Quality-sensitive foraging by a robot swarm through virtual pheromone trails

FONT LLENAS, Anna, TALAMALI, Mohamed S., XU, Xu, MARSHALL, James A.R. and REINA, Andreagiovanni (2018). Quality-sensitive foraging by a robot swarm through virtual pheromone trails. In: Swarm intelligence : 11th International Conference, ANTS 2018, Rome, Italy, October 29–31, 2018, Proceedings. Lecture Notes in Computer Science (11172). Springer, 135-149.

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Official URL: https://link.springer.com/chapter/10.1007/978-3-03...
Link to published version:: https://doi.org/10.1007/978-3-030-00533-7_11

Abstract

Large swarms of simple autonomous robots can be employed to find objects clustered at random locations, and transport them to a central depot. This solution offers system parallelisation through concurrent environment exploration and object collection by several robots, but it also introduces the challenge of robot coordination. Inspired by ants’ foraging behaviour, we successfully tackle robot swarm coordination through indirect stigmergic communication in the form of virtual pheromone trails. We design and implement a robot swarm composed of up to 100 Kilobots using the recent technology Augmented Reality for Kilobots (ARK). Using pheromone trails, our memoryless robots rediscover object sources that have been located previously. The emerging collective dynamics show a throughput inversely proportional to the source distance. We assume environments with multiple sources, each providing objects of different qualities, and we investigate how the robot swarm balances the quality-distance trade-off by using quality-sensitive pheromone trails. To our knowledge this work represents the largest robotic experiment in stigmergic foraging, and is the first complete demonstration of ARK, showcasing the set of unique functionalities it provides.

Item Type: Book Section
Additional Information: “The final authenticated version is available online at https://doi.org/10.1007/978-3-030-00533-7_11.” ISSN: 0302-9743
Uncontrolled Keywords: 08 Information And Computing Sciences; Artificial Intelligence & Image Processing
Identification Number: https://doi.org/10.1007/978-3-030-00533-7_11
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 29 Nov 2018 14:21
Last Modified: 29 Apr 2019 09:21
URI: http://shura.shu.ac.uk/id/eprint/23375

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