A Self-Guided Approach for Navigation in a Minimalistic Foraging Robotic Swarm

TL;DR

This paper introduces a biologically inspired, decentralized swarm foraging method that requires minimal capabilities, no infrastructure, and scalable communication, demonstrated through experiments with Elisa-3 robots.

Contribution

The paper proposes a novel self-guided foraging approach for minimalistic robotic swarms inspired by ant pheromone behavior, with decentralized control and one-hop communication.

Findings

Swarm self-organizes to find shortest paths in unknown environments.

The system operates without global positioning or infrastructure.

Experimental validation on Elisa-3 robots demonstrates effectiveness.

Abstract

We present a biologically inspired design for swarm foraging based on ant's pheromone deployment, where the swarm is assumed to have very restricted capabilities. The robots do not require global or relative position measurements and the swarm is fully decentralized and needs no infrastructure in place. Additionally, the system only requires one-hop communication over the robot network, we do not make any assumptions about the connectivity of the communication graph and the transmission of information and computation is scalable versus the number of agents. This is done by letting the agents in the swarm act as foragers or as guiding agents (beacons). We present experimental results computed for a swarm of Elisa-3 robots on a simulator, and show how the swarm self-organizes to solve a foraging problem over an unknown environment, converging to trajectories around the shortest path. At last, we discuss the limitations of such a system and propose how the foraging efficiency can be increased.