Collective Decision-Making on Task Allocation Feasibility

TL;DR

This paper introduces a distributed approach for robot swarms to collectively assess task feasibility using local observations and a majority-based decision strategy, enabling effective resource utilization in diverse environments.

Contribution

It presents the concept of distributed feasibility and applies a novel decision-making method to enable swarms to evaluate task suitability collectively.

Findings

Swarm can determine high or low feasibility based on local info.

Decision accuracy depends on robot-to-task ratio.

Method works well in homogeneous settings.

Abstract

Robot swarms offer the potential to bring several advantages to the real-world applications but deploying them presents challenges in ensuring feasibility across diverse environments. Assessing the feasibility of new tasks for swarms is crucial to ensure the effective utilisation of resources, as well as to provide awareness of the suitability of a swarm solution for a particular task. In this paper, we introduce the concept of distributed feasibility, where the swarm collectively assesses the feasibility of task allocation based on local observations and interactions. We apply Direct Modulation of Majority-based Decisions as our collective decision-making strategy and show that, in a homogeneous setting, the swarm is able to collectively decide whether a given setup has a high or low feasibility as long as the robot-to-task ratio is not near one.