Abstract
We study the problem of optimally coordinating multiple fixed-wing UAVs to perform vision-based target tracking, which entails that the UAVs are tasked with gathering the best joint vision-based measurements of an unpredictable ground target. We utilize an analytic expression for the error covariance associated with the fused measurements of the target's position, and we employ stochastic fourth-order models for all vehicles, thereby incorporating a high degree of realism into the problem formulation. While dynamic programming can generate an optimal control policy that minimizes the expected value of the fused geolocation error covariance over time, it is accompanied by significant computational challenges due to the curse of dimensionality. In order to circumvent this challenge, we present a novel policy generation technique that combines simulation-based policy iteration with a robust regression scheme. The resulting control policy offers a significant advantage over alternative approaches and shows that the optimal control strategy involves coordinating the UAVs' distances to the target rather than their viewing angles, which had been a common practice in target tracking.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.