Master of Engineering (Research) / In many multi-objective missions there are situations when actions based on maximum information gain may not be the `best' given the overall mission objectives. In addition to properties such as entropy, information also has value, which is situationally dependent. This thesis examines the concept of information value in a multi-objective mission from an information theory perspective. A derivation of information value is presented that considers both the context of information, via a fused world belief state, and a system mission. The derived information value is used as part of the objective function for control of autonomous platforms within a framework developed for human robot cooperative control. A simulated security operation in a structured environment is implemented to test both the framework, and information value based control. The simulation involves a system of heterogeneous, sensor equipped Unmanned Aerial Vehicles (UAVs), tasked with gathering information regarding ground vehicles. The UAVs support an e ort to protect a number of important buildings in the area of operation. Thus, the purpose of the information is to aid the security operation by ensuring that security forces can deploy e ciently to counter any threat. A number of di erent local controllers using information based control are implemented and compared to a task based control scheme. The relative performance of each is examined with respect to a number of performance metrics with conclusions drawn regarding the performance and exibility of information value based control.

Master of Engineering (Research) / In many multi-objective missions there are situations when actions based on maximum information gain may not be the `best' given the overall mission objectives. In addition to properties such as entropy, information also has value, which is situationally dependent. This thesis examines the concept of information value in a multi-objective mission from an information theory perspective. A derivation of information value is presented that considers both the context of information, via a fused world belief state, and a system mission. The derived information value is used as part of the objective function for control of autonomous platforms within a framework developed for human robot cooperative control. A simulated security operation in a structured environment is implemented to test both the framework, and information value based control. The simulation involves a system of heterogeneous, sensor equipped Unmanned Aerial Vehicles (UAVs), tasked with gathering information regarding ground vehicles. The UAVs support an e ort to protect a number of important buildings in the area of operation. Thus, the purpose of the information is to aid the security operation by ensuring that security forces can deploy e ciently to counter any threat. A number of di erent local controllers using information based control are implemented and compared to a task based control scheme. The relative performance of each is examined with respect to a number of performance metrics with conclusions drawn regarding the performance and exibility of information value based control.

This thesis considers the coordinated control of autonomous agents. The agents are modeled as double integrators, one for each Cartesian dimension. The goal is to force the agents to converge to a formation specified by their desired relative positions. To this end a pair of one-step-ahead optimization based control laws are developed. The control algorithms produce a communication topology that mirrors the geometric formation topology due to the careful choice of the minimized cost functions. Through this equivalence a natural understanding of the relationship between the geometric formation topology and the communication infrastructure is gained. It is shown that the control laws are stable and guarantee convergence for all viable formation topologies. Additionally, velocity constraints can be added to allow the formation to follow fixed or arbitrary time dependent velocities. Both control algorithms only require local information exchange. As additional agents attach to the formation, only those agents that share position constraints with the joining agents need to adjust their control laws. When redundancy is incorporated into the formation topology, it is possible for the system to survive loss of agents or communication channels. In the event that an agent drops out of the formation, only the agents with position interdependence on the lost agent need to adjust their control laws. Finally, if a communication channel is lost, only the agents that share that communication channel must adjust their control laws. The first control law falls into the category of distributed control, since it requires either the global information exchange to compute the formation size or an a priori knowledge of the largest possible formation. The algorithm uses the network size to penalize the control input for each formation. When using a priori knowledge, it is shown that additional redundancy not only adds robustness to loss of agents or communication channels, but it also decreases the settling times to the desired formation. Conversely, the overall control strategy suffers from sluggish response when the network is small with respect to the largest possible network. If global information exchange is used, scalability suffers. The second control law was developed to address the negative aspects of the first. It is a fully decentralized controller, as it does not require global information exchange or any a priori knowledge.

Multi-agent systems are extensively used in several applications. An important class of applications involves the optimal spatial distribution of a group of mobile robots on a given area, where the optimality refers to the assignment of subregions to the robots, in such a way that a suitable coverage metric is maximized. Typically the coverage metric encodes a risk distribution defined on the area, and a measure of the performance of individual robots with respect to points inside the region of interest. The coverage metric will be maximized when the set of mobile robots configure themselves as the centroids of the Voronoi tessellation dictated by the risk density. In this work we advance on this result by considering a generalized area control problem in which the coverage metric is non-autonomous, that coverage metric is time varying independently of the states of the robots. This generalization is motivated by the study of coverage control problems in which the coordinated motion of a set of mobile robots accounts for the kinematics of objects penetrating from the outside. Asymptotic convergence and optimality of the non-autonmous system are studied by means of Barbalat's Lemma, and connections with the kinematics of the moving intruders is established. Several numerical simulation results are used to illustrate theoretical predictions.