There is a significant amount of ongoing research on developing multi-agent algorithms for mobile robots. Moving those algorithms beyond simulation and into the real world requires multi-robot testbeds. However, there is currently no easily accessible source of information for guiding the creation of such a testbed. In this thesis, we describe the process of creating a testbed at Purdue University involving a set of unmanned aerial vehicles (UAVs). We discuss the components of the testbed, including the software that is used to interface with the UAVs. We also describe the challenges that we faced during the setup process, and evaluate the UAV platforms that we are using. Finally, we demonstrate the implementation of a multi-agent task allocation algorithm on our testbed.

Autonomous Unmanned Aerial Vehicles (UAVs) in agriculture are increasingly in demand to reduce cost, labour and increase effectiveness and quality in farming. However, it is necessary to improve reliability for this technology to perform its full potential without harming humans, animals or the environment. The reliability increases by identifying the hazards and mitigating them. Therefore the risks are identified, analyzed and mitigated using analysis tools. Two different methods are used to analyze and reduce hazards, and each method utilizes various analysis tools. In addition, redundancy and preventive action are proposed to eliminate or minimize the danger. This thesis identifies risks by studying and reviewing a generic use-case from the AFarCloud project and compares the two hazard analysis methods to determine which method provides the most reliable result.

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It is a well-recognized fact that unmanned aerial vehicles are an essential element in today’s network-centric integrated battlefield environment. Compared to solo UAV missions, multiple unmanned aerial vehicles deployed in co-operative mode, offer many advantages that has motivated UAV researchers all over the world to evolve concept of operations that aims in achieving a paradigm shift from traditional ”dull” missions to perform ”dirty” and ”dangerous” missions. In future success of a mission will depend on interaction among UAV groups with no interaction with any ground entity. To reach this capability level, it is necessary for researchers, to first understand the various levels of autonomy and the crucial role that information and communication plays in making these autonomy levels possible. The thesis is in four parts: (i) Development of an organized framework to realize the goal of achieving fully autonomous systems. (ii) Design of UAV grouping algorithm and coordination tactics for ground attack missions. (iii) Cooperative network management in GPS denied environments. (iv) UAV group tactical path and goal re-plan in GPS denied wide area urban environments. This research thesis represents many first steps taken in the study of autonomous UAV systems and in particular group autonomy. An organized framework for autonomous mission control level by defining various sublevels, classifying the existing solutions and highlighting the various research opportunities available at each level is discussed. Significant contribution to group autonomy research, by providing first of its kind solution for UAV grouping based on Dubins’ path, establishing GPS protected wireless network capable of operating in GPS denied environment and demonstration of group tactical path and goal re-plan in a layered persistent ISR mission is presented. Algorithms discussed in this thesis are generic in nature and can be applied to higher autonomous mission control levels, involving strategic decisions among UAVs, satellites and ground forces in a network centric environment.