Future exploration tasks of small bodies will need to sample or visit multiple points on the target to obtain more scientific returns, requiring rovers to have the ability to hop on a small body surface. This paper proposes an approach to generate a desensitized optimal trajectory for hopping rovers, aiming at reducing the sensitivity of hopping trajectory in the presence of uncertainties. Firstly, considering parameter uncertainties and initial state errors, analytical expressions of optimal initial states are derived on a planar scene, based on ballistic dynamics. Then, similar methods are developed in both uphill and downhill cases of inclined scenes. Subsequently, the desensitization performance of long-distance hopping trajectory is analyzed under single-hop, identical, and non-identical N-hop strategies. To facilitate the application of the proposed analytical solution to the simulated surface environment of small bodies, a prediction-correction procedure is presented. Finally, Monte Carlo simulations are carried out to verify the effectiveness of the proposed methods. The results indicate that the sensitivity of the hopping trajectory to uncertainties can be effectively diminished by employing the desensitized optimal trajectory and multiple hopping strategy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Desensitized optimal trajectory for hopping rovers on small bodies


    Contributors:


    Publication date :

    2024-09-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Stochastic Motion Planning for Hopping Rovers on Small Solar System Bodies

    Hockman, Benjamin / Pavone, Marco | British Library Conference Proceedings | 2017


    Desensitized Optimal Trajectories

    Seywald, H. / Kumar, R. R. / American Astronautical Society et al. | British Library Conference Proceedings | 1996


    Desensitized Optimal Control

    Seywald, Hans / Seywald, Kevin L. | AIAA | 2024


    Desensitized Optimal Filtering

    Karlgaard, C. / Shen, H. / American Institute of Aeronautics and Astronautics; Conferderation of European Aerospace Societies | British Library Conference Proceedings | 2011