Persistent Robotic Tasks: Monitoring and Sweeping in Changing Environments

In this paper, we present controllers that enable mobile robots to persistently monitor or sweep a changing environment. The environment is modeled as a field that is defined over a finite set of locations. The field grows linearly at locations that are not within the range of a robot and decreases linearly at locations that are within range of a robot. We assume that the robots travel on given closed paths. The speed of each robot along its path is controlled to prevent the field from growing unbounded at any location. We consider the space of speed controllers that are parametrized by a finite set of basis functions. For a single robot, we develop a linear program that computes a speed controller in this space to keep the field bounded, if such a controller exists. Another linear program is derived to compute the speed controller that minimizes the maximum field value over the environment. We extend our linear program formulation to develop a multirobot controller that keeps the field bounded. We characterize, both theoretically and in simulation, the robustness of the controllers to modeling errors and to stochasticity in the environment.

[1]  Jorge Cortés,et al.  Distributed Kriged Kalman Filter for Spatial Estimation , 2009, IEEE Transactions on Automatic Control.

[2]  Ben Grocholsky,et al.  Information-Theoretic Control of Multiple Sensor Platforms , 2002 .

[3]  Huan Liu,et al.  Building a distributed robot garden , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Randy A. Freeman,et al.  Multi-Agent Coordination by Decentralized Estimation and Control , 2008, IEEE Transactions on Automatic Control.

[5]  Randy A. Freeman,et al.  Decentralized Environmental Modeling by Mobile Sensor Networks , 2008, IEEE Transactions on Robotics.

[6]  Naomi Ehrich Leonard,et al.  Cooperative Filters and Control for Cooperative Exploration , 2010, IEEE Transactions on Automatic Control.

[7]  Peter Corke,et al.  A New robot for environmental monitoring on the Great Barrier Reef , 2004 .

[8]  Alexei Makarenko,et al.  Information based adaptive robotic exploration , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Daniela Rus,et al.  Multi-robot monitoring in dynamic environments with guaranteed currency of observations , 2010, 49th IEEE Conference on Decision and Control (CDC).

[10]  Dusan M. Stipanovic,et al.  On persistent coverage control , 2007, 2007 46th IEEE Conference on Decision and Control.

[11]  Howie Choset,et al.  Coverage for robotics – A survey of recent results , 2001, Annals of Mathematics and Artificial Intelligence.

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

[13]  Dimitris Bertsimas,et al.  Stochastic and Dynamic Vehicle Routing in the Euclidean Plane with Multiple Capacitated Vehicles , 1993, Oper. Res..

[14]  Marilena Vendittelli,et al.  WMR control via dynamic feedback linearization: design, implementation, and experimental validation , 2002, IEEE Trans. Control. Syst. Technol..

[15]  Ryan N. Smith.  Persistent Ocean Monitoring with Underwater Gliders : Adapting Spatiotemporal Sampling Resolution , 2010 .

[16]  T. Poggio,et al.  The Mathematics of Learning: Dealing with Data , 2005, 2005 International Conference on Neural Networks and Brain.

[17]  Leonard Kleinrock,et al.  Queueing Systems: Volume I-Theory , 1975 .

[18]  George J. Pappas,et al.  On trajectory optimization for active sensing in Gaussian process models , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[19]  B Bethke,et al.  Agent capability in persistent mission planning using approximate dynamic programming , 2010, Proceedings of the 2010 American Control Conference.

[20]  Randal W. Beard,et al.  Decentralized Perimeter Surveillance Using a Team of UAVs , 2005, IEEE Transactions on Robotics.

[21]  Boleslaw K. Szymanski,et al.  Efficient and inefficient ant coverage methods , 2001, Annals of Mathematics and Artificial Intelligence.

[22]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.

[23]  Suman Srinivasan,et al.  Airborne traffic surveillance systems: video surveillance of highway traffic , 2004, VSSN '04.

[24]  N. Cressie.  The origins of kriging , 1990 .

[25]  Yiannis Ventikos,et al.  Robotic swarm concept for efficient oil spill confrontation. , 2008, Journal of hazardous materials.

[26]  Howie Choset,et al.  Limited communication, multi-robot team based coverage , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[27]  Jorge Cortés,et al.  Adaptive Information Collection by Robotic Sensor Networks for Spatial Estimation , 2012, IEEE Transactions on Automatic Control.

[28]  Emilio Frazzoli,et al.  Dynamic Vehicle Routing with Priority Classes of Stochastic Demands , 2009, SIAM J. Control. Optim..

[29]  S. Zucker,et al.  Toward Efficient Trajectory Planning: The Path-Velocity Decomposition , 1986 .

[30]  I. Kroo,et al.  Persistent Surveillance Using Multiple Unmanned Air Vehicles , 2008, 2008 IEEE Aerospace Conference.

[31]  Srinivas Akella,et al.  Coordinating Multiple Robots with Kinodynamic Constraints Along Specified Paths , 2005, Int. J. Robotics Res..

[32]  Gaurav S. Sukhatme,et al.  Persistent ocean monitoring with underwater gliders: Adapting sampling resolution , 2011, J. Field Robotics.

[33]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[34]  Yann Chevaleyre,et al.  Theoretical analysis of the multi-agent patrolling problem , 2004, Proceedings. IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2004. (IAT 2004)..

[35]  Tucker Balch,et al.  Making a Clean Sweep: Behavior Based Vacuuming , 1993 .

[36]  Peter Stone,et al.  A multi-robot system for continuous area sweeping tasks , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[37]  Esther M. Arkin,et al.  Angewandte Mathematik Und Informatik Universit at Zu K Oln Approximation Algorithms for Lawn Mowing and Milling Ss Andor P.fekete Center for Parallel Computing Universitt at Zu Kk Oln D{50923 Kk Oln Germany Approximation Algorithms for Lawn Mowing and Milling , 2022 .

[38]  Noa Agmon,et al.  Multi-Robot Area Patrol under Frequency Constraints , 2007, ICRA.