complete these tasks -constituting a different problem that is defined only in a discrete setting.…”

Section: Introductionmentioning

confidence: 99%

Environment Optimization for Multi-Agent Navigation

Gao

Prorok

2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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This work views the multi-agent system and its surrounding environment as a co-evolving system, where the behavior of one affects the other. The goal is to take both agent actions and environment configurations as decision variables, and optimize these two components in a coordinated manner to improve some measure of interest. Towards this end, we consider the problem of decentralized multi-agent navigation in cluttered environments. By introducing two sub-objectives of multi-agent navigation and environment optimization, we propose an agentenvironment co-optimization problem and develop a coordinated algorithm that alternates between these sub-objectives to search for an optimal synthesis of agent actions and obstacle configurations in the environment; ultimately, improving the navigation performance. Due to the challenge of explicitly modeling the relation between agents, environment and performance, we leverage policy gradient to formulate a model-free learning mechanism within the coordinated framework. A formal convergence analysis shows that our coordinated algorithm tracks the local minimum trajectory of an associated time-varying non-convex optimization problem. Extensive numerical results corroborate theoretical findings and show the benefits of co-optimization over baselines. Interestingly, the results also indicate that optimized environment configurations are able to offer structural guidance that is key to de-conflicting agents in motion.

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Task Selection Algorithm for Multi-Agent Pickup and Delivery with Time Synchronization

Yamauchi

Miyashita²,

Sugawara³

2022

PRIMA 2022: Principles and Practice of Multi-Agent Systems

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Path and Action Planning in Non-uniform Environments for Multi-agent Pickup and Delivery Tasks

Cited by 8 publications

References 21 publications

Standby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Tasks

Standby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Tasks

Environment Optimization for Multi-Agent Navigation

Task Selection Algorithm for Multi-Agent Pickup and Delivery with Time Synchronization

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