Ben Grocholsky Doctor of Philosophy The University of Sydney March 2002 Information-Theoretic Control of Multiple Sensor Platforms This thesis is concerned with the development of a consistent, information-theoretic basis for understanding of coordination and cooperation decentralised multi-sensor multi-platform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue, mining or intelligent manufacturing. However, the effective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis. This thesis focuses on the problem of coordination and cooperation for teams of autonomous systems engaged in information gathering and data fusion tasks. While this is a subset of the general cooperative autonomous systems problem, it still encompasses a range of possible applications in picture compilation, navigation, searching and map building problems. The great advantage of restricting the domain of interest in this way is that an underlying mathematical model for coordination and cooperation can be based on the use of information-theoretic models of platform and sensor abilities. The information theoretic approach builds on the established principles and architecture previously developed for decentralised data fusion systems. In the decentralised control problem addressed in this thesis, each platform and sensor system is considered to be a distinct decision maker with an individual information-theoretic utility measure capturing both local objectives and the inter-dependencies among the decisions made by other members of the team. Together these information-theoretic utilities constitute the team objective. The key contributions of this thesis lie in the quantification and study of cooperative control between sensors and platforms using information as a common utility measure. In particular, • The problem of information gathering is formulated as an optimal control problem by identifying formal measures of information with utility or pay-off. • An information-theoretic utility model of coupling and coordination between decentralised decision makers is elucidated. This is used to describe how the information gathering strategies of a team of autonomous systems are coupled. • Static and dynamic information structures for team members are defined. It is shown that the use of static information structures can lead to efficient, although sub-optimal, decentralised control strategies for the team. • Significant examples in decentralised control of a team of sensors are developed. These include the multi-vehicle multi-target bearings-only tracking problem, and the area coverage or exploration problem for multiple vehicles. These examples demonstrate the range of non-trivial problems to which the theory in this thesis can be employed.

Information-Theoretic Control of Multiple Sensor Platforms

In this paper, we study the problem of optimal trajectory generation for a team of heterogeneous robots moving in a plane and tracking a moving target by processing relative observations, i.e., distance and/or bearing. Contrary to previous approaches, we explicitly consider limits on the robots' speed and impose constraints on the minimum distance at which the robots are allowed to approach the target. We first address the case of a single tracking sensor and seek the next sensing location in order to minimize the uncertainty about the target's position. We show that although the corresponding optimization problem involves a nonconvex objective function and a nonconvex constraint, its global optimal solution can be determined analytically. We then extend the approach to the case of multiple sensors and propose an iterative algorithm, i.e., the Gauss-Seidel relaxation (GSR), to determine the next best sensing location for each sensor. Extensive simulation results demonstrate that the GSR algorithm, whose computational complexity is linear in the number of sensors, achieves higher tracking accuracy than gradient descent methods and has performance that is indistinguishable from that of a grid-based exhaustive search, whose cost is exponential in the number of sensors. Finally, through experiments, we demonstrate that the proposed GSR algorithm is robust and applicable to real systems.

/pdf/multirobot-active-target-tracking-with-combinations-of-1br5a2fv94.pdf

Multirobot Active Target Tracking With Combinations of Relative Observations

Small unmanned aerial vehicles (UAVs) equipped with navigation and video capabilities can be used to perform target localization Combining UAV state estimates with image data leads to bearing measurements of the target that can be processed to determine its position This 3-D bearings-only estimation problem is nonlinear and traditional ltering methods are prone to biases, noisy estimates, and lter instabilities The performance of the target localization is highly dependent on the vehicle trajectory, motivating the development of optimal UAV trajectories This work presents methods for designing trajectories that increase the amount of information provided by the measurements and shows that these trajectories lead to enhanced estimation performance The Fisher Information Matrix (FIM) is used to quantify the information provided by the measurements Several objective functions based on the FIM are considered and the A-optimality criterion is shown to be the best suited for trajectory optimization in the 3-D bearings-only target localization problem The resulting trajectories produce spirals, which increase the angular separation between measurements and reduce the range to the target, supporting geometric intuition The problem of simultaneous target estimation and vehicle trajectory optimization is explored and the resulting algorithms produce vehicle trajectories that increase the information provided by the measurements, enhancing the target estimation performance by increasing accuracy, reducing uncertainty and improving lter convergence

/pdf/trajectory-optimization-for-target-localization-using-small-10nbamt6xm.pdf

Trajectory Optimization for Target Localization Using Small Unmanned Aerial Vehicles

A path planning algorithm is presented for uninhabited aerial vehicles (UAVs) trying to geolocate an emitter using passive payload sensors. The objective is to generate a sequence of waypoints for each vehicle that minimizes localization uncertainty. The path planning problem is cast as a nonlinear programming problem using an approximation of the Fisher information matrix (FIM) and solved at successive waypoints to generate vehicle trajectories. The effectiveness of the proposed algorithm is illustrated with simulation examples.

UAV Path Planning for Passive Emitter Localization

The bearings-only pseudolinear target track estimator is known to suffer from severe bias problems. This paper presents a bias analysis for the pseudolinear estimator and develops a method of bias compensation, resulting in a closed-form reduced-bias pseudolinear estimator. The reduced-bias estimator is then incorporated into an instrumental variable estimator to produce asymptotically unbiased target motion parameter estimates. Unlike batch iterative estimators, the proposed instrumental variable estimator has a closed-from solution and therefore avoids the convergence problems associated with iterative estimators. The performance of the proposed instrumental variable estimator is illustrated by way of simulation examples and is shown to be almost identical to that of the computationally more demanding iterative maximum likelihood estimator.

Bias compensation for the bearings-only pseudolinear target track estimator

In bearings-only tracking, observer maneuver is critical to ensure observability and to obtain an accurate target localization. Here, optimal control theory is applied to the determination of the course of a constant speed observer that minimizes an accuracy criterion deduced from the Fisher information matrix (FIM). Necessary conditions for optimal maneuver (Euler equations) are established and resolved, partly by analytical means and partly by an iterative numerical procedure. Examples of optimal observer maneuvers are presented and discussed.

Optimal observer maneuver for bearings-only tracking

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Optimal observer maneuver for bearings-only tracking