Swarm Engineering Through Quantitative Measurement of Swarm Robotic Principles in a 10,000 Robot Swarm
This work addresses the problem of designing scalable swarm-robotic systems for researchers and engineers, though it is incremental as it builds on existing principles with new metrics.
The paper tackles the challenge of systematically comparing swarm robotic algorithms by proposing quantitative metrics for scalability, flexibility, and emergence, and demonstrates their use in solving a large object gathering problem with swarms of over 10,000 robots in simulation.
When designing swarm-robotic systems, systematic comparison of algorithms from different domains is necessary to determine which is capable of scaling up to handle the target problem size and target operating conditions. We propose a set of quantitative metrics for scalability, flexibility, and emergence which are capable of addressing these needs during the system design process. We demonstrate the applicability of our proposed metrics as a design tool by solving a large object gathering problem in temporally varying operating conditions using iterative hypothesis evaluation. We provide experimental results obtained in simulation for swarms of over 10,000 robots.