Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/34750
Title: Information-theoretic multi-robot path planning
Authors: CAO NANNAN
Keywords: Environmental Sensing, Gaussian Process, Robotics, Path Planning
Issue Date: 8-Jun-2012
Source: CAO NANNAN (2012-06-08). Information-theoretic multi-robot path planning. ScholarBank@NUS Repository.
Abstract: Research in environmental sensing and monitoring is especially important in supporting environmental sustainability efforts worldwide, and has recently attracted significant attention and interest. A key direction of this research lies in modeling and predicting the spatiotemporally varying environmental phenomena. One approach is to use a team of robots to sample the area and model the measurement values at unobserved points. For smoothly varying and hot-spot fields, there is some work which has been done to model the fields well. However, there is still a class of common environmental fields called anisotropic fields in which the spatial phenomena are highly correlated along one direction and less correlated along the perpendicular direction. We exploit the environmental structure to improve the sampling performance and time efficiency of planning for anisotropic fields. In this thesis, we cast the planning problem into a stagewise decision-theoretic problem. we adopt Gaussian Process to model spatial phenomena. Maximum entropy criterion and maximum mutual information criterion are used to measure the informativeness of the observation paths. It is found that for many GPs, correlation of two points exponentially decreases with the distance between the two points. With this property, for maximum entropy criterion, we propose a polynomial-time approximation algorithm, MEPP, to find the maximum entropy paths. We also provide a theoretical performance guarantee for this algorithm. For maximum mutual information criterion, we propose another polynomial-time approximation algorithm, M$^2$IPP. Similar to the MEPP, a performance guarantee is also provided for this algorithm. We demonstrate the performance advantages of our algorithms on two real data sets. To get lower prediction error, three priciples have also been proposed to select the criterion for different environmental fields.
URI: http://scholarbank.nus.edu.sg/handle/10635/34750
Appears in Collections:Master's Theses (Open)

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