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.
|Original language||English (US)|
|Title of host publication||Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019|
|Publisher||International Joint Conferences on Artificial Intelligence|
|Number of pages||7|
|State||Published - 2019|
|Event||28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China|
Duration: Aug 10 2019 → Aug 16 2019
|Name||IJCAI International Joint Conference on Artificial Intelligence|
|Conference||28th International Joint Conference on Artificial Intelligence, IJCAI 2019|
|Period||8/10/19 → 8/16/19|
Bibliographical noteFunding Information:
We gratefully acknowledge Amazon Robotics, the Mn-DRIVE RSAM initiative at the University of Minnesota, the Minnesota Supercomputing Institute for their support of this work, as well as London Lowmanstone's work on developing the initial experimental framework.