Abstract In this paper, a kinodynamic inspection path planner, called Random Kinodynamic Inspection Tree algorithm, is presented to perform in-close proximity full-coverage on-orbit inspection for the space structures, under kinematic/dynamic motion constraints in deep space environments. Inspection, as one of the key elements of proximity operations, would make a significant breakthrough in on-orbit servicing and, therefore, space exploration missions. The presented approach combines a novel coverage planning scheme with a kinodynamic motion planner to quickly and effectively solve the inspection path planning problem while at the same time handling both holonomic and non-holonomic constraints of the environment and the inspector robot. Our planner avoids the previous decoupled two steps method, which usually has been used to solve coverage planning problems, namely the Art Gallery and Traveling salesman problems, which are difficult or infeasible to be applied to a robot with differential constraints working in a high-dimensional environment. By contrast, our planner employs the Rapidly Exploring Random Tree algorithm as an asymptotically-optimal sampling-based technique in cooperation with a Linear Quadratic Minimum Time controller to generate an optimal and smooth inspection trajectory for any space structures, given complete knowledge of the structure and inspector’s dynamics. The algorithm guarantees probabilistic completeness. Simulations are provided as a validation of the achieved inspection performance.

    Highlights Kinodynamic inspection path planning for space applications is studied. Three-dimensional kinodynamic coverage path planner is designed. Full coverage inspection trajectories are developed for three different geometrical shapes. Our results can be applied to space exploration missions in the future.


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    Title :

    Kinodynamic on-orbit inspection path planning for full-coverage inspection in close proximity of space structures


    Contributors:

    Published in:

    Acta Astronautica ; 198 ; 354-365


    Publication date :

    2022-04-30


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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