Enhance Connectivity of Promising Regions for Sampling-Based Path Planning

Ma, Han1; Li, Chenming1; Liu, Jianbang1; Wang, Jiankun2,3; Meng, Max Q-H2,3,4

2022-07-01

10.1109/TASE.2022.3191519

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

1545-5955

1558-3783

Sampling-based path planning algorithms usually implement uniform sampling methods to search the state space. However, uniform sampling may lead to unnecessary exploration in many scenarios, such as the environment with a few dead ends. Our previous work proposes to use the promising region to guide the sampling process to address the issue. However, the predicted promising regions are often disconnected, which means they cannot connect the start and goal states, resulting in a lack of probabilistic completeness. This work focuses on enhancing the connectivity of predicted promising regions. Our proposed method regresses the connectivity probability of the edges in the x and y directions. In addition, it calculates the weight of the promising edges in loss to guide the neural network to pay more attention to the connectivity of the promising regions. We conduct a series of simulation experiments, and the results show that the connectivity of promising regions improves significantly. Furthermore, we analyze the effect of connectivity on sampling-based path planning algorithms and conclude that connectivity plays an essential role in maintaining algorithm performance. Note to Practitioners-This work is derived from the promising region prediction for sampling-based path planning. The sampling-based path planning methods have been widely used in robotics due to their efficiency. To further improve the efficiency of these algorithms, sampling in the promising region predicted by a neural network is introduced into the sampling procedure. However, the connectivity of the promising region has yet to be considered, and it will affect the performance of the algorithms in several aspects. To demonstrate this problem, we compare the performance of the neural heuristic algorithms under different connectivity statuses in this paper. Furthermore, to enhance the connectivity of the predicted promising region, the novel prediction output and loss function are proposed. The simulation results show improvements in the algorithms after utilizing our method.

Path planning Robots Planning Prediction algorithms Heuristic algorithms Probabilistic logic Predictive models Sampling-based path planning neural network promising region prediction

SCI ; EI

英语

通讯

National Key Research and Development Program of China[2019YFB1312400] ; Hong Kong Research Grants Council (RGC) General Research Fund (GRF)[14200618] ; National Natural Science Foundation of China[62103181]

Automation & Control Systems

Automation & Control Systems

WOS:000829067500001

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20223112531449

Efficiency ; Forecasting ; Motion planning ; Neural networks ; Probabilistic logics ; Robot programming

Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Computer Programming:723.1 ; Robotics:731.5 ; Production Engineering:913.1

Web of Science

1.Chinese Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China
2.Southern Univ Sci & Technol, Shenzhen Key Lab Robot Percept & Intelligence, Shenzhen 518055, Peoples R China
3.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Peoples R China
4.Chinese Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518057, Peoples R China

Ma, Han,Li, Chenming,Liu, Jianbang,et al. Enhance Connectivity of Promising Regions for Sampling-Based Path Planning[J]. IEEE Transactions on Automation Science and Engineering,2022,PP(99):1-14.

Ma, Han,Li, Chenming,Liu, Jianbang,Wang, Jiankun,&Meng, Max Q-H.(2022).Enhance Connectivity of Promising Regions for Sampling-Based Path Planning.IEEE Transactions on Automation Science and Engineering,PP(99),1-14.

Ma, Han,et al."Enhance Connectivity of Promising Regions for Sampling-Based Path Planning".IEEE Transactions on Automation Science and Engineering PP.99(2022):1-14.