Eyes in the Sky: Decentralized Control for the Deployment of Robotic Camera Networks

This paper presents a decentralized control strategy for positioning and orienting multiple robotic cameras to collectively monitor an environment. The cameras may have various degrees of mobility from six degrees of freedom, to one degree of freedom. The control strategy is proven to locally minimize a novel metric representing information loss over the environment. It can accommodate groups of cameras with heterogeneous degrees of mobility (e.g., some that only translate and some that only rotate), and is adaptive to robotic cameras being added or deleted from the group, and to changing environmental conditions. The robotic cameras share information for their controllers over a wireless network using a specially designed multihop networking algorithm. The control strategy is demonstrated in repeated experiments with three flying quadrotor robots indoors, and with five flying quadrotor robots outdoors. Simulation results for more complex scenarios are also presented.

[1]  J.K. Hedrick,et al.  An overview of emerging results in cooperative UAV control , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[2]  Roberto Cipolla,et al.  Multiview Photometric Stereo , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Randal W. Beard,et al.  Cooperative Surveillance with Multiple UAVs , 2008 .

[5]  Jorge Cortes,et al.  Maximizing visibility in nonconvex polygons: nonsmooth analysis and gradient algorithm design , 2005 .

[6]  Gerd Hirzinger,et al.  Energy-efficient Autonomous Four-rotor Flying Robot Controlled at 1 kHz , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

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

[8]  Mac Schwager,et al.  Optimal coverage for multiple hovering robots with downward facing cameras , 2009, 2009 IEEE International Conference on Robotics and Automation.

[9]  Mac Schwager,et al.  A Location-Based Algorithm for Multi-Hopping State Estimates within a Distributed Robot Team , 2009, FSR.

[10]  Mubarak Shah,et al.  Consistent Labeling of Tracked Objects in Multiple Cameras with Overlapping Fields of View , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Francesco Bullo,et al.  Distributed Control of Robotic Networks , 2009 .

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

[13]  Takeo Kanade,et al.  Algorithms for cooperative multisensor surveillance , 2001, Proc. IEEE.

[14]  Vijay Kumar,et al.  Planning and Control of Mobile Robots in Image Space from Overhead Cameras , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[15]  F. Bullo,et al.  Motion Coordination with Distributed Information , 2007 .

[16]  A. Volgenant,et al.  Facility location: a survey of applications and methods , 1996 .

[17]  Francesco Bullo,et al.  Maximizing visibility in nonconvex polygons: nonsmooth analysis and gradient algorithm design , 2005, Proceedings of the 2005, American Control Conference, 2005..

[18]  M. Hirsch,et al.  Differential Equations, Dynamical Systems, and Linear Algebra , 1974 .

[19]  Jonathan P. How,et al.  Cooperative Vision Based Estimation and Tracking Using Multiple UAVs , 2007 .

[20]  José Carlos Goulart de Siqueira,et al.  Differential Equations , 1919, Nature.

[21]  Shahin Sirouspour,et al.  Optimal positioning of multiple cameras for object recognition using Cramer-Rao lower bound , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[22]  Y. Bar-Shalom,et al.  Multisensor resource deployment using posterior Cramer-Rao bounds , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[23]  Mac Schwager,et al.  Decentralized, Adaptive Coverage Control for Networked Robots , 2009, Int. J. Robotics Res..

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

[25]  Tim J. Ellis,et al.  Multi camera image tracking , 2006, Image Vis. Comput..

[26]  Supun Samarasekera,et al.  Aerial video surveillance and exploitation , 2001, Proc. IEEE.

[27]  J. P. Lasalle Some Extensions of Liapunov's Second Method , 1960 .

[28]  Said Salhi,et al.  Facility Location: A Survey of Applications and Methods , 1996 .

[29]  Jake K. Aggarwal,et al.  Tracking Human Motion in Structured Environments Using a Distributed-Camera System , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Brian John Julian An embedded controller for quad-rotor flying robots running distributed algorithms , 2009 .

[31]  Roland Siegwart,et al.  Voronoi coverage of non-convex environments with a group of networked robots , 2010, 2010 IEEE International Conference on Robotics and Automation.

[32]  Hanumant Singh,et al.  Toward large-area mosaicing for underwater scientific applications , 2003 .

[33]  W. Marsden I and J , 2012 .

[34]  Mac Schwager,et al.  A gradient optimization approach to adaptive multi-robot control , 2009 .