New Motion Planning and Real-Time Localization Methods Using Proximity for Autonomous Mobile Robots.

One of the most difficult theoretical problems in robotics--motion planning for rigid body robots-- must be solved before a robot can perform real-world tasks such as mine searching and processing. This dissertation proposes a new motion planning algorithm for an autonomous robot, as well as the method and results of implementing this algorithm on a real vehicle. This dissertation addresses the problem of safely navigating an autonomous vehicle through free space of a two dimensional, world model with polygonal obstacles from a start configuration position orientation to a goal configuration using smooth motion under the structure of a layered motion planning approach. The approach proposes several new concepts, including v-edges and directed v-edges, and divides the motion planning problem of a rigid body vehicle into two subproblems 1 finding a global path using Voronoi diagrams and for a given start and goal configurations planning an optimal global path the planned path is a sequence of directed v-edges, 2 planning a local motion from the start configuration, using the aforementioned global path. The problem of how to design a safe and smooth path, is effectively solved by the steering function method and proximity. Another problem addressed here is how to make a smooth transition when the vehicle gets closer to an intersection of two distinct boundaries. This dissertation also presents a robust algorithm for the vehicle to continually eliminate its positional uncertainty while executing missions. This functionality is called self-localization which is an essential component of model-based navigation for indoor applications. This algorithm is based on the two-dimensional transformation group. Through this method, the robot can minimize its positional uncertainty, make safe and reliable motions, and perform useful tasks in a partially known world.