Direct learning coverage control based on expectation maximization in wireless sensor and robot network

This paper proposes a direct learning controller for wireless sensor and robot network to coverage an environment by utilizing expectation maximization algorithm. In addition to sensors, including high-level and low-level sensors, mounted on mobile robots, low-level stationary sensors are considered to provide information for enhance the performance of coverage control. The main objective is to maximize the information quantity of high-level sensors from the sensing density generated by a group of low-level sensors. This direct method uses the parameter of basis function to design controller based on Expectation Maximization (EM) algorithm. Moreover, the proposed estimation and learning law are also used in indirect method for other coverage controller. Subsequently, this paper proposes a transformation method based on EM algorithm for coverage problem with complicated sensing function such as Gaussian function. Numerical examples are introduced to demonstrate the performance of the proposed direct coverage control in wireless sensor and robot network.

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

[2]  E. Levitan,et al.  A Maximum a Posteriori Probability Expectation Maximization Algorithm for Image Reconstruction in Emission Tomography , 1987, IEEE Transactions on Medical Imaging.

[3]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[4]  T. Moon.  The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..

[5]  U. Fayyad,et al.  Scaling EM (Expectation Maximization) Clustering to Large Databases , 1998 .

[6]  Lynne E. Parker,et al.  Editorial: Advances in Multi-Robot Systems , 2002 .

[7]  Lynne E. Parker,et al.  Guest editorial advances in multirobot systems , 2002, IEEE Trans. Robotics Autom..

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

[9]  Dusan M. Stipanovic,et al.  Effective Coverage Control for Mobile Sensor Networks With Guaranteed Collision Avoidance , 2007, IEEE Transactions on Control Systems Technology.

[10]  Jaeyong Lee,et al.  Potential Field Based Hierarchical Structure for Mobile Sensor Network Deployment , 2007, 2007 American Control Conference.

[11]  Mark W. Spong,et al.  Cooperative Avoidance Control for Multiagent Systems , 2007 .

[12]  Attila Gilányi,et al.  An Introduction to the Theory of Functional Equations and Inequalities , 2008 .

[13]  Biswanath Mukherjee,et al.  Wireless sensor network survey , 2008, Comput. Networks.

[14]  Masayuki Fujita,et al.  Coverage control for camera sensor networks: Its implementation and experimental verification , 2009, 2009 17th Mediterranean Conference on Control and Automation.

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

[16]  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.

[17]  Gang Feng,et al.  Decentralized adaptive awareness coverage control for multi-agent networks , 2011, Autom..

[18]  Dongbing Gu,et al.  Spatial Gaussian Process Regression With Mobile Sensor Networks , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Sung G. Lee,et al.  Multirobot Control Using Time-Varying Density Functions , 2014, IEEE Transactions on Robotics.

[20]  Fu Xiao,et al.  Surface Coverage Algorithm in Directional Sensor Networks for Three-Dimensional Complex Terrains , 2016 .

[21]  Hoam Chung,et al.  Decentralized Adaptive Coverage Control of Nonholonomic Mobile Robots , 2016 .

[22]  Farshid Abbasiy,et al.  Team-based coverage control of moving sensor networks , 2016 .

[23]  Yen-Chen Liu,et al.  Dynamic coverage control for mobile robot network with limited and nonidentical sensory ranges , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).