Efficiently reconfiguring a connected swarm of labeled robots

IF 2.6 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Autonomous Agents and Multi-Agent Systems Pub Date : 2024-08-07 DOI:10.1007/s10458-024-09668-3
Sándor P. Fekete, Peter Kramer, Christian Rieck, Christian Scheffer, Arne Schmidt
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Abstract

When considering motion planning for a swarm of n labeled robots, we need to rearrange a given start configuration into a desired target configuration via a sequence of parallel, collision-free moves. The objective is to reach the new configuration in a minimum amount of time. Problems of this type have been considered before, with recent notable results achieving constant stretch for parallel reconfiguration: If mapping the start configuration to the target configuration requires a maximum Manhattan distance of d, the total duration of an overall schedule can be bounded to \(\mathcal {O}(d)\), which is optimal up to constant factors. An important constraint for coordinated reconfiguration is to keep the swarm connected after each time step. In previous work, constant stretch could only be achieved if disconnected reconfiguration is allowed, or for scaled configurations of unlabeled robots; on the other hand, the existence of non-constant lower bounds on the stretch factor was unknown. We resolve these major open problems by (1) establishing a lower bound of \(\Omega (\sqrt{n})\) for connected, labeled reconfiguration and, most importantly, by (2) proving that for scaled arrangements, constant stretch for connected, labeled reconfiguration can be achieved. In addition, we show that (3) it is NP-complete to decide whether a makespan of 2 can be achieved, while it is possible to check in polynomial time whether a schedule of makespan 1 exists.

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高效重新配置连接的贴标机器人群
在考虑由 n 个贴标机器人组成的机器人群的运动规划时,我们需要通过一连串平行、无碰撞的移动,将给定的起始配置重新排列为所需的目标配置。目标是在最短时间内到达新配置。此类问题以前也曾被考虑过,最近的显著成果是实现了并行重新配置的恒定拉伸:如果将起始配置映射到目标配置需要的最大曼哈顿距离为 d,那么整体计划的总持续时间就可以被限定为 \(\mathcal {O}(d)\) ,这在常数因子范围内是最优的。协调重新配置的一个重要约束条件是在每个时间步之后保持蜂群的连接。在以前的工作中,只有在允许断开重新配置或无标记机器人按比例配置的情况下才能实现恒定拉伸;另一方面,拉伸因子的非恒定下限的存在也是未知的。我们通过以下方法解决了这些主要的未决问题:(1)为连接的、有标签的重新配置建立了一个 \Omega (\sqrt{n})\ 的下限;最重要的是(2)证明了对于按比例排列,连接的、有标签的重新配置可以实现恒定的拉伸。此外,我们还证明了(3)决定是否能实现跨度为 2 是 NP-complete,而检查跨度为 1 的时间表是否存在则只需多项式时间。
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来源期刊
Autonomous Agents and Multi-Agent Systems
Autonomous Agents and Multi-Agent Systems 工程技术-计算机:人工智能
CiteScore
6.00
自引率
5.30%
发文量
48
审稿时长
>12 weeks
期刊介绍: This is the official journal of the International Foundation for Autonomous Agents and Multi-Agent Systems. It provides a leading forum for disseminating significant original research results in the foundations, theory, development, analysis, and applications of autonomous agents and multi-agent systems. Coverage in Autonomous Agents and Multi-Agent Systems includes, but is not limited to: Agent decision-making architectures and their evaluation, including: cognitive models; knowledge representation; logics for agency; ontological reasoning; planning (single and multi-agent); reasoning (single and multi-agent) Cooperation and teamwork, including: distributed problem solving; human-robot/agent interaction; multi-user/multi-virtual-agent interaction; coalition formation; coordination Agent communication languages, including: their semantics, pragmatics, and implementation; agent communication protocols and conversations; agent commitments; speech act theory Ontologies for agent systems, agents and the semantic web, agents and semantic web services, Grid-based systems, and service-oriented computing Agent societies and societal issues, including: artificial social systems; environments, organizations and institutions; ethical and legal issues; privacy, safety and security; trust, reliability and reputation Agent-based system development, including: agent development techniques, tools and environments; agent programming languages; agent specification or validation languages Agent-based simulation, including: emergent behavior; participatory simulation; simulation techniques, tools and environments; social simulation Agreement technologies, including: argumentation; collective decision making; judgment aggregation and belief merging; negotiation; norms Economic paradigms, including: auction and mechanism design; bargaining and negotiation; economically-motivated agents; game theory (cooperative and non-cooperative); social choice and voting Learning agents, including: computational architectures for learning agents; evolution, adaptation; multi-agent learning. Robotic agents, including: integrated perception, cognition, and action; cognitive robotics; robot planning (including action and motion planning); multi-robot systems. Virtual agents, including: agents in games and virtual environments; companion and coaching agents; modeling personality, emotions; multimodal interaction; verbal and non-verbal expressiveness Significant, novel applications of agent technology Comprehensive reviews and authoritative tutorials of research and practice in agent systems Comprehensive and authoritative reviews of books dealing with agents and multi-agent systems.
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