A supervisory mission in which a team of unmanned vehicles visits a set of targets and collects sensory data to be analyzed in real time by a remotely located human operator is considered. A framework is proposed to simultaneously construct the operator’s task-processing schedule and each vehicle’s target visitation route, with the dual goal of moderating the operator’s task load and preventing unnecessary vehicle loitering. The joint scheduling/routing problem is posed as a mixed-integer (nonlinear) program that can be equivalently represented as a mixed-integer linear program through expansion of the solution space. In single-vehicle missions, it is shown that an alternative linearization that does not increase the problem size exists. Next, a dynamic solution strategy that incrementally constructs suboptimal schedules and routes by solving a comparatively small, mixed-integer linear program whenever the operator finishes a task is introduced. Using a scenario-based extension, this dynamic framework is then modified to provide robustness to uncertainty in operator processing times. The flexibility and utility of these algorithms are explored in simulated missions.
Robust Scheduling and Routing for Collaborative Human/Unmanned Aerial Vehicle Surveillance Missions
Journal of Aerospace Information Systems ; 15 , 10 ; 585-603
2018-08-31
19 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Multi-Unmanned Aerial Vehicle Coverage Planner for Area Surveillance Missions AIAA Paper
British Library Conference Proceedings | 2007
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