Robot swarms are envisioned in applications such as surveillance, agriculture, search-and-rescue operations, and construction. The decentralized nature of swarm intelligence has three key advantages over traditional multi-robot control algorithms: it is scalable, it is fault tolerant, and it is not susceptible to a single point of failure. These advantages are critical to the task of persistent surveillance - where a number of target locations need to be visited as frequently as possible. Unfortunately, in the real world, the autonomous robots that can be used for persistent surveillance have a limited battery life (or fuel capacity). Thus, they need to abandon their surveillance duties to visit a battery swapping station (or refueling depot) a.k.a. €˜depots€™. This €˜down time€™ reduces the frequency of visitation. This problem can be eliminated if the depots themselves were autonomous vehicles that could meet the (surveillance) robots at some point along their path from one target to another. Thus, the robots would spend less time on the 'charging' (or refueling) task. In this thesis we present decentralized control algorithms, and their results, for three stages of the persistent surveillance problem. First, we consider the case where the robots have no energy constraints, and use a decentralized approach to allow the robots choose the €˜best€™ target that they should visit next. While the selection process is decentralized, the robots can communicate with all the other robots in the swarm, and let them know which is their chosen target. We then consider the energy constraints of the robots, and slightly modify the algorithm, so that the robots visit a depot before they run out of energy. Lastly, we consider the case where the depots themselves can move, and communicate with the robots to pick a location and time to meet, to be able to swap the empty battery of a robot, with a fresh one. The goal of persistent surveillance is to visit target locations as frequently as possible, and thus, the performance measurement parameter is chosen to be the median frequency of visitation for all target locations. We evaluate the performance of the three algorithms in an extensive set of simulated experiments.

The success of unmanned aerial vehicles (UAV) in the Iraq and Afghanistan conflicts has led to increased interest in further digitalization of the United States armed forces. Although unmanned systems have been a tool of the military for several decades, only recently have advances in the field of Micro-Electro-Mechanical Systems (MEMS) technology made it possible to develop systems capable of being transported by an individual soldier. These miniature unmanned systems, more commonly referred to as micro air vehicles (MAV), are envisioned by the Department of Defense as being an integral part of maintaining America?s military superiority. As researchers continue to make advances in the miniaturization of flight hardware, a new problem with regard to MAV field operations is beginning to present itself. To date, little work has been done to determine an effective means of collecting, analyzing, and handling information that can satisfy the goal of using MAVs as tools for persistent surveillance. Current systems, which focus on the transmission of analog video streams, have been very successful on larger UAVs such as the RQ-11 Raven but have proven to be very demanding of the operator. By implementing a new and innovative data processing methodology, currently existing hardware can be adapted to effectively present critical information with minimal user input. Research currently being performed at Texas A&M University in the areas of attitude determination and image processing has yielded a new application of photographic projection. By replacing analog video with spatially aware high-resolution images, the present MAV handheld ground control stations (GCS) can be enhanced to reduce the number of functional manpower positions required during operation. Photographs captured by an MAV can be displayed above pre-existing satellite imagery to give an operator a lasting reference to the location of objects in his vicinity. This newly generated model also increases the functionality of micro air vehicles by allowing for target tracking and energy efficient perch and stare capabilities, both essential elements of persistent surveillance.

In a multi-agent system (MAS), the overall performance is greatly influenced by both the design and the control of the agents. The physical design determines the agent capabilities, and the control strategies drive the agents to pursue their objectives using the available capabilities. The objective of this thesis is to incorporate control strategies in the early conceptual design of an MAS. As such, this thesis proposes a methodology that mainly explores the interdependency between the design variables of the agents and the control strategies used by the agents. The output of the proposed methodology, i.e. the interdependency between the design variables and the control strategies, can be utilized in the requirement analysis as well as in the later design stages to optimize the overall system through some higher fidelity analyses. In this thesis, the proposed methodology is applied to a persistent multi-UAV surveillance problem, whose objective is to increase the situational awareness of a base that receives some instantaneous monitoring information from a group of UAVs. Each UAV has a limited energy capacity and a limited communication range. Accordingly, the connectivity of the communication network becomes essential for the information flow from the UAVs to the base. In long-run missions, the UAVs need to return to the base for refueling with certain frequencies depending on their endurance. Whenever a UAV leaves the surveillance area, the remaining UAVs may need relocation to mitigate the impact of its absence. In the control part of this thesis, a set of energy-aware control strategies are developed for efficient multi-UAV surveillance operations. To this end, this thesis first proposes a decentralized strategy to recover the connectivity of the communication network. Second, it presents two return policies for UAVs to achieve energy-aware persistent surveillance. In the design part of this thesis, a design space exploration is performed to investigate the overall performance by varying a set of design variables and the candidate control strategies. Overall, it is shown that a control strategy used by an MAS affects the influence of the design variables on the mission performance. Furthermore, the proposed methodology identifies the preferable pairs of design variables and control strategies through low fidelity analysis in the early design stages.

Although sensor planning in computer vision has been a subject of research for over two decades, a vast majority of the research seems to concentrate on two particular applications in a rather limited context of laboratory and industrial workbenches, namely 3D object reconstruction and robotic arm manipulation. Recently, increasing interest is engaged in research to come up with solutions that provide wide-area autonomous surveillance systems for object characterization and situation awareness, which involves portable, wireless, and/or Internet connected radar, digital video, and/or infrared sensors. The prominent research problems associated with multisensor integration for wide-area surveillance are modality selection, sensor planning, data fusion, and data exchange (communication) among multiple sensors. Thus, the requirements and constraints to be addressed include far-field view, wide coverage, high resolution, cooperative sensors, adaptive sensing modalities, dynamic objects, and uncontrolled environments. This article summarizes a new survey and analysis conducted in light of these challenging requirements and constraints. It involves techniques and strategies from work done in the areas of sensor fusion, sensor networks, smart sensing, Geographic Information Systems (GIS), photogrammetry, and other intelligent systems where finding optimal solutions to the placement and deployment of multimodal sensors covering a wide area is important. While techniques covered in this survey are applicable to many wide-area environments such as traffic monitoring, airport terminal surveillance, parking lot surveillance, etc., our examples will be drawn mainly from such applications as harbor security and long-range face recognition.