Abstract
Industrial modifications of Delta-like robots impose high challenges on the dimensional synthesis. On the one hand, purely kinematic approaches taking into account the input and output transmission capabilities may be sufficient to optimize the design of a basic Delta robot in the field of high-speed application. On the other hand, a combined kinematic and dynamic optimization approach can be employed to additionally include the actuation torques as highly important parameters for the motor selection process. More importantly, taking into account dynamics optimization and evaluation criteria (such as energy consumption and peak power of a system) meets the growing demands for energy-efficient and sustainable manufacturing. Such combined approaches may be particularly advantageous when it comes to additional moving masses as observed in recently modified industrial variants. In this context, efficient kinematic models are introduced establishing the input and output transmission indices based on the notion of pressure angles. The problem of increased computational complexity of dynamic modelling approaches for functionally extended Delta-like robots is tackled by employing an efficient approach based on the Lagrange-d’Alembert Principle of Virtual Work. With these models, sets of Pareto optimal candidates are identified by multi-objective optimization techniques. The most efficient candidates resulting from both approaches are selected and compared against one and another. It is shown that the combined approach helps to further improve the design of the extended variants whereas superior candidates for the light-weight basic Delta robots can already be obtained using kinematic performance indices only.
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This work is supported by the German Academic Exchange Service (DAAD) with funds from the Federal Foreign Office (FFO).
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Brinker, J., Corves, B., Takeda, Y. (2019). Kinematic and Dynamic Dimensional Synthesis of Extended Delta Parallel Robots. In: (Chunhui) Yang, R., Takeda, Y., Zhang, C., Fang, G. (eds) Robotics and Mechatronics. ISRM 2017. Mechanisms and Machine Science, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-17677-8_11
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