Design and analysis of a coding and classification system for a systematic interactive computer-aided robot selection procedure (CARSP)
Offodile, Onyebuchi Felix
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A coding and classification system (ROBOCODE) was developed for robots, and used to model a computer based robot selection algorithm. Forty attributes were used to design a taxonomic system for robots and provided a fast and easy standard basis for comparing robots. The system is semi-polycode structure and was readily computerized for easy storage and retrieval of information on robots. The goal of the ROBOCODE system was a user oriented computer-aided robot selection procedure (CARSP). The CARS system software is interactive, and its design showed that the ease with which the vast amount of data on robots, and the number of robots, could be handled was limited primarily by the disc storage space of the computer rather than the computer memory. Coding and classification was found to augment this storage space by a factor of about ten. The same coding system was used to code the tasks the robot was to perform in order to establish an effective matching procedure between the task and the robot. For some task variables that could not be matched directly, a indirect matching procedure was developed. A set of cost equations was developed and used to measure the performance of the robots under shop conditions The necessary condition for selecting a robot was that the codes for the robot be as good as or superior to the corresponding task codes, and the sufficient condition was that the robot had a total minimum operating cost. The robot selection model was evaluated using a statistical procedure to investigate the stability of the model selecting the cost effective robot. Experimental result; showed that the model was fairly stable in selecting this cost effective robot based on the robots' first period total operating cost. For a robot selection problem in which one machine was used to perform one type of task on one type of product (1/1/1), the cost effective robot was selected 67' of the time, and 54% of the time for the robot selection problem in which one machine was used to perform one type of task on four different types of products (1/1/4).