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Space drones require autonomy for operations. This study validates autonomous center of gravity auto-location using nonlinear state estimation, achieving rapid, accurate mass property determination in space experiments.

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adaptiveauto-locationautonomycenter of gravitycontrolestimationexperimental validationinertia identificationnonlinear Luenberger observersspace drone

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Area of Science:

  • Space exploration and robotics
  • Autonomous systems engineering
  • Aerospace dynamics

Background:

  • Space drone operations necessitate advanced autonomy for tasks like navigation and control.
  • Accurate real-time determination of mass properties is crucial for stable and efficient space missions.

Purpose of the Study:

  • To experimentally evaluate a novel autonomous method for center of gravity auto-location in space.
  • To validate the efficacy of nonlinear state estimation for identifying mass properties.

Main Methods:

  • Utilized nonlinear, coupled governing kinetics as the control strategy.
  • Employed inversion techniques to derive closed-form estimates of mass properties.
  • Applied the parallel axis theorem to parameterize mass center coordinates using inertia cross-products.

Main Results:

  • Experimental validation in space demonstrated the feasibility of the proposed autonomous approach.
  • The study identified a symmetric longitudinal mass distribution and an off-balance lateral distribution.
  • Achieved hundreds of millimeters convergence in minutes, significantly outperforming the state-of-the-art benchmark's millimeter convergence over hundreds of days.

Conclusions:

  • The developed method provides a highly efficient and accurate solution for autonomous center of gravity determination in space.
  • This advancement enhances the autonomy and operational capabilities of space drones.