SubCDM: Collective Decision-Making with a Swarm Subset

TL;DR

SubCDM introduces a resource-efficient method for swarm robots to make collective decisions using only a dynamically determined subset of robots, maintaining accuracy while reducing resource consumption.

Contribution

It presents a decentralized, adaptive subset selection approach for collective decision-making in robot swarms, improving efficiency over existing methods.

Findings

Achieves decision accuracy comparable to full swarm with fewer robots.

Reduces resource usage in collective decision-making processes.

Demonstrates effectiveness in simulation with 100 robots.

Abstract

Collective decision-making is a key function of autonomous robot swarms, enabling them to reach a consensus on actions based on environmental features. Existing strategies require the participation of all robots in the decision-making process, which is resource-intensive and prevents the swarm from allocating the robots to any other tasks. We propose Subset-Based Collective Decision-Making (SubCDM), which enables decisions using only a swarm subset. The construction of the subset is dynamic and decentralized, relying solely on local information. Our method allows the swarm to adaptively determine the size of the subset for accurate decision-making, depending on the difficulty of reaching a consensus. Simulation results using one hundred robots show that our approach achieves accuracy comparable to using the entire swarm while reducing the number of robots required to perform collective decision-making, making it a resource-efficient solution for collective decision-making in swarm robotics.