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A Study on Kinematics and Workspace Determination of a General 6-P US Robot

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Abstract

The 6-UP S parallel manipulator known as Stewart platform is the most common type of 6-DOF parallel robots. Most industrial applications such as the motion simulators and applications requiring high load and high workspace use this structure. However, the 6-UP S has its disadvantages as the actuated joint is located in the second place in the kinematic chain. The 6-P US is another 6-DOF structure and when compared with the 6-UP S, offers several advantages by allowing to mount the heavy and the vibration inducing actuated prismatic joint on the ground. This offers the added benefit of transferring the majority of the payload to the ground, resulting in lowering of the overall costs. The question is: Can we find a 6-P US architecture that meets or exceeds the prescribed workspace while meeting the same kinematics footprint requirement of a given 6-UP S? In the present paper, a recently constructed 6-UP S mechanism for a metro station is selected and using an optimization method based on a genetic algorithm (GA), a 6-P US mechanism that meets or exceeds the required workspace is identified. To do this, first, the various architectures of the 6-P US parallel robot are presented. The inverse kinematics solution for a general 6-P US architecture is obtained. To fully define the architecture, three additional kinematics parameters are selected and employed to define the cost function used for the optimization. The cost function acts as a penalizing mechanism which ignores unwanted architectures of the 6-P US. Finally, a new concept for 6-DOF workspace visualization representation, called workspace spheres, is presented. The new concept aids the user by offering quantitatively data and visualization tool for simultaneous description of rotational and translational workspace.

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Correspondence to Alireza Akbarzadeh.

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Nabavi, S.N., Akbarzadeh, A. & Enferadi, J. A Study on Kinematics and Workspace Determination of a General 6-P US Robot. J Intell Robot Syst 91, 351–362 (2018). https://doi.org/10.1007/s10846-017-0704-5

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  • DOI: https://doi.org/10.1007/s10846-017-0704-5

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