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Abstract
The first part of this thesis documents experimental investigation into the use of vision for wheeled robot navigation problems. Specifically, using a video camera as a source of feedback to control a wheeled robot toward a static and a moving object in an environment in real-time. The wheeled robot control algorithms are dependent on information from a vision system and an estimator.
The vision system design consists of a pan video camera and a visual gaze algorithm which attempts to search and continuously maintain an object of interest within limited camera field of view. Several vision-based algorithms are presented to recognize simple objects of interest in an environment and to calculate relevant parameters required by the control algorithms. An estimator is designed for state estimation of the motion of an object using visual measurements. The estimator uses noisy measurements of relative bearing to an object and object's size on an image plane formed by perspective projection. These measurements can be obtained from the vision system.
A set of algorithms have been designed and experimentally investigated using a pan video camera and two wheeled robots in real-time in a laboratory setting. Experimental results and discussion are presented on the performance of the vision-based control algorithms where a wheeled robot successfully approached an object in various motions.
The second part of this thesis investigates the coordination problem of flocking in multi-robot system using concepts from graph theory. New control laws are presented for flocking motion of groups of mobile robots based on several leaders. Simulation results are provided to illustrate the control laws and its applications.