# Simple Swarm Foraging Algorithm Based on Gradient Computation

## TL;DR

This paper introduces a simple swarm foraging algorithm where robots use gradient-based signals for attraction and repulsion, improving search efficiency in unknown environments, validated through simulation and hardware experiments.

## Contribution

The paper presents a novel gradient-based communication method for swarm robots that enhances foraging performance compared to other signal configurations.

## Key findings

- Using both attraction and repulsion signals yields the best performance.
- The algorithm performs well in simulation and is feasible for real robots.
- Communication signals significantly influence swarm foraging efficiency.

## Abstract

Swarm foraging is a common test case application for multi-robot systems. In this paper we present a novel algorithm for controlling swarm robots with limited communication range and storage capacity to efficiently search for and retrieve targets within an unknown environment. In our approach, robots search using random walk and adjust their turn probability based on attraction and repulsion signals they sense from other robots. We compared our algorithm with five different variations reflecting absence or presence of attractive and/or repulsive communication signals. Our results show that best performance is achieved when both signals are used by robots for communication. Furthermore, we show through hardware experiments how the communication model we used in the simulation could be realized on real robots.