Probabilistic Roadmap Method for Path Planning of Intelligent Vehicle Based on Artificial Potential Field Model in Off-roadEnvironment

TIAN Hongqing;WANG Jianqiang;HUANG Heye;DING Feng

CN Submission Journal Articles Figures Search
Adv. Search

  • Sponsored by: China Association for Science and Technology (CAST)

    Editor-In-Chief: Xu Yida

    ISSN 1000-1093

    　

  • Hosted By: China Ordnance Society

    Published By: Acta Armamentarii

    CN 11-2176/TJ

Acta Armamentarii, a Chinese monthly journal, was first published in 1979. The journal is supervised by China Association for Science and Technology, sponsored by China Ordnance Society，and edited and published by Editorial Board of Acta Armamentarii. The Journal is included in all important domestic databases，and also collected by Engineering Index (EI, USA), Chemical Abstracts (CA, USA), Science Abstracts (SA, UK）, Abstract Journal (AJ, Russia), and Current Bibliography on Science and Technology(CBST).Acta Armamentarii has been selected into the journal Of Ordnance Science and Technology (Weapons Industry) Field Of High Quality Scientific and Technological Periodical Classification Catalogue (2022 edition), ranking T1, and the journal of Automotive Engineering Field of High Quality Scientific and technological Periodical classification Catalogue, ranking T2.
Acta Armamentarii ›› 2021, Vol. 42 ›› Issue (7) : 1496-1505. DOI: 10.3969/j.issn.1000-1093.2021.07.017
Paper

Probabilistic Roadmap Method for Path Planning of Intelligent Vehicle Based on Artificial Potential Field Model in Off-roadEnvironment

  • TIAN Hongqing, WANG Jianqiang, HUANG Heye, DING Feng
Author information +
History +

Abstract

Path planning in complex off-road environment is a key technology to realize the autonomous driving of intelligent vehicle. There are obstacles, environmental threats and terrains that affect vehicle movement in off-road environment. In the traditional path planning methods, the shortest path length and time cost are usually taken as the optimization goal, which makes it difficult to plan a feasible and safe driving path in complex off-road environment. An artificial potential field based probabilistic roadmap (APF-PRM) algorithm is proposed to solve the problem. The potential field algorithm is used to model the off-road environment and evaluate the vehicle traffic risk, and then the probabilistic roadmap method is used to conduct the path planning with multi-dimensional traffic cost between path nodes as the goal. Considering the dynamic characteristic of the vehicle, a dynamic curvature smoothing method is used to optimize the vehicle trajectory. Finally, the APF-PRM algorithm is used to conduct the path planning in a simulated off-road environment. The simulated results show that the APF-PRM algorithm utilizes the artificial potential field algorithm to integrate the obstacles, environmental threats and road conditions in the off-road environment in the process of path planning; the probabilistic roadmap method is used to establish a multi-dimensional traffic cost evaluation matrix among the sampling points; and a feasible, safe and efficient path is generated under complex off-road conditions, which provides a multi-objective optimization path planning method for intelligent vehicles.

Key words

intelligentvehicle / off-roadenvironment / potentialfield / probabilisticroadmap / pathplanning

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
TIAN Hongqing, WANG Jianqiang, HUANG Heye, DING Feng. Probabilistic Roadmap Method for Path Planning of Intelligent Vehicle Based on Artificial Potential Field Model in Off-roadEnvironment. Acta Armamentarii. 2021, 42(7): 1496-1505 https://doi.org/10.3969/j.issn.1000-1093.2021.07.017

