A Comparative Study of Game-theoretical and Markov-chain-based Approaches to Division of Labour in a Robotic Swarm
| Autor: | Inmo Jang, Hyo-Sang Shin, Antonios Tsourdos |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Markov chain Computer science business.industry Swarm behaviour 02 engineering and technology Swarm intelligence Task (computing) 020901 industrial engineering & automation Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | IFAC-PapersOnLine. 51:62-68 |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2018.07.089 |
| Popis: | This paper compares two swarm intelligence frameworks that we previously proposed for multi-robot task allocation problems: the game-theoretical approach based on anonymous hedonic games, called GRAPE, and the Markov-Chain-based approach under local information consistency assumption, called LICA-MC. We implement both frameworks into swarm distribution guidance problem, the objective of which is to distribute a swarm of robots into a set of tasks in proportion to the tasks’ demands, and then we perform extensive numerical experiments with various environmental settings. The statistical results show that LICA-MC provides excellent scalability regardless of the number of robots, whereas GRAPE is more efficient in terms of convergence time (especially when accommodating a moderate number of robots) as well as total travelling costs. Furthermore, this study investigates other implicit advantages of the frameworks such as mission suitability, additionally-built-in decision-making functions, and sensitivity to traffic congestion or robots’ mobility. |
| Databáze: | OpenAIRE |
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