Motion Planning of Manipulator by Points-Guided Sampling Network

Lyu，Erli1; Liu，Tingting2; Wang，Jiaole1; Song，Shuang1; Meng，Max Q.H.3

2022

10.1109/TASE.2022.3168542

IEEE Transactions on Automation Science and Engineering   影响因子和分区

1545-5955

1558-3783

This paper proposes a network called points-guided sampling net (PGSN) to guide the sampling process in sampling-based motion planner by utilizing the geometric information of obstacles. The geometric information is extracted from the point cloud of obstacles. By analyzing the properties of the point cloud, we propose a VAE feature extraction net that incorporates the variational autoencoder (VAE) framework with unique architectures designed for point clouds. Furthermore, we design a multi-modal sampling net to model the probability distribution of the states based on training trajectories taken from different environments. Based on PGSN, we propose a sampling-based motion planning algorithm called the point-guided rapidly-exploring random tree (PG-RRT). Three experiments are conducted to verify the proposed PGSN: Exp I shows the proposed VAE feature extraction net can successfully extract geometric features from the inputted point cloud; Exp II verifies the multi-modal sampling net successfully chooses corresponding mode with respect to extracted features; Exp III demonstrates the efficacy of our PG-RRT algorithm by showing PG-RRT outperforms other algorithms. Moreover, we provide theoretical analysis and insights towards understanding our model.

Data mining deep learning Feature extraction Planning Point cloud compression point clouds Points-guided sampling sampling-based motion planning. Three-dimensional displays Training Trajectory

SCI ; EI

英语

其他

WOS:000791743500001

20221912092222

Deep learning ; Extraction ; Feature extraction ; Geometry ; Motion planning ; Probability distributions ; Robot programming ; Three dimensional computer graphics ; Trees (mathematics)

Ergonomics and Human Factors Engineering:461.4 ; Computer Programming:723.1 ; Data Processing and Image Processing:723.2 ; Computer Applications:723.5 ; Robotics:731.5 ; Chemical Operations:802.3 ; Mathematics:921 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Probability Theory:922.1

2-s2.0-85129412386

Scopus

1.School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
2.Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China
3.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 5180000, China, on leave from the Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, and also with the Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518172, China

Lyu，Erli,Liu，Tingting,Wang，Jiaole,et al. Motion Planning of Manipulator by Points-Guided Sampling Network[J]. IEEE Transactions on Automation Science and Engineering,2022,PP(99):1-11.

Lyu，Erli,Liu，Tingting,Wang，Jiaole,Song，Shuang,&Meng，Max Q.H..(2022).Motion Planning of Manipulator by Points-Guided Sampling Network.IEEE Transactions on Automation Science and Engineering,PP(99),1-11.

Lyu，Erli,et al."Motion Planning of Manipulator by Points-Guided Sampling Network".IEEE Transactions on Automation Science and Engineering PP.99(2022):1-11.