This thesis addresses the problem of information-driven sensor path planning for the purpose of target detection, measurement, and classification using non-holonomic mobile sensor agents (MSAs). Each MSA is equipped with two types of sensors. One is the measuring sensor with small FOV, while the other is the detecting sensor with large FOV. The measuring sensor could be ground penetrating radar (GPR), while the detecting sensor can be infrared radar (IR). The classification of a target can be reduced to the problem of estimating one or more random variables associated with this target from partial or imperfect measurements from sensors, and can be represented by a probability mass function (PMF). Previous work shows the performance of MSAs can be greatly improved by planning their motion and control laws based on their sensing objectives. Because of the stochastic nature of sensing objective, the expected measurement benefit of a target, i.e., the information value, is defined as the expected entropy reduction of its classification PMF before the next measurement is taken of this target. The information value of targets is combined with other robot motion planning methods to address the sensor planning problem. By definition, the entropy reduction can be represented by conditional mutual information of PMF given a measurement. MSAs are deployed in an obstacle-populated environment, and must avoid collisions with obstacles, as well as, in some cases, targets.

This thesis first presents a modified rapidly-exploring random trees (RRTs) approach with a novel milestone sampling method. The sampling function for RRTs takes into account the information value of targets, and sensor measurements of obstacle locations, as well as MSAs' configurations (e.g., position and orientation) and velocities to generate new milestones for expanding the trees online. By considering the information value, the sample function favors expansions toward targets with higher expected measurement benefit. After sampling, the MSAs navigate to the selected milestones based on the critic introduced later and take measurements of targets within their FOVs. Then, the thesis introduces an information potential method (IPM) approach which combined information values of targets with the potential functions. Targets with high information value have larger influence distance and tend to have high probability to be measured by the MSAs. Additionally, this information potential field is utilized to generate the milestones in a local probabilistic roadmap method to help MSAs escape their local minima.

The proposed two methods are applied to a landmine classification problem. It is assumed that geometries and locations of partial obstacles and targets are available as prior information, as well as previous measurements on targets concerning their classification. The experiments show that paths of MSAs using the modified RRTs and IPM take advantages of the information value by favoring targets with high information value. Furthermore, the results show that the IPM outperforms other approaches such as the modified RRTs with information value and classical potential field method that does not take target information value into account.