TY - GEN
T1 - Fast Motion Planning via Free C-space Estimation Based on Deep Neural Network
AU - Li, Xiang
AU - Cao, Qixin
AU - Sun, Mingjing
AU - Yang, Ganggang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - This paper presents a novel learning-based method for fast motion planning in high-dimensional spaces. A deep neural network is designed to predict the free configuration space rapidly given the environment point cloud. With a generated roadmap as an approximate view of the free C-space, LazyPRM is applied to find and check the path with A∗ search. Due to the application of LazyPRM, the presented method can preserve probabilistic completeness and asymptotic optimality. The new algorithm is tested on a 3-DOF robot arm and a 6-DOF UR3 robot to plan in randomly generated obstacle environments. Results indicate that compared to planners including PRM, RRT∗, RRT-connect and the original LazyPRM, our method is of the lowest time consumption and relatively short path length, showing good performance on both planning speed and path quality.
AB - This paper presents a novel learning-based method for fast motion planning in high-dimensional spaces. A deep neural network is designed to predict the free configuration space rapidly given the environment point cloud. With a generated roadmap as an approximate view of the free C-space, LazyPRM is applied to find and check the path with A∗ search. Due to the application of LazyPRM, the presented method can preserve probabilistic completeness and asymptotic optimality. The new algorithm is tested on a 3-DOF robot arm and a 6-DOF UR3 robot to plan in randomly generated obstacle environments. Results indicate that compared to planners including PRM, RRT∗, RRT-connect and the original LazyPRM, our method is of the lowest time consumption and relatively short path length, showing good performance on both planning speed and path quality.
UR - https://www.scopus.com/pages/publications/85081165580
U2 - 10.1109/IROS40897.2019.8968474
DO - 10.1109/IROS40897.2019.8968474
M3 - 会议稿件
AN - SCOPUS:85081165580
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 3542
EP - 3548
BT - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Y2 - 3 November 2019 through 8 November 2019
ER -