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Planning and Monitoring Multi-Job Type Swarm Search and Service Missions

Chandarana, Meghan and Hughes, Dana and Lewis, Michael and Sycara, Katia and Scherer, Sebastian (2021) Planning and Monitoring Multi-Job Type Swarm Search and Service Missions. Journal of Intelligent & Robotic Systems, 101 (3). ISSN 1573-0409

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To transition from control theory to real applications, it is important to study missions such as Swarm Search and Service (SSS) where vehicles are not only required to search an area, but also service all jobs that they find. In SSS missions, each type of job requires a group of vehicles to break off from the swarm for a given amount of time to service it. The required number of vehicles and the service rate are unique to each job type. Once a job has been completed, the vehicles are able to return to the swarm for use elsewhere. If not enough vehicles are present in the swarm at the time that the job is identified, that job is dropped without being serviced. In SSS missions that occur in open environments, the arrival rate of jobs varies dynamically as vehicles move in and out of the swarm to service jobs. Human operators are tasked with effectively planning and managing these complex missions. This paper presents a user study that seeks to test the efficacy and ease-of-use of a prediction model known as the Hybrid Model as an aid in planning and monitoring tasks. Results show that the novel computational model aid allows operators to more effectively choose the necessary swarm size to handle expected mission workload, as well as, maintain sufficient situation awareness to evaluate the performance of the swarm during missions.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Chandarana, Meghan
Hughes, Dana
Lewis, Michaelcmlewis@pitt.educmlewis0000-0002-1013-9482
Sycara, Katia
Scherer, Sebastian
Date: March 2021
Date Type: Publication
Journal or Publication Title: Journal of Intelligent & Robotic Systems
Volume: 101
Number: 3
Publisher: Springer
DOI or Unique Handle: 10.1007/s10846-020-01272-3
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
ISSN: 1573-0409
Article Type: Research Article
Date Deposited: 13 Aug 2021 18:52
Last Modified: 13 Aug 2021 18:52


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