Research on 3D information collection path planning for hyper-redundant space robots (HSRs)
-
Published:2024-09-05
Issue:2
Volume:15
Page:531-539
-
ISSN:2191-916X
-
Container-title:Mechanical Sciences
-
language:en
-
Short-container-title:Mech. Sci.
Author:
Qin Guodong, Zhang Haoran, Zheng Lei, Liu Shijie, Chen Quan, Hu Haimin, Zhang Deyang, Cheng Yong, Zuo Congju, Ji AihongORCID
Abstract
Abstract. This paper proposes a path-planning method for 3D information collection on the space station surface via the hyper-redundant space robot (HSR). Firstly, to efficiently acquire information on the space station surface, the space station is reduced to a cylindrical model for modelling, and the initial mapping of the temperature field is carried out by a popular Gaussian process. Based on the active information collection method, the collision-free viewpoint trajectory of the space station surface can be planned to improve the efficiency of surface information collection. Then, the path planning of the space station surface information collection can be realized by importing the space station model and temperature field data and performing weight initialization, stochastic search, and continuous optimization. Finally, simulation experiments show that the root-mean-square errors in the surface information collection process are lower than 1 mm relative to the true value. It proves the effectiveness of the online information collection path-planning (IP) method.
Funder
National Natural Science Foundation of China Postdoctoral Research Foundation of China National Magnetic Confinement Fusion Program of China
Publisher
Copernicus GmbH
Reference32 articles.
1. Benzaoui, M., Chekireb, H., Tadjine, M., and Boulkroune, A.: Trajectory Tracking with Obstacle Avoidance of Redundant Manipulator Based on Fuzzy Inference Systems, Neurocomputing, 196, 23–30, https://doi.org/10.1016/j.neucom.2016.02.037, 2016. 2. Bircher, A., Kamel, M., Alexis, K., Burri, M., Oettershagen, P., Omari, S., and Siegwart, T.: Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots, Auton. Robot., 40, 1059–1078, https://doi.org/10.1007/s10514-015-9517-1, 2016. 3. Braganza, D., Dawson, D., Walker, I., and Nath, N.: A Neural Network Controller for Continuum Robots, IEEE T. Robot., 23, 1270–1277, https://doi.org/10.1109/TRO.2007.906248, 2007. 4. Del Castillo, E., Colosimo, B., and Tajbakhsh, S.: Geodesic Gaussian processes for the parametric reconstruction of a free-form surface, Technometrics, 57, 87–99, https://doi.org/10.1080/00401706.2013.879075, 2015. 5. Dong, H., Li, C., Wu, W., Yao, L., and Sun, H.: A novel algorithm by combining nonlinear workspace partition with neural networks for solving the inverse kinematics problem of redundant manipulators, Mech. Sci., 12, 259–267, https://doi.org/10.5194/ms-12-259-2021, 2021.
|
|