A set-membership approach to find and track multiple targets using a fleet of UAVs

This paper presents a set-membership approach for the coordinated control of a fleet of UAVs aiming to search and track an a priori unknown number of targets spread over some delimited geographical area. The originality of the approach lies in the description of the perturbations and measurement uncertainties via bounded sets. A set-membership approach is used to address the localization and tracking problem. At each time step, sets guaranteed to contain the actual state of already localized targets are provided. A set containing the states of targets still to be discovered is also evaluated. These sets are then used to evaluate the control input to apply to the UAVs so as to minimize the estimation uncertainty at the next time step. Simulations considering several UAVs show that the proposed set-membership estimator and the associated control input optimization are able to provide good localization and tracking performance for multiple targets.

[1]  Jianda Han,et al.  Active Persistent Localization of a Three-Dimensional Moving Target Under Set-Membership Uncertainty Description Through Cooperation of Multiple Mobile Robots , 2015, IEEE Transactions on Industrial Electronics.

[2]  Randal W. Beard,et al.  Cooperative Path Planning for Target Tracking in Urban Environments Using Unmanned Air and Ground Vehicles , 2015, IEEE/ASME Transactions on Mechatronics.

[3]  Siegfried M. Rump,et al.  INTLAB - INTerval LABoratory , 1998, SCAN.

[4]  Bernhard Rinner,et al.  Cooperative Robots to Observe Moving Targets: Review , 2018, IEEE Transactions on Cybernetics.

[5]  Fabio Morbidi,et al.  Active Target Tracking and Cooperative Localization for Teams of Aerial Vehicles , 2013, IEEE Transactions on Control Systems Technology.

[6]  Philip M. Dames,et al.  Distributed multi-target search and tracking using the PHD filter , 2017, 2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS).

[7]  Aníbal Ollero,et al.  Decentralized multi-robot cooperation with auctioned POMDPs , 2012, 2012 IEEE International Conference on Robotics and Automation.

[8]  Ümit Özgüner,et al.  Collaborative Multi-MSA Multi-Target Tracking and Surveillance: a Divide & Conquer Method Using Region Allocation Trees , 2017, J. Intell. Robotic Syst..

[9]  Eric Walter,et al.  Guaranteed recursive non‐linear state bounding using interval analysis , 2002 .

[10]  Simon Lacroix,et al.  Multi-robot target detection and tracking: taxonomy and survey , 2016, Auton. Robots.