Abstract
In the last decades, the Robot Selection Problem (RSP) has been widely investigated, and the importance of properly structuring the decision problem has been stated. Crucial aspect in this process is the correct identification of the robot attributes, which should be limited in number as much as possible, but should be also able to detect at best the peculiar requirements of specific applications. Literature describes several attributes examples, but mainly dedicated to traditional industrial tasks, and applied to the selection of conventional industrial robots. After a synthetic review of the robot attributes depicted in the RSP literature, presented with a custom taxonomy, this paper proposes a set of possible requirements for the selection problem of small scale parallel kinematic machines (PKMs). The RSP is based on a task-driven approach: two mini-manipulators are compared as equivalent linear actuators to be integrated within a more complex system, for the application in both an industrial and a biomedical environment. The set of identified criteria for the two environments is proposed in the results and investigated with respect to working conditions and context in the discussion, emphasizing limits and strength points of this approach; finally, the conclusions synthesizes the main results.
Highlights
Scientific research on the Robot Selection Problem (RSP) in industrial applications has widely evolved in the last years
After a synthetic review of the robot attributes depicted in the RSP literature, presented with a custom taxonomy, this paper proposes a set of possible requirements for the selection problem of small scale parallel kinematic machines (PKMs)
The RSP is based on a task-driven approach: two mini-manipulators are compared as equivalent linear actuators to be integrated within a more complex system, for the application in both an industrial and a biomedical environment
Summary
Scientific research on the Robot Selection Problem (RSP) in industrial applications has widely evolved in the last years. Since the first works in the end of the seventies, the number of robotic devices available on the market for different purposes has grown, and the number of methods to support the decision maker in the selection process among devices has increased [1,2,3,4]. Those methods basically represent objective and repeatable strategies for ranking some attributes of the different solutions, expression of robot performance characteristics or economic evaluations. Production system performance optimization models are suitable for single product production systems, like in assembly and machine loading applications, since they can handle robot engineering attributes and product design specifications; as a drawback, these models are less flexible than those in MCDM
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