ArticleFrontiers in robotics and AI2018
Information Exchange Design Patterns for Robot Swarm Foraging and Their Application in Robot Control Algorithms.
Article in Frontiers in robotics and AI, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review.Frontiers in robotics and AI · 2021Review
- A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints.Frontiers in robotics and AI · 2020Review
- Toward Formal Models and Languages for Verifiable Multi-Robot Systems.Frontiers in robotics and AI · 2018Review
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3 authors.
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Abstract
In swarm robotics, a design pattern provides high-level guidelines for the implementation of a particular robot behaviour and describes its impact on swarm performance. In this paper, we explore information exchange design patterns for robot swarm foraging. First, a method for the specification of design patterns for robot swarms is proposed that builds on previous work in this field and emphasises modular behaviour design, as well as information-centric micro-macro link analysis. Next, design pattern application rules that can facilitate the pattern usage in robot control algorithms are given. A catalogue of six design patterns is then presented. The patterns are derived from an extensive list of experiments reported in the swarm robotics literature, demonstrating the capability of the proposed method to identify distinguishing features of robot behaviour and their impact on swarm performance in a wide range of swarm implementations and experimental scenarios. Each pattern features a detailed description of robot behaviour and its associated parameters, facilitated by the usage of a multi-agent modeling language, BDRML, and an account of feedback loops and forces that affect the pattern's applicability. Scenarios in which the pattern has been used are described. The consequences of each design pattern on overall swarm performance are characterised within the Information-Cost-Reward framework, that makes it possible to formally relate the way in which robots acquire, share and utilise information. Finally, the patterns are validated by demonstrating how they improved the performance of foraging e-puck swarms and how they could guide algorithm design in other scenarios.
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