Journal of Intelligent & Robotic Systems

, Volume 82, Issue 1, pp 135–162 | Cite as

Stochastic Optimal Coordination of Small UAVs for Target Tracking using Regression-based Dynamic Programming

  • Steven A. P. Quintero
  • Michael Ludkovski
  • João P. Hespanha
Article

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.

Keywords

Target tracking Unmanned aerial vehicle Autonomous vehicle Regression Monte Carlo Motion planning Probabilistic planning 

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Steven A. P. Quintero
    • 1
  • Michael Ludkovski
    • 2
  • João P. Hespanha
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of CaliforniaSanta BarbaraUSA
  2. 2.Department of Statistics and Applied ProbabilityUniversity of CaliforniaSanta BarbaraUSA

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