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Title: Neural networks for visual feedback control of an industrial robot
Author: Franklin, D. R.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2000
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The majority of industrial robots in use today are configured by on-line programming at the start of each production run. The workpieces are located using precision indexing. The robots have little or no sensory input, other than joint position feedback, and are unable to operate in changing or loosely constrained environments. To overcome these constraints and to increase the range of practical applications, robots need to be able to apply adaptive intelligence to manufacturing operations. This calls for enhanced sensory capabilities. Vision systems have been introduced successfully into many production processes to perform component identification, inspection and location. When introduced into the robot workspace as part of a dynamic visual feedback control scheme they have the potential to reduce the costs associated with precise component fixturing, to compensate for calibration errors, to extend the working life of the robot, to align a robot program developed off-line with the part it is operating on, and to compensate for variations in components. The research presented here used a world-based stereo vision system to control an industrial robot in 3-dimensional space. A visual tracking algorithm was developed to follow the robot end-effector. Iterative and dynamic visual feedback control strategies were investigated. To achieve this it was necessary to translate between the visually observed position of the robot end-effector and its position in the workspace. The bulk of the experimental work was devoted to techniques for achieving this. Methods based on an affine stereo algorithm, a geometric perspective stereo algorithm, and a neural gas network were investigated. The neural gas network is an artificial neural network algorithm that uses a rapid interpolative training scheme. The network was used to implement either an image to robot joint space mapping or an image to Cartesian space mapping. The neural network algorithm had no prior knowledge of the positions of the cameras or the kinematics of the robot, but instead learned the mapping by making a series of trial movements and by updating the network weights based on the results. A number of different training scheme variations were investigated and optimised. The most accurate mapping algorithms were used to implement a dynamic dual loop visual control system. The resulting system was capable of driving the end-effector along a visually defined path. The system was able to tolerate a degree of robot miscalibration as well as serious image to robot miscalibration.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available