Generative design for self-balancing unicycle robot in additive manufacturing

Jiaqi Chen; Qilong Cheng; Mengrui Han

doi:10.1117/12.2639454

27 June 2022 Generative design for self-balancing unicycle robot in additive manufacturing

Jiaqi Chen, Qilong Cheng, Mengrui Han

Proceedings Volume 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022); 122530A (2022) https://doi.org/10.1117/12.2639454
Event: Second International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 2022, Qingdao, China

Abstract

Chassis structures have a fundamental influence on the stability of self-balancing unicycles. This paper documents how using generative design can improve the robotic system’s stability and reduce the overall mass compared to the conventional designs. The chassis structure is divided into seven parts for stress analysis. According to the results from the stress analysis, loading conditions and constraints can be set for the generative studies. The ideal solutions were chosen based on the mass-safety factor graphs for the most optimized geometry. Stress analysis was then carried out on Inventor to verify the model’s reliability. To test the controllability of the generative unicycle, simulations were carried out in Simulink by comparing the generative design with the conventional design. The results showed that the generative chassis structure has a higher center of mass and lower mass, hence is easier to achieve balancing from an angular deviation.

Citation Download Citation

Jiaqi Chen, Qilong Cheng, and Mengrui Han "Generative design for self-balancing unicycle robot in additive manufacturing", Proc. SPIE 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 122530A (27 June 2022); https://doi.org/10.1117/12.2639454

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