The implementation of a heterogeneous multi-agent swarm with autonomous target tracking capabilities
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This thesis details the development of a custom autopilot system designed specifically for multi-agent robotic missions. The project was motivated by the need for a flexible autopilot system architecture that could be easily adapted to a variety of future multi-vehicle experiments. The development efforts can be split into three categories: algorithm and software development, hardware development, and testing and integration. Over 12,000 lines of C++ code were written in this project, resulting in custom flight and ground control software. The flight software was designed to run on a Gumstix Overo Fire(STORM) computer on module (COM) using a Linux Angstrom operating system. The flight software was designed to support the onboard GN&C algorithms. The ground control station and its graphical user interface were developed in the Qt C++ framework. The ground control software has been proven to operate safely during multi-vehicle tests, and will be an asset in future work. Two TSH GAUI 500X quad-rotors and one Gears Educational Systems SMP rover were integrated into an autonomous swarm. Each vehicle used the Gumstix Overo COM. The C-DUS Pilot board was designed as a custom interface circuit board for the Overo COM and its expansion board, the Gumstix Pinto-TH. While the built-in WiFi capability of the Overo COM served as a communication link to a central wireless router, the C-DUS Pilot board allowed for the compact and reliable integration of sensors and actuators. The sensors used in this project were limited to accelerometers, gyroscopes, magnetometers, and GPS. All of the components underwent extensive testing. A series of ground and flight tests were conducted to safely and gradually prove system capabilities. The work presented in this thesis culminated with a successful three-vehicle autonomous demonstration comprised of two quad-rotors executing a standoff tracking trajectory around a moving rover, while simultaneously performing GPS-based collision avoidance.