TY - JOUR
T1 - Navigating UAVs for Optimal Monitoring of Groups of Moving Pedestrians or Vehicles
AU - Huang, Hailong
AU - Savkin, Andrey V.
N1 - Funding Information:
Manuscript received April 3, 2020; revised August 8, 2020, November 24, 2020, and February 26, 2021; accepted March 7, 2021. Date of publication March 10, 2021; date of current version May 5, 2021. This work was supported in part by Australian Research Council and in part by Australian Government under Grant AUSMURIB000001 associated with ONR MURI under Grant N00014-19-1-2571. The review of this article was coordinated by Prof. X. Wang. (Corresponding author: Hailong Huang.) The authors are with the School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney 2052, Australia (e-mail: [email protected]; [email protected]). Digital Object Identifier 10.1109/TVT.2021.3065102
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - This paper focuses on navigating a team of unmanned aerial vehicles (UAVs) equipped with cameras to monitor groups of ground pedestrians or vehicles that move along given paths with unkown, time-varying but bounded speeds. The objective is to deliver a high-quality surveillance of the pedestrians or vehicles. We formulate a surveillance problem which requires the UAVs to monitor the pedestrians or vehicles from as short as possible distances. We propose a navigation algorithm that enables each UAV to determine its movement locally with a minor participation of a central station. We prove that this algorithm is locally optimal. Simulations confirm its performance.
AB - This paper focuses on navigating a team of unmanned aerial vehicles (UAVs) equipped with cameras to monitor groups of ground pedestrians or vehicles that move along given paths with unkown, time-varying but bounded speeds. The objective is to deliver a high-quality surveillance of the pedestrians or vehicles. We formulate a surveillance problem which requires the UAVs to monitor the pedestrians or vehicles from as short as possible distances. We propose a navigation algorithm that enables each UAV to determine its movement locally with a minor participation of a central station. We prove that this algorithm is locally optimal. Simulations confirm its performance.
KW - Aerial surveillance
KW - Internet of flying robots
KW - UAVs
KW - coverage
KW - drones
KW - mobile ground targets
KW - navigation
UR - http://www.scopus.com/inward/record.url?scp=85102640055&partnerID=8YFLogxK
U2 - 10.1109/TVT.2021.3065102
DO - 10.1109/TVT.2021.3065102
M3 - Journal article
VL - 70
SP - 3891
EP - 3896
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 4
M1 - 9374712
ER -