On the computational power of energy-constrained mobile robots

IF 0.8 4区计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS

Information and Computation Pub Date : 2025-02-07 DOI:10.1016/j.ic.2025.105280

Kevin Buchin , Paola Flocchini , Irina Kostitsyna , Tom Peters , Nicola Santoro , Koichi Wada

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引用次数: 0

Abstract

We consider distributed systems of autonomous robots operating in the plane under synchronous Look-Compute-Move (LCM) cycles. Prior research on four distinct models assumes robots have unlimited energy. We remove this assumption and investigate systems where robots have limited but renewable energy, requiring inactivity for energy restoration.

We analyze the computational impact of this constraint, fully characterizing the relationship between energy-restricted and unrestricted robots. Surprisingly, we show that energy constraints can enhance computational power.

Additionally, we study how memory persistence and communication capabilities influence computation under energy constraints. By comparing the four models in this setting, we establish a complete characterization of their computational relationships.

A key insight is that energy-limited robots can be modeled as unlimited-energy robots controlled by an adversarial activation scheduler. This provides a novel equivalence framework for analyzing energy-constrained distributed systems.

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来源期刊

Information and Computation 工程技术-计算机：理论方法

CiteScore

2.30

自引率

0.00%

发文量

119

审稿时长

140 days

期刊介绍： Information and Computation welcomes original papers in all areas of theoretical computer science and computational applications of information theory. Survey articles of exceptional quality will also be considered. Particularly welcome are papers contributing new results in active theoretical areas such as -Biological computation and computational biology- Computational complexity- Computer theorem-proving- Concurrency and distributed process theory- Cryptographic theory- Data base theory- Decision problems in logic- Design and analysis of algorithms- Discrete optimization and mathematical programming- Inductive inference and learning theory- Logic & constraint programming- Program verification & model checking- Probabilistic & Quantum computation- Semantics of programming languages- Symbolic computation, lambda calculus, and rewriting systems- Types and typechecking