Analytical Approach to Multi-Objective Joint Inference Control for Fixed Wing Unmanned Aerial Vehicles
Abstract
Fixed-wing Unmanned Aerial Vehicles (UAVs) have been found highly useful in various environments, including military and law enforcement. With the increased use of fixed-wing UAVs, there becomes an increased need to optimize the resources available. One approach to resource management is to create multi-objective flights. This thesis presents the design, analysis, and experimental implementation of multi-objective resource management for the resource of Range, distance available to the UAV, from the viewpoint of Intelligence Surveillance and Reconnaissance (ISR). First, a Simulation Environment is created capable of tracking multiple fixed-wing UAVs and to allow for the UAVs being controlled by an externally driven algorithm. Second, an Inference algorithm is developed with the objective of information seeking. Several algorithms are developed and used in conjunction with a Sequential Analysis test to allow for calculating Target Value, calculating Target Confidence, and validating the calculated Target Value. Third, a Control algorithm is developed with the objective of Target seeking. The Control algorithm uses several approaches to path generation, including Dubins path, Optimized Order path, and Closest Target path. Finally, a supervisor algorithm termed Joint Inference and Control (JIC) joins Inference and Control together. Monte Carlo simulated test flight results are shown to illustrate the effectiveness of the developed algorithms.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 07, 2020
- Accession Number
- AD1116915
Entities
People
- Julian L. Casey
Organizations
- Wright State University