Health-Aware Coverage Control With Application to a Team of Small UAVs

This paper studies the problem of controlling a team of vehicles in order to cover a region with their onboard sensors. The goal is to perform such a coverage task when the capability of the vehicles to carry out the task varies through time. We consider variable sensing performance and the loss of vehicles. Algorithms are proposed to enable coverage despite such variable health conditions. These algorithms are validated through experiments with quad-rotor unmanned air vehicles. Since these algorithms require communication among the vehicles, simulations are carried out to show that the proposed algorithms degrade nicely when perfect communication is not possible.

[1]  Mac Schwager,et al.  Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment , 2011, Int. J. Robotics Res..

[2]  Anthony Tzes,et al.  Convex Voronoi-inspired space partitioning for heterogeneous networks: a coverage-oriented approach , 2010 .

[3]  Nicolas Lechevin,et al.  Optimizing the location of sensors subject to health degradation , 2011, Proceedings of the 2011 American Control Conference.

[4]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[5]  Mac Schwager,et al.  Persistent Robotic Tasks: Monitoring and Sweeping in Changing Environments , 2011, IEEE Transactions on Robotics.

[6]  Franz Aurenhammer,et al.  Voronoi diagrams—a survey of a fundamental geometric data structure , 1991, CSUR.

[7]  Qiang Du,et al.  Centroidal Voronoi Tessellations: Applications and Algorithms , 1999, SIAM Rev..

[8]  Mac Schwager,et al.  Eyes in the Sky: Decentralized Control for the Deployment of Robotic Camera Networks , 2011, Proceedings of the IEEE.

[9]  Franz Aurenhammer,et al.  Power Diagrams: Properties, Algorithms and Applications , 1987, SIAM J. Comput..

[10]  Jorge Cortés,et al.  Coverage Optimization and Spatial Load Balancing by Robotic Sensor Networks , 2010, IEEE Transactions on Automatic Control.

[11]  B. Bethke,et al.  Group health management of UAV teams with applications to persistent surveillance , 2008, 2008 American Control Conference.

[12]  Mario J. Valenti.  Approximate dynamic programming with applications in multi-agent systems , 2007 .

[13]  Yue Wang,et al.  Awareness coverage control over large scale domains with intermittent communications , 2008, ACC.

[14]  Sonia Martínez,et al.  Coverage control for mobile sensing networks , 2002, IEEE Transactions on Robotics and Automation.

[15]  Francesco Bullo,et al.  Esaim: Control, Optimisation and Calculus of Variations Spatially-distributed Coverage Optimization and Control with Limited-range Interactions , 2022 .

[16]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[17]  Vijay Kumar,et al.  Sensing and coverage for a network of heterogeneous robots , 2008, 2008 47th IEEE Conference on Decision and Control.