Autonomous navigation of mobile robots through unstructured terrain presents many challenges. The task becomes even more difficult with increasing obstacle density, at higher speeds, and when a priori knowledge of the terrain is not available. Reactive navigation schemas are often dismissed as overly simplistic or considered to be inferior to deliberative approaches for off-road navigation. The Potential Field algorithm has been a popular reactive approach for low speed, highly maneuverable mobile robots. However, as vehicle speeds increase, Potential Fields becomes less effective at avoiding obstacles. The traditional shortcomings of the Potential Field approach can be largely overcome by using dynamically expanding perception zones to help track objects of immediate interest. This newly developed technique is hereafter referred to as the Dynamic Expanding Zones (DEZ) algorithm. In this approach, the Potential Field algorithm is used for waypoint navigation and the DEZ algorithm is used for obstacle avoidance. This combination of methods facilitates high-speed navigation in obstacle-rich environments at a fraction of the computational cost and complexity of deliberative methods. The DEZ reactive navigation algorithm is believed to represent a fundamental contribution to the body of knowledge in the area of high-speed reactive navigation. This method was implemented on the Virginia Tech DARPA Grand Challenge vehicles. The results of this implementation are presented as a case study to demonstrate the efficacy of the newly developed DEZ approach. / Master of Science

Micro-unmanned aerial rotorcraft are quickly gaining acceptance as indoor platforms for performing stealth, surveillance, and rescue and reconnaissance missions. These rotorcraft are generally required to operate in cluttered, unknown, and dynamic GPS-denied environments, which present threats to the safe operation of the vehicle. To overcome these environmental challenges, we describe a system that is capable of localizing itself by producing accurate odometry estimates that can detect and track moving objects and avoid collisions with obstacles while following a moving target using a laser range finder. Our system has been implemented in the Simulink environment in MATLAB. Various simulations have shown our methods to work well, even in the presence of sensor noise and out-of-plane motion. Our system is capable of localizing itself within ±20 mm in North and East and ±0.5 degrees in ψ while detecting and tracking