Forming 2D3D Operator Control for Ambiguous Image Environments

Author(s):  
Bipul Islam ◽  
Ji Liu ◽  
Anthony Yezzi ◽  
Romeil Sandhu
Keyword(s):  
1980 ◽  
Author(s):  
Harold F. Engler ◽  
Esther L. Davenport ◽  
Joanne Green ◽  
William E. Sears

Author(s):  
Steven B. Shooter ◽  
Charles F. Reinholtz

Abstract Portable manipulators are installed for operation and then removed upon completion of their task. Typical applications of portable manipulators include the inspection of nuclear reactors, inspection and repair of nuclear steam generators and asbestos removal in buildings. In such operations, it is difficult to precisely position the manipulator at a fixed location within its workplace, yet this is critical for accurate tool positioning. It can be possible, however, to position the tool tip at several points in the environment using video feedback and manual operator control of the manipulator. This provides sufficient information to determine the position and orientation of the manipulator base frame with respect to the environment, hereafter referred to as extrinsic calibration. Following extrinsic calibration, subsequent moves of the manipulator can be automated. This paper describes a closed-form method for performing extrinsic calibration by contacting the tool to a total of six places on three orthogonal plane surfaces of reference.


Author(s):  
M. P. Kukhtik ◽  
A. I. Repnikov ◽  
Yu. P. Serdobintsev ◽  
M. A. Khaustov

Block diagram and operation principle for automated system of prevention of emergency situations in a sliding support of a gas pumping unit have been developed. Control program has been written and HMI-interface of operator control panel has been developed.


Genetic algorithms (GAs) are heuristic, blind (i.e., black box-based) search techniques. The internal working of GAs is complex and is opaque for the general practitioner. GAs are a set of interconnected procedures that consist of complex interconnected activity among parameters. When a naive GA practitioner tries to implement GA code, the first question that comes into the mind is what are the value of GA control parameters (i.e., various operators such as crossover probability, mutation probability, population size, number of generations, etc. will be set to run a GA code)? This chapter clears all the complexities about the internal interconnected working of GA control parameters. GA can have many variations in its implementation (i.e., mutation alone-based GA, crossover alone-based GA, GA with combination of mutation and crossover, etc.). In this chapter, the authors discuss how variation in GA control parameter settings affects the solution quality.


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