MS, University of Cincinnati, 2002, Engineering : Industrial Engineering

Obstacle avoidance is one of the most critical factors in the design of autonomous vehicles such as mobile robots. One of the major challenges in designing intelligent vehicles capable of autonomous travel on highways is reliable obstacle avoidance. Obstacle avoidance may be divided into two parts, obstacle detection and avoidance control. Numerous methods for obstacle avoidance have been suggested and research in this area of robotics is done extensively. Three different methods for obstacle detection and avoidance are available on the BEARCAT III. These include fixed mounting of sonar sensors, a rotating sonar sensor and a laser scanner. The fixed mounting system uses two sonar sensors which are mounted at the outer front edges of the vehicle. The rotating sonar system consists of a Polaroid ultrasound transducer element mounted on a micro motor with an encoder feedback. The motion of this motor is controlled using a Galil DMC 1000 motion control board. It is possible to obtain range readings at known angles with respect to the center of the robot. The laser range scanner system consists of a SICK Optics laser scanner which returns a two dimensional profile of the horizontal region in front of the vehicle. The data from these systems can be used to detect and avoid obstacles. The systems were tested in July 2002 at the International Ground Robotics Competition. The BEARCAT III placed third in the autonomous challenge contest. This test bed system provides experimental evaluation of the tradeoffs among the systems in terms of resolution, range and computation speed as well as mounting arrangements. The significance of this work is in the increased understanding of obstacle avoidance for robot control and the applications of autonomous guided vehicle technology for industry, defense and medicine.

Committee: Dr. Ernest L. Hall (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2012, Geodetic Science and Surveying

The ability of living creatures to navigate their environment is one of the great mysteries of life. Humans, even from an early age, can acquire data about their surroundings, determine whether objects are movable or fixed, and identify open space, separate static and non-static objects, and move towards another location with minimal effort, in infinitesimal time spans. Over extended time periods humans can recall the location of objects and duplicate navigation tasks based purely on relative positioning of landmarks. Our ability to emulate this complex process in autonomous vehicles remains incomplete, despite significant research efforts over the past half century. Autonomous vehicles rely on a variety of electronic sensors to acquire data about their environment; the challenge is to transform that data into information supporting the objective of navigation. Historically, much of the sensor data was limited to the two dimensional (2D) instance; recent technological developments such as Laser Ranging and 3D Sonar are extending data collection to full three dimensional (3D) acquisition. The objective of this dissertation is the development of an algorithm to support the transformation of 3D ranging data into a navigation solution within unknown environments, and in the presence of dynamically moving objects. The algorithm reflects one of the very first attempts to leverage the 3D ranging technology for the purpose of autonomous navigation, and provides a system which enables the ability to complete the following objectives: • Separation of static and non-static elements in the environment • Navigation based upon the range measurements of static elements This research extends the body of knowledge in three primary topics. 1) The first is the development of a general method to identify n features in an initial data set from m features in a subsequent data set, given that both data sets are acquired via 3D ranging sensors. Accomplishing this objective, particularly (open full item for complete abstract)

Committee: Dorota Grejner-Brzezinska PhD (Advisor); Alper Yilmaz PhD (Committee Member); Ralph von-Frese PhD (Committee Member); Charles Toth PhD (Committee Member) Subjects: Geotechnology