Master of Science, The Ohio State University, 2008, Electrical and Computer Engineering

This thesis presents an obstacle avoidance strategy designed to meet the demands of the 2007 Defense Advanced Research Projects Agency (DARPA) Urban Challenge. The central theme of this work is the combination of the reactive and the global motion planning techniques to achieve an efficient and robust obstacle avoidance module (OAM). The aim of the OAM is to navigate the autonomous ground vehicle (AGV) robustly and without any collisions in an outdoor urban environment using the local sensory information. The OAM attempts to alleviate the problems faced by purely reactive approaches and provides an obstacle-free navigation in unknown and complicated environments. The motion commands generated by the OAM are: a smooth collision-free path constructed using the Cubic Bezier Polynomials and a longitudinal speed at which the path needs be followed. Furthermore, the OAM is enhanced by the capabilities which allow the AGV to navigate safely among the moving traffic. The proposed OAM was tested in simulation extensively to validate its capabilities and to improve its overall performance. A number of challenging outdoor obstacle fields were constructed to test the functionalities of the OAM. The OAM was successfully integrated on the Ohio State University Autonomous City Transport (OSU-ACT) vehicle which competed in the 2007 DARPA Urban Challenge. Finally, the OAM demonstrated its reliability and the robustness by completing all the given tasks successfully in the National Qualification Event (NQE) of the Urban Challenge. The main focus of this thesis is on the overall algorithm structure and the demonstration of its capability using simulation and the experimental results. Furthermore, the challenges associated with the motion planning of nonholonomic robots, with sensor limitations and the external disturbances are discussed.

Committee: Ümit Özgüner (Advisor); David Orin (Committee Member) Subjects: Electrical Engineering; Engineering

Master of Sciences (Engineering), Case Western Reserve University, 2008, EECS - Computer Engineering

This thesis explores the use of machine learning in the context of autonomous mobile robots driving on roads, with the focus on improving the robot's internal map. Early chapters cover the mapping efforts of DEXTER, Team Case's entry in the 2007 DARPA Urban Challenge. Competent driving may include the use of a priori information, such as road maps, and online sensory information, including vehicle position and orientation estimates in absolute coordinates as well as error coordinates relative to a sensed road. An algorithm may select the best of these typically flawed sources, or more robustly, use all flawed sources to improve an uncertain world map, both globally in terms of registration corrections and locally in terms of improving knowledge of obscured roads. It is shown how unsupervised learning can be used to train recognition of sensor credibility in a manner applicable to optimal data fusion.

Committee: Wyatt Newman PhD (Advisor); M. Cenk Cavusoglu PhD (Committee Member); Francis Merat PhD (Committee Member) Subjects: Computer Science; Engineering; Robots

Master of Sciences, Case Western Reserve University, 2008, Computer Engineering

This thesis describes the lane detection system developed for the autonomous robot DEXTER in the 2007 DARPA Urban Challenge. Though DEXTER was capable of navigating purely off of GPS signals, it often needed to drive in areas where GPS navigation could not be trusted completely. In these areas it was necessary to use a method of automatically detecting the lane of travel so that DEXTER could drive properly within it. The developed system functions by merging the outputs of a number of independent road detection modules coming from several sensors into a single drivable output path. This sensor derived path is compared with the map derived path in order to produce an optimal output based on the relative confidences of the two information sources. The full lane detection system is able to adaptively drive according to the best information source and perform well in a variety of diverse driving environments.

Committee: Wyatt Newman (Advisor) Subjects: