ROAIMANEFeb 27, 2022

PheroCom: Decentralised and asynchronous swarm robotics coordination based on virtual pheromone and vibroacoustic communication

arXiv:2202.13456v14 citations
Originality Incremental advance
AI Analysis

This addresses coordination problems in swarm robotics for applications like surveillance, though it appears incremental as it builds on bio-inspired approaches with new modeling.

The paper tackles the challenge of representing and controlling stigmergic substances in swarm robotics by proposing a decentralized, asynchronous coordination model using virtual pheromone maps and vibroacoustic communication, with experiments showing the model successfully coordinated robots for surveillance tasks.

Representation and control of the dynamics of stigmergic substances used by bio-inspired approaches is a challenge when applied to robotics. In order to overcome this challenge, this work proposes a model to coordinate swarms of robots based on the virtualisation and control of these substances in a local scope. The model presents a new pheromone modelling, which enables the decentralisation and asynchronicity of navigation decisions. Each robot maintains an independent virtual pheromone map, which is continuously updated with the robot's deposits and pheromone evaporation. Moreover, the individual pheromone map is also updated by aggregating information from other robots that are exploring nearby areas. Thus, individual and independent maps replace the need of a centralising agent that controls and distributes the pheromone information, which is not always practicable. Pheromone information propagation is inspired by ants' vibroacoustic communication, which, in turn, is characterised as an indirect communication through a type of gossip protocol. The proposed model was evaluated through an agent simulation software, implemented by the authors, and in the Webots platform. Experiments were carried out to validate the model in different environments, with different shapes and sizes, as well as varying the number of robots. The analysis of the results has shown that the model was able to perform the coordination of the swarm, and the robots have exhibited an expressive performance executing the surveillance task.

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