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Fast Trajectory Generation and Asteroid Sequence Selection in Multispacecraft for Multiasteroid Exploration.

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    This summary is machine-generated.

    This study introduces a fast Monte Carlo tree search (MCTS) pruning algorithm for selecting asteroid detection sequences and optimizing spacecraft trajectories. The method significantly improves efficiency for multi-spacecraft asteroid exploration missions.

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    Area of Science:

    • Aerospace Engineering
    • Astrodynamics
    • Computational Science

    Background:

    • Increasing asteroid discoveries necessitate efficient detection strategies.
    • Limited propulsion and time pose challenges for multi-asteroid exploration.
    • Traditional trajectory optimization methods are computationally intensive and slow.

    Purpose of the Study:

    • To develop a fast asteroid sequence selection and trajectory generation method for multi-spacecraft missions.
    • To improve the computational efficiency of asteroid detection trajectory optimization.
    • To enable rapid exploration of multiple asteroids with limited resources.

    Main Methods:

    • Integration of Monte Carlo Tree Search (MCTS) with spacecraft trajectory optimization.
    • Development of a fast MCTS pruning algorithm for sequence selection and trajectory generation.
    • Application of the Bezier shape-based (SB) method combined with MCTS for efficient trajectory optimization.

    Main Results:

    • The MCTS pruning algorithm rapidly identified the globally optimal detection sequence.
    • The Bezier SB method achieved faster and better-performing transfer trajectories compared to the finite Fourier series SB method.
    • The proposed method demonstrated effective performance in a complex multi-flyby asteroid mission design.

    Conclusions:

    • The combined MCTS and Bezier SB approach offers a significant advancement in multi-asteroid exploration mission design.
    • This method provides a computationally efficient solution for rapid asteroid sequence selection and trajectory optimization.
    • The study validates the effectiveness of the proposed approach for complex space exploration scenarios.