BRMPNet: bidirectional recurrent motion planning networks for generic robotic platforms in smart manufacturing

doi:10.1007/s40436-024-00529-6

Advances in Manufacturing ›› 2025, Vol. 13 ›› Issue (3): 477-492.doi: 10.1007/s40436-024-00529-6

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BRMPNet: bidirectional recurrent motion planning networks for generic robotic platforms in smart manufacturing

Bo-Han Feng, Bo-Yan Li, Xin-Ting Jiang, Qi Zhou, You-Yi Bi

University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China

收稿日期:2023-09-29 修回日期:2023-12-14 发布日期:2025-09-19
通讯作者: You-Yi Bi,E-mail:youyi.bi@sjtu.edu.cn E-mail:youyi.bi@sjtu.edu.cn
作者简介:Bo-Han Feng is currently a Ph.D. student at University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, China. He received B.S. degree in Computer Science and Engineering from Dalian University of Technology in 2020. His research focuses on robot motion planning and human-robot collaboration algorithms in smart manufacturing.
Bo-Yan Li is currently a M.S. student at University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, China. He received B.S. degree in Mechanical Engineering from Shanghai Jiao Tong University. His research interests include 6D pose estimation, motion planning and robotics.
Xin-Ting Jiang is currently an undergraduate student in University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University. His research interests are computer vision algorithms and robotics.
Qi Zhou is currently a Ph.D. student at University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, China. He received B.S. degree in Mechanical and Electronic Engineering from Shandong University, China, in 2021. His research topics include robotics and reinforcement learning.
You-Yi Bi is currently an associate professor at University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, China. He obtained his Ph.D. in Mechanical Engineering from Purdue University in 2017. His research interests include datadriven design, AI for robotics and smart manufacturing.
基金资助:
The authors would like to acknowledge the financial support from National Key R&D Program of China (Grant No. 2022YFB4702400).

BRMPNet: bidirectional recurrent motion planning networks for generic robotic platforms in smart manufacturing

Bo-Han Feng, Bo-Yan Li, Xin-Ting Jiang, Qi Zhou, You-Yi Bi

University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China

Received:2023-09-29 Revised:2023-12-14 Published:2025-09-19
Supported by:
The authors would like to acknowledge the financial support from National Key R&D Program of China (Grant No. 2022YFB4702400).

摘要/Abstract

摘要： In the era of Industry 4.0, robot motion planning faces unprecedented challenges in adapting those high-dimension dynamic working environments with rigorous real-time planning requirements. Traditional sampling-based planning algorithms can find solutions in high-dimensional spaces but often struggle with achieving the balance among computational efficiency, real-time adaptability, and solution optimality. To overcome these challenges and unlock the full potential of robotic automation in smart manufacturing, we propose bidirectional recurrent motion planning network (BRMPNet). As an imitation learning-based approach for robot motion planning, it leverages deep neural networks to learn the heuristics for approximate-optimal path planning. BRMPNet employs the refined PointNet++ network to incorporate raw point-cloud information from depth sensors and generates paths with a bidirectional strategy using long short-term memory (LSTM) network. It can also be integrated with traditional sampling-based planning algorithms, offering theoretical assurance of the probabilistic completeness for solutions. To validate the effectiveness of BRMPNet, we conduct a series of experiments, benchmarking its performance against the state-of-the-art motion planning algorithms. These experiments are specifically designed to simulate common operations encountered within generic robotic platforms in smart manufacturing such as mobile robots and multi-joint robotic arms. The results demonstrate BRMPNet’s superior performance on key metrics including solution quality and computational efficiency, suggesting the promising potential of learning-based planning in addressing complex motion planning challenges.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00529-6

关键词: Robot motion planning, Imitation learning, Deep neural network, Smart manufacturing, Adaptive and real-time planning

Abstract: In the era of Industry 4.0, robot motion planning faces unprecedented challenges in adapting those high-dimension dynamic working environments with rigorous real-time planning requirements. Traditional sampling-based planning algorithms can find solutions in high-dimensional spaces but often struggle with achieving the balance among computational efficiency, real-time adaptability, and solution optimality. To overcome these challenges and unlock the full potential of robotic automation in smart manufacturing, we propose bidirectional recurrent motion planning network (BRMPNet). As an imitation learning-based approach for robot motion planning, it leverages deep neural networks to learn the heuristics for approximate-optimal path planning. BRMPNet employs the refined PointNet++ network to incorporate raw point-cloud information from depth sensors and generates paths with a bidirectional strategy using long short-term memory (LSTM) network. It can also be integrated with traditional sampling-based planning algorithms, offering theoretical assurance of the probabilistic completeness for solutions. To validate the effectiveness of BRMPNet, we conduct a series of experiments, benchmarking its performance against the state-of-the-art motion planning algorithms. These experiments are specifically designed to simulate common operations encountered within generic robotic platforms in smart manufacturing such as mobile robots and multi-joint robotic arms. The results demonstrate BRMPNet’s superior performance on key metrics including solution quality and computational efficiency, suggesting the promising potential of learning-based planning in addressing complex motion planning challenges.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00529-6

Key words: Robot motion planning, Imitation learning, Deep neural network, Smart manufacturing, Adaptive and real-time planning

Bo-Han Feng, Bo-Yan Li, Xin-Ting Jiang, Qi Zhou, You-Yi Bi. BRMPNet: bidirectional recurrent motion planning networks for generic robotic platforms in smart manufacturing[J]. Advances in Manufacturing, 2025, 13(3): 477-492.

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BRMPNet: bidirectional recurrent motion planning networks for generic robotic platforms in smart manufacturing

BRMPNet: bidirectional recurrent motion planning networks for generic robotic platforms in smart manufacturing

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