Home/Determining Effective Swarm Sizes for Multi-Job Type Missions

Determining Effective Swarm Sizes for Multi-Job Type Missions

Meghan Chandarana, Michael Lewis, Katia Sycara and Sebastian Scherer
Conference Paper, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018, September, 2018

Download Publication

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


Swarm search and service (SSS) missions require large swarms to simultaneously search an area while servicing jobs as they are encountered. Jobs must be immediately serviced and can be one of several different job types – each requiring a different service time and number of vehicles to complete its service successfully. After jobs are serviced, vehicles are returned to the swarm and become available for reallocation. As part of SSS mission planning, human operators must determine the number of vehicles needed to achieve this balance. The complexities associated with balancing vehicle allocation to multiple as yet unknown tasks with returning vehicles makes this extremely difficult for humans. Previous work assumes that all system jobs are known ahead of time or that vehicles move independently of each other in a multi-agent framework. We present a dynamic vehicle routing (DVR) framework whose policies optimally allocate vehicles as jobs arrive. By incorporating time constraints into the DVR framework, an M/M/k/k queuing model can be used to evaluate overall steady state system performance for a given swarm size. Using these estimates, operators can rapidly compare system performance across different configurations, leading to more effective choices for swarm size. A sensitivity analysis is performed and its results are compared with the model, illustrating the appropriateness of our method to problems of plausible scale and complexity.

author = {Meghan Chandarana and Michael Lewis and Katia Sycara and Sebastian Scherer},
title = {Determining Effective Swarm Sizes for Multi-Job Type Missions},
booktitle = {Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018},
year = {2018},
month = {September},
} 2018-09-19T14:55:16-04:00