• CN: 11-2187/TH
  • ISSN: 0577-6686

Journal of Mechanical Engineering ›› 2025, Vol. 61 ›› Issue (10): 305-321.doi: 10.3901/JME.2025.10.305

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Classification Optimization and Motion Control of Multi-configuration Unmanned Metamorphic Vehicle

LIU Jun, LIU Hongxun, ZHU Jiawei, CUI Taowen, CHEN Wuwei   

  1. School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009
  • Received:2024-06-08 Revised:2024-12-20 Published:2025-07-12

Abstract: An unmanned metamorphic vehicle is a new type of deformable robot designed by introducing metamorphic mechanism on the basis of vehicle structure, which can adapt to different road conditions for wheeled or legged movement. Its structural parameters and posture parameters have different degrees of influence on the stability and energy consumption of the motions of each configuration. Therefore, the parameters of the unmanned metamorphic vehicle are classification optimized combined with the center-of-mass position-adjusting mechanism. The Sobol sensitivity algorithm is used to develop a classification optimization scheme: for walking stability and energy consumption, the Hummingbird optimization algorithm is used to optimize the design of the leg structural parameters; for the reconfiguration process, a sliding mode controller is designed, and the NSGA-III optimization algorithm is used to optimize the structural parameters, the posture parameters, and the control parameters of the sliding mode controller for the multi-objective integrated optimization on the premise of the vehicle's state-steady steering and driving stability. The simulation and test results showed that the unmanned metamorphic vehicle after classification optimization perform higher reconfiguration stability, lower energy consumption, and better control quality under the reconfiguration condition; in the vehicle state, the steady-state steering stability of the system during driving are significantly improved; in the humanoid state, the energy consumption of the striding process in the walking process is reduced and the walking stability is improved, thus is more conducive to walking. Established hierarchical optimization method for different configurations not only reduces the computational complexity during subsequent optimization, but also obtains the globally optimal parameters applicable to multi-configuration of unmanned metamorphic vehicle.

Key words: unmanned metamorphic vehicle, motion stability and energy consumption, sliding mode control for stability, multi-configuration classification optimization, integrated design

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