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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Bibliographic Details
Published in:IEEE transactions on automation science and engineering Vol. 19; no. 4; pp. 3434 - 3443
Main Authors: Wang, Jiankun, Jia, Xiao, Zhang, Tianyi, Ma, Nachuan, Meng, Max Q.-H.
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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 address these problems, in this article, we present a novel sampling-based path planning framework enhanced by the deep neural network (DNN) with applications to 3D space. In the proposed framework, we first train the DNN with a number of successful path planning cases in 3D space. Then the DNN is utilized to predict the promising region where the feasible path probably exists for a given path planning problem. This predicted promising region serves as a nonuniform sampling heuristic to bias the sampling process of the path planner. In this way, the path planner can focus on the promising region in the exploration and exploitation process so that the path planning speed gets accelerated. We conduct numerical simulations to evaluate the performance of the proposed algorithm and the results show that it can perform much better than conventional path planning algorithms. Furthermore, we also investigate the performance of different DNN architectures for path planning in 3D space. Note to Practitioners-In this work, we aim to provide an efficient learning-based method to accelerate the robot path planning process in 3D space. Conventional path planning algorithms need to perceive the environment first, and then implement a series of calculations such as collision checking and data storing to generate a feasible path. When facing complex and high-dimensional environments, they do not perform well. But the proposed neural network method in this article can predict the promising region where the feasible path exists for any given environment. This prediction result is used to guide the path planning process so that the algorithm performance can get significantly improved. Apart from sampling-based algorithms, the proposed neural network model can also be extended to other types of path planning algorithms.
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ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2021.3121408