基于多层Morphin搜索树的UGV局部路径规划算法

doi:10.13973/j.cnki.robot.2014.0491

摘要
图/表
参考文献
相关文章 (4)

全文: PDF (2597 KB) HTML ( ) 输出: BibTeX \| EndNote (RIS)
引用本文:
诸葛程晨, 唐振民, 石朝侠. 基于多层Morphin搜索树的UGV局部路径规划算法[J]. 机器人, 2014, 36(4): 491-497.DOI: 10.13973/j.cnki.robot.2014.0491. ZHUGE Chengchen, TANG Zhenmin, SHI Zhaoxia. Local Path Planning Algorithm for UGV Based on Multilayer Morphin Search Tree. ROBOT, 2014, 36(4): 491-497. DOI: 10.13973/j.cnki.robot.2014.0491.

摘要

以地面自主车辆（UGV）为实际应用背景，提出了一种基于多层Morphin搜索树的局部路径规划算法．该算法结合车辆非完整性约束构造多层Morphin搜索树，通过模糊Q学习进行动态行为控制的学习，并以此为依据对搜索树进行评估，从而得到一条具备车辆非完整约束的平滑可跟踪路径，克服了传统Morphin算法搜索轨迹不灵活的缺点．最后，通过仿真实验以及实车实验验证了算法的有效性和正确性．

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	诸葛程晨
	唐振民
	石朝侠

关键词 ：多层Morphin搜索树, UGV, 非完整性约束, 模糊Q学习, 局部路径规划

Abstract：

Aiming at the application of unmanned ground vehicle (UGV), a local path planning algorithm based on multilayer Morphin search tree is proposed. By considering the nonholonomic constraints of vehicle, a multilayer Morphin search tree is constructed. Then control of dynamic behavior is learned by fuzzy Q-learning, as a basis for the evaluation of search tree to get a smooth and trackable trajectory with nonholonomic constraints of vehicle. This algorithm overcomes the inflexibility of Morphin algorithm in trajectory searching. The simulation results and the test on a real vehicle verify correctness and effectiveness of the algorithm.

Key words： multilayer Morphin search tree UGV (unmanned ground vehicle) nonholonomic constraint fuzzy Q-learning local path planning

收稿日期: 2013-10-08 录用日期: 2014-01-08

TP249

基金资助:

国家自然科学基金资助项目（91220301，61371040）

通讯作者: 诸葛程晨，zgccmax@163.com E-mail: zgccmax@163.com

作者简介: 诸葛程晨（1986-），男，博士生．研究领域：智能机器人自主导航．
唐振民（1961-），男，教授，博士生导师．研究领域：智能系统，图像处理，模式识别．
石朝侠（1974-），男，副教授．研究领域：移动机器人自主导航，视觉环境认知，多机器人协作．

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.2014.0491 或 https://robot.sia.cn/CN/Y2014/V36/I4/491

[1] 蔡自兴,贺汉根,陈虹.未知环境中移动机器人导航控制研究的若干问题[J].控制与决策,2002,17(4):385-390.Cai Z X, He H G, Chen H. Some issues for mobile robots navigation under unknown environments[J]. Control and Decision, 2002, 17(4): 385-390.

[2] Ferguson D, Stentz A. Field D^*: An interpolation-based path planner and replanner[C]//12th International Symposium on Robotics Research. Berlin, Germany: Springer, 2007: 239-253.

[3] Stentz A. Optimal and efficient path planning for partially-known environments[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 1994: 3310-3317.

[4] Khatib O. Real-time obstacle avoidance for manipulators and mobile robots[J]. International Journal of Robotics Research, 1986, 5(1): 90-98.

[5] 康亮,赵春霞,郭剑辉.未知环境下改进的基于RRT算法的移动机器人路径规划[J].模式识别与人工智能,2009,22(3):337-343.Kang L, Zhao C X, Guo J H. Improved path planning based on rapidly-exploring random tree for mobile robot in unknown environment[J]. Pattern Recognition and Artificial Intelligence, 2009, 22(3): 337-343.

[6] Melchior N A, Simmons R. Particle RRT for path planning with uncertainty[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2007: 1617-1624.

[7] 宋金泽,戴斌,单恩忠,等.一种改进的RRT路径规划算法[J].电子学报,2010,38(B02):225-228.Song J Z, Dai B, Shan E Z, et al. An improved RRT path planning algorithm[J]. Acta Eletronica Sinica, 2010, 38(B02): 225-228.

[8] Borenstein J, Koren Y. The vector field histogram-Fast obstacle avoidance for mobile robots[J]. IEEE Transactions on Robotics and Automation, 1991, 7(3): 278-288.

[9] Simmons R, Krotkov E, Chrisman L, et al. Experience with rover navigation for lunar-like terrains[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 1995: 441-446.

[10] Valiollahi S, Ghaderi R, Ebrahimzadeh A. A fuzzy Q-learning approach to navigation of an autonomous robot[C]//16th CSI International Symposium on Artificial Intelligence and Signal Processing. Piscataway, USA: IEEE, 2012: 520-525.