Operational Planning for Multiple Heterogeneous Unmanned Aerial Vehicles in Three Dimensions

Abstract

Unmanned aerial vehicles are being incorporated in an increasing variety of operations. To take full advantage of the vehicles, the plans for the operations should integrate each vehicle's capabilities when planning the operations. This thesis focuses on planning operations for multiple, heterogeneous UAVs for the purpose of monitoring Earth's phenomena through data collection. The planning is done for flight in three dimensions. The problem also includes time window constraints for data collection and incorporates human input in the planning process.Two solution methods are presented: (1) a mixed-integer program, and (2) an algorithm that utilizes a metaheuristic to generate composite variables for a linear program, called the Composite Operations Planning Algorithm. The suitability of the two methods to solve the operations planning problem is compared based on the ability of each of the methods to find high-value, feasible solutions for large-scale, operationally sized problems in a reasonable amount of time. The analysis shows that the Composite Operations Planning Algorithm can develop operations plans for problems including 15 UAVs and 5000 nodes in less than 25 minutes using a desktop computer.

Optimized Stochastic Coordinated Planning of Asynchronous Air and Space Assets

“…In this context, a request is defined as the collection specification, input into the CP by some user, for obtaining a piece of data (ground image, infrared image, climate data, etc.) at a given location, during a specific time window, using sensing assets (motivated by the definition of a request found in [10]). A planner is defined as a function that takes requests as inputs to produce an operation schedule for an asset or set of assets, such as UAVs, airplanes, satellites, ground vehicles, or underwater vehicles.…”