System architectures and environment modeling for high-speed autonomous navigation
Successful high-speed autonomous navigation requires integration of tools from robotics, control theory, computer vision, and systems engineering. This thesis presents work that develops and combines these tools in the context of navigating desert terrain.
A comparative analysis of reactive, behavior-based, and deliberative control architectures provides important guidelines for design of robotic systems. These guidelines depend on the particular task and environment of the vehicle. Two important factors are identified which guide an effective choice between these architectures: dynamic feasibility for the vehicle, and predictability of the environment. This is demonstrated by parallels to control theory, illustrative examples, simulations, and analysis of Bob and Alice---Caltech's full-scale autonomous ground vehicle entries in the 2004 and 2005 Grand Challenge races, respectively.
Further, new model-based methods are developed for constructing and maintaining estimates of terrain elevation and road geometry. These are demonstrated in simulation and in fully autonomous operation of Alice, including accurate detection and tracking of the centerline of desert roads at speeds up to 5 m/s. Finally, Alice's navigation architecture is presented in full along with experimental results that demonstrate its capabilities.
0800: Artificial intelligence