A Decentralized Mobile Computing Network for Multi-Robot Systems Operations

TL;DR

This paper introduces a decentralized collective computing framework inspired by animal group behaviors, enabling multi-robot systems to perform complex tasks like mapping with robustness, scalability, and minimal intervention.

Contribution

It presents a novel decentralized collective computing framework for multi-robot systems, extending beyond basic tasks to complex cooperative mapping.

Findings

Framework exhibits robustness to robot loss

System is scalable to large robot groups

Network topology significantly affects performance

Abstract

Collective animal behaviors are paradigmatic examples of fully decentralized operations involving complex collective computations such as collective turns in flocks of birds or collective harvesting by ants. These systems offer a unique source of inspiration for the development of fault-tolerant and self-healing multi-robot systems capable of operating in dynamic environments. Specifically, swarm robotics emerged and is significantly growing on these premises. However, to date, most swarm robotics systems reported in the literature involve basic computational tasks---averages and other algebraic operations. In this paper, we introduce a novel Collective computing framework based on the swarming paradigm, which exhibits the key innate features of swarms: robustness, scalability and flexibility. Unlike Edge computing, the proposed Collective computing framework is truly decentralized and does not require user intervention or additional servers to sustain its operations. This Collective computing framework is applied to the complex task of collective mapping, in which multiple robots aim at cooperatively map a large area. Our results confirm the effectiveness of the cooperative strategy, its robustness to the loss of multiple units, as well as its scalability. Furthermore, the topology of the interconnecting network is found to greatly influence the performance of the collective action.