Continuous trajectory planning of mobile sensors for informative forecasting
By Han-Lim Choi, Jonathan P. How. 2010
79 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Aryan Mokhtari
- Hamed Hassani
- Amirhossein Reisizadeh
- Ramtin Pedarsani
- Hossein Taheri
- Soumik Sarkar
- Chinmay Hegde
- Aditya Balu
In Computational Theory and Mathematics
In Computer Vision and Pattern Recognition
- Soumik Sarkar
- Chinmay Hegde
- Aditya Balu
- Zhanhong Jiang
- Ramtin Pedarsani
- Aryan Mokhtari
- Hamed Hassani
- Wenhan Yang
Citations in the corpus
- Robust and Communication-Efficient Collaborative Learning (2019, background): “…learning naturally arises in various applications such as distributed deep learning (LeCun et al., 2015; Dean et al., 2012), multi-agent robotics and path planning (Choi and How, 2010; Jha et al., 2016), distributed resource allocation in wireless networks (Ribeiro, 2010), to name a few.”
- Collaborative Deep Learning in Fixed Topology Networks (2017, background): “network that aims to learn a model of the environment in a collaborative manner [10, 11].”