Multilayer feedforward networks are universal approximators
Neural Networks
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Coverage for robotics – A survey of recent results
Annals of Mathematics and Artificial Intelligence
Decentralized, Adaptive Coverage Control for Networked Robots
International Journal of Robotics Research
Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms
Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms
Optimal coverage for multiple hovering robots with downward facing cameras
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Gaussian networks for direct adaptive control
IEEE Transactions on Neural Networks
Dispersion and exploration for robot teams
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Rolling dispersion for robot teams
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Rolling dispersion and exploration for robot teams
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper proposes a distributed adaptive control algorithm for coverage control in unknown environments with networked mobile sensors. An online neural network weight tuning algorithm is used in order for the robots to estimate the sensory function of the environment, and the control law is derived according to the feedforward neural network estimation of the distribution density function of the environment. It is distributed in that it only takes advantage of local information of each robot. A Lyapunov function is introduced in order to show that the proposed control law causes the network to converge to a near-optimal sensing configuration. Due to neural network nonlinear approximation property, a major advantage of the proposed method is that in contrary to previous well known approaches for coverage, it is not restricted to a linear regression form. Finally the controller is demonstrated in numerical simulations. Simulation results have been shown that the proposed controller outperforms the previous adaptive approaches in the sense of performance and convergence rate.