Deep Neural Network Enhanced Sampling-Based Path Planning in 3D Space

Robot path planning in 3D space is a challenging problem for its complex configuration. Sampling-based algorithms have gained great success in solving path planning problems in 3D space, but the quality of the initial path is not guaranteed and the convergence to the optimal solution is slow. To add...

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Veröffentlicht in:	IEEE transactions on automation science and engineering Jg. 19; H. 4; S. 3434 - 3443
Hauptverfasser:	Wang, Jiankun, Jia, Xiao, Zhang, Tianyi, Ma, Nachuan, Meng, Max Q.-H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial neural networks Collision avoidance Collision dynamics Deep learning deep neural network Machine learning Mathematical models Neural networks Path planning Performance evaluation Planning Prediction algorithms Robot path planning Robots Sampling sampling-based algorithm Space robots Three-dimensional displays
ISSN:	1545-5955, 1558-3783
Online-Zugang:	Volltext
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