Robust Planning for Effects-Based Operations
Abstract
In this thesis, the author introduces and analyzes methods of creating theater-level robust mission plans for Effects-Based Operations (EBO) of teams of Unmanned Aerial Vehicles (UAVs), and propose methods of effectively presenting the robust plan to an end user. Recent conflicts have demonstrated the utility of UAVs in performing Intelligence, Surveillance, and Reconnaissance (ISR) and strike missions. As UAVs become more common, high-level pre-planning and task delegation will increase in complexity, requiring computer-aided planning. Traditional planning methods, based on deterministic input data, generate plans that become infeasible in uncertain environments. Because military operations tend to contain substantial amounts of uncertainty and re-planning at a theater level is costly, plans should be robust to uncertainty yet still accomplish the desired effects. The author presents an effects-based planning framework in which planners connect end effects to tasks, enabling them to value task assignments based on their ability to achieve desired effects. He applies two robust planning techniques to this framework (Bertsimas/Sim and Chance-Constrained Programming) and analyzes their performance. Through this he demonstrates how robust planning increases the length of time that a plan remains feasible in execution and achieves better overall value by avoiding re-planning costs. He analyzes the strengths and weaknesses of each model and suggests when their use is appropriate. Finally, he applies Human Machine Collaborative Decision Making (HMCDM) concepts to facilitate human interaction with a robust effects-based planner.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2006
- Accession Number
- ADA452230
Entities
People
- Corban H. Bryant