Dual Kalman--Bucy Filters and Interactive Entropy Production
SIAM Journal on Control and Optimization
Dual Nonlinear Filters and Entropy Production
SIAM Journal on Control and Optimization
On the roles of smoothing in planning of informative paths
ACC'09 Proceedings of the 2009 conference on American Control Conference
Brief Sensor scheduling in continuous time
Automatica (Journal of IFAC)
Optimal sensor placement and motion coordination for target tracking
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Mutual information of the white Gaussian channel with and without feedback
IEEE Transactions on Information Theory
Mutual information and minimum mean-square error in Gaussian channels
IEEE Transactions on Information Theory
On the roles of smoothing in planning of informative paths
ACC'09 Proceedings of the 2009 conference on American Control Conference
Distributed robotic sensor networks: An information-theoretic approach
International Journal of Robotics Research
Persistent awareness coverage control for mobile sensor networks
Automatica (Journal of IFAC)
Hi-index | 22.15 |
This paper addresses the planning of continuous paths for mobile sensors to reduce the uncertainty in some quantities of interest in the future. The mutual information between the measurement along the continuous path and the future verification variables defines the information reward. Two expressions for computing this mutual information are presented: the filter form extended from the state of the art and the smoother form inspired by the conditional independence structure. The key properties of the approach using the filter and smoother strategies are presented and compared. The smoother form is shown to be preferable because it provides better computational efficiency, facilitates easy integration with existing path synthesis tools, and, most importantly, enables correct quantification of the rate of information accumulation. A spatial interpolation technique is used to relate the motion of the sensor to the evolution of the measurement matrix, which leads to the formulation of the optimal path planning problem. A gradient-ascent steering law based on the concept of information potential field is also presented as a computationally efficient suboptimal strategy. A simplified weather forecasting example is used to compare several planning methodologies and to illustrate the potential performance benefits of using the proposed planning approach.