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Information-driven Sensor Path Planning and the Treasure Hunt Problem

dc.contributor.advisor Ferrari, Silvia Cai, Chenghui 2008-05-14T16:29:14Z 2008-05-14T16:29:14Z 2008-04-25
dc.description.abstract This dissertation presents a basic information-driven sensor management problem, referred to as treasure hunt, that is relevant to mobile-sensor applications such as mine hunting, monitoring, and surveillance. The objective is to classify/infer one or multiple fixed targets or treasures located in an obstacle-populated workspace by planning the path and a sequence of measurements of a robotic sensor installed on a mobile platform associated with the treasures distributed in the sensor workspace. The workspace is represented by a connectivity graph, where each node represents a possible sensor deployment, and the arcs represent possible sensor movements. A methodology is developed for planning the sensing strategy of a robotic sensor deployed. The sensing strategy includes the robotic sensor's path, because it determines which targets are measurable given a bounded field of view. Existing path planning techniques are not directly applicable to robots whose primary objective is to gather sensor measurements. Thus, in this dissertation, a novel approximate cell-decomposition approach is developed in which obstacles, targets, the sensor's platform and field of view are represented as closed and bounded subsets of an Euclidean workspace. The approach constructs a connectivity graph with observation cells that is pruned and transformed into a decision tree, from which an optimal sensing strategy can be computed. It is shown that an additive incremental-entropy function can be used to efficiently compute the expected information value of the measurement sequence over time. The methodology is applied to a robotic landmine classification problem and the board game of CLUE$^{\circledR}$. In the landmine detection application, the optimal strategy of a robotic ground-penetrating radar is computed based on prior remote measurements and environmental information. Extensive numerical experiments show that this methodology outperforms shortest-path, complete-coverage, random, and grid search strategies, and is applicable to non-overpass capable platforms that must avoid targets as well as obstacles. The board game of CLUE$^{\circledR}$ is shown to be an excellent benchmark example of treasure hunt problem. The test results show that a player implementing the strategies developed in this dissertation outperforms players implementing Bayesian networks only, Q-learning, or constraint satisfaction, as well as human players.
dc.format.extent 3904924 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject Engineering, Mechanical
dc.subject Artificial Intelligence
dc.subject Sensor path planning
dc.subject information theory
dc.subject value of information
dc.subject robotic sensors
dc.subject Bayesian network sensor model
dc.subject fusion
dc.subject geometric sensing
dc.subject demining
dc.subject computer game
dc.title Information-driven Sensor Path Planning and the Treasure Hunt Problem
dc.type Dissertation
dc.department Mechanical Engineering and Materials Science

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