References


［1］陈慧岩,张玉.军用地面无人机动平台技术发展综述［J］.兵工学报,2014,35(10):1696-1706.
CHEN H Y, ZHANG Y. An overview of research on military unmanned ground vehicles［J］. Acta Armamentarii, 2014, 35(10): 1696-1706. (in Chinese)
［2］LAVALLES M. Planning algorithms［M］. Cambridge,UK: Cambridge University Press, 2006:195-209.
［3］邓海鹏,麻斌,赵海光.自主驾驶车辆紧急避障的路径规划与轨迹跟踪控制［J］.兵工学报,2020,41(3):585-594.
DENG H P，MA B, ZHAO H G. Path planning and tracking control of autonomous vehicle for obstacle avoidance［J］. Acta Armamentarii, 2020, 41(3): 585-594. (in Chinese)
［4］PADEN B, CAP M, YONG S Z, et al. A survey of motion planning and control techniques for self-driving urban vehicles［J］. IEEE Transactions on Intelligent Vehicles, 2016, 1(1):33-55.
［5］LIKHACHEV M，FERGUSON D． Planning long dynamically feasible maneuvers for autonomous vehicles［J］． International Journal of Robotics Research， 2009， 28(8): 933-945．
［6］DOLGOV D, THRUN S, MONTEMERLO M, et al. Path planning for autonomous vehicles in unknown semi-structured environments［J］. International Journal of Robotics Research, 2010, 29(5): 485-501.
［7］LAVALLE S M, KUFFNER J J. Rapidly-exploring random trees: progress and prospects［M］∥DONALD B R, LYNCH K M, RUS D. Algorithmic and Computational Robotics: New Directions. Wellesley, MA, US: A K Peters/CRC Press, 2001:293-308.
［8］KAVRAKI L E, SVESTKA P, LATOMBE J C, et al. Probabilistic roadmaps for path planning in high-dimensional configuration spaces［J］. IEEE Transactions on Robotics & Automation, 1996, 12(4):566-580.
［9］GAMMELLJ D, SRINIVASA S S, BARFOOT T D. Informed RRT*: optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic［C］∥Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, IL, US: IEEE, 2014: 2997-3004.
［10］CHENL, SHAN Y, TIAN W. A fast and efficient double-tree RRT*-like sampling-based planner applying on mobile robotic systems［J］. IEEE/ASME Transactions on Mechatronics, 2018, 23(6):2568-2578.
［11］KARAMAN S, WALTER M R, PEREZ A, et al. Anytime motion planning using the RRT*［C］∥Proceedings of IEEE International Conference on Robotics and Automation. Shanghai, China: IEEE, 2011: 1478-1483.
［12］CHI W Z, WANG C Q, WANG J K, et al. Risk-DTRRT-based optimal motion planning algorithm for mobile robots［J］. IEEE Transactions on Automation and Engineering, 2019, 16(3):1271-1288.
［13］陈天德,黄炎焱,沈炜.基于虚拟障碍物法的无震荡航路规划［J］.兵工学报,2019,40(3):651-658.
CHEN T D, HUANG Y Y, SHEN W. Non-oscillation path planning based on virtual obstacle method［J］. Acta Armamentarii, 2019, 40(3): 651-658. (in Chinese)
［14］KOMORIYA K, TANIE K. Trajectory control of a wheel-type mobile robot using B-spline curve［J］. Journal of the Robotics Society of Japan, 1990,8(2): 398-405.
［15］TAKAHASHI A, HONGO T, NINOMIYA Y, et al. Local path planning and motion control for AGV in positioning［C］∥Proceedings of IEEE/RSJ International Workshop on Intelligent Robots & Systems'89. Tsukuba, Japan: IEEE, 1989: 392-397.
［16］JI Y, TAMURA Y, TAMURA Y, et al. Adaptive motion planning based on vehicle characteristics and regulations for off-road UGVs［J］. IEEE Transactions on Industrial Informatics, 2018, 15(1): 599-611.
［17］张献,任耀峰,王润芃.基于自适应遗传算法的连续时空最优搜索路径规划研究［J］.兵工学报,2015,36(12):2386-2395.
ZHANG X, REN Y F, WANG R P. Research on optimal search path programming in continuous time and space based on an adaptive genetic algorithm［J］. Acta Armamentarii, 2015, 36(12):2386-2395. (in Chinese)
［18］吴天羿, 许继恒, 刘建永,等. 基于改进A*算法的越野路径规划研究［J］.计算机应用研究,2013, 30(6): 1723-1726.
WU T Y, XU J H, LIU J Y, et al. Research of cross-country path planning based on improved A* algorithm［J］. Application Research of Computers, 2013,30(6): 1723-1726. (in Chinese)
［19］MOREAU J, MELCHIOR P, VICTOR S, et al. Path planning with fractional potential fields for autonomous vehicles［J］. IFAC-PapersOnLine, 2017, 50(1): 14533-14538
［20］张文诗, 董成全. 地形通行性能的研究［J］. 测绘科学技术学报, 1996, 13(2):143-147.
ZHANG W S, DONG C Q. Research on the trafficability of terrain［J］. Journal of Surveying and Mapping Science and Techno-logy,1996, 13(2):143-147. (in Chinese)

251

Accesses

0

Citation

Detail
Sections
Recommended

/

〈 〉