Swarm Size Planning Tool for Multi-Job Type Missions

[摘要] As part of swarm search and service (SSS) missions, swarms are tasked with searching an area while simultaneously servicing jobs as they are encountered. Jobs must be immediately serviced and can be one of multiple types. Each type requires that vehicle(s) break off from the swarm and travel to the job site for a specified amount of time. The number of vehicles needed and the service time for each job type are known. Once a job has been successfully serviced, vehicles return to the swarm and are available for reallocation. When planning SSS missions, human operators are tasked with determining the required number of vehicles needed to handle the expected job demand. The complex relationship between job type parameters makes this choice challenging. This work presents a prediction model used to estimate the swarm size necessary to achieve a given performance. User studies were conducted to determine the usefulness and ease of use of such a prediction model as an aid during mission planning. Results show that using the planning tool leads to 7 times less missed area and a 50 percent cost reduction.

[发布日期] 2018-06-25

[学科分类] 人工智能

[关键词] PILOTLESS AIRCRAFT (SWARMS);AMOUNT;SIZE DISTRIBUTION;TASK PLANNING (ROBOTICS);MISSION PLANNING;PERFORMANCE PREDICTION;COST REDUCTION;QUEUEING THEORY;MATHEMATICAL MODELS