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 successfully 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. Human operators as tasked with effectively planning and managing these complex missions. This work 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.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Planning and Monitoring for Swarm Search and Service Missions


    Contributors:

    Conference:

    AIAA SciTech Forum ; 2021 ; Nashville, TN, US


    Type of media :

    Conference paper


    Type of material :

    No indication


    Language :

    English








    UAV path planning for offshore swarm-based missions

    Platanitis, Konstantinos S. / Kladis, Georgios P. / Petrongonas, Evangelos et al. | IEEE | 2022