Allocation of UAV Search Efforts Using Dynamic Programming and Bayesian Updating
Abstract
As unmanned aerial vehicle (UAV) technology and availability improves, it becomes increasingly more important to operate UAVs efficiently. Utilizing one UAV at a time is a relatively simple task, but when multiple UAVs need to be coordinated, optimal search plans can be difficult to create in a timely manner. In this thesis, we create a decision aid that generates efficient routes for multiple UAVs using dynamic programming and a limited-look-ahead heuristic. The goal is to give the user the best knowledge of the locations of an arbitrary number of targets operating on a specified graph of nodes and arcs. The decision aid incorporates information about detections and nondetections and determines the probabilities of target locations using Bayesian updating. Target movement is modeled by a Markov process. The decision aid has been tested in two multi-hour field experiments involving actual UAVs and moving targets on the ground.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2008
- Accession Number
- ADA483499
Entities
People
- Christopher Nigus
- Kevin Mccadden
Organizations
- Naval Postgraduate School