Statistical procedures for task assignment and robot selection in assembly cells

doi:10.1080/095119200129957

Statistical procedures for task assignment and robot selection in assembly cells

2000-01-01

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DOI：10.1080/095119200129957

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The put-pose of this paper is to show how statistical procedures can be used to design robotic assembly cells. The proposed methodology has two stages. In the first stage, a fuzzy clustering algorithm is employed to group similar tasks together so that they can be assigned to robots while maintaining a balanced cell and achieving a desired production cycle time. In the second stage, a Mahalanobis distance procedure is used to select robots appropriate for the task groups. The proposed approach recognizes and exploits the flexibility of robots. It also recognizes that the manufacturer specifications of robots do not hold simultaneously under normal operating conditions. A numerical example is presented and a small experiment is conducted to test die procedures.

Keyword:

COOPERATING ROBOTS

INDUSTRIAL ROBOTS

REPEATABILITY

FRAMEWORK

MULTIPLE

SYSTEMS

MODEL

Statistical procedures for task assignment and robot selection in assembly cells

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