Climbing Strategy of Variable Topology Cellular Space Robots Considering Configuration Optimization

Xiaomeng Liu, Bindi You, Rui Wang (), Jianmin Wen (), Dexiao An and Xiaolei Wen
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Xiaomeng Liu: School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
Bindi You: School of Ocean Engineering, Harbin Institute of Technology, Weihai 264209, China
Rui Wang: School of Ocean Engineering, Harbin Institute of Technology, Weihai 264209, China
Jianmin Wen: School of Ocean Engineering, Harbin Institute of Technology, Weihai 264209, China
Dexiao An: School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
Xiaolei Wen: School of Ocean Engineering, Harbin Institute of Technology, Weihai 264209, China

Mathematics, 2023, vol. 11, issue 6, 1-18

Abstract: In optimizing the energy consumption of a cellular space robot (CSR) climbing in orbit, traditional trajectory planning methods do not include the configuration as a decision variable; thus, it is difficult to obtain an effective energy-optimal climbing strategy for CSRs. To solve this kind of problem, first, we considered the effect of configuration on the kinetic parameters of the robot and established the kinetic equations of a variable-size cellular space robot by combining the spin volume and Lagrange equations. Second, we constructed a higher-order continuous joint trajectory using a cubic spline curve and combined it with a robot dynamics model to establish a robot climbing energy consumption model. Finally, for the energy consumption problem of climbing in cellular space robots, we proposed a comprehensive optimization algorithm considering the configuration and climbing time. Compared with another algorithm in the literature, the algorithm proposed in this paper has stronger global optimization capability and convergence. The simulation results have important theoretical significance and engineering practical value for the study of space robot on-orbit climbing trajectory optimization strategies.

Keywords: self-reconfiguring robot; trajectory planning; energy-optimal; optimization strategies (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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