尤越2, 4,
沐雅琪2, 4,
张钊2,
冯超1, 3, 4,,
1.齐鲁工业大学(山东省科学院)电子信息工程学院(大学物理教学部) 济南 250353
2.齐鲁工业大学(山东省科学院)电气工程与自动化学院 济南 250353
3.齐鲁工业大学(山东省科学院)山东省科学院自动化研究所 济南 250101
4.济南市人机智能协同工程实验室 济南 250353
基金项目:国家自然科学基金(61701270)，齐鲁工业大学(山东省科学院)青年博士合作基金(2017BSHZ008)
详细信息
作者简介:李庆华：男，1977年生，副教授，研究方向为信号与信息处理
尤越：女，1996年生，硕士生，研究方向为智能路径规划
沐雅琪：女，1996年生，硕士生，研究方向为复杂环境路径规划算法
张钊：男，1995年生，硕士生，研究方向为人工智能运动目标行为识别
冯超：男，1984年生，讲师，研究方向为路径规划运动规划
通讯作者:冯超 cfeng@qlu.edu.cn
中图分类号:TN96计量
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被引次数:0
出版历程
收稿日期:2019-03-26
修回日期:2019-11-10
网络出版日期:2019-11-13
刊出日期:2020-06-04
Integrated Navigation Algorithm for Large Concave Obstacles
Qinghua LI1, 4,Yue YOU2, 4,
Yaqi MU2, 4,
Zhao ZHANG2,
Chao FENG1, 3, 4,,
1. School of Electronic and Information Engineering (Department of Physics), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)
2. School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
3. Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250101, China
4. Jinan Engineering Laboratory of Human-machine Intelligent Cooperation, Jinan 250353, China
Funds:The National Natural Science Foundation of China (61701270), The Young Doctor Cooperation Foundation of Qilu University of Technology (Shandong Academy of Sciences) (2017BSHZ008)
摘要
摘要:针对移动机器人导航过程中无法规避大型凹型障碍物问题，该文提出一种多状态的组合导航算法。算法按照不同的运动环境，将移动机器人的运行状态分类为运行态、切换态、避障态，同时定义了基于移动机器人运行速度和运行时间的状态双切换条件。当移动机器人处于运行态时，采用人工势场法(APFM)进行导航，并实时观测毗邻障碍物的几何构型。在遭遇障碍物时，切换态用于判断是否满足状态切换条件，以进入避障态执行避障算法。避障完成后，状态自动切换回运行态继续执行导航任务。多状态的提出，可有效解决传统人工势场法在大型凹形障碍物的避障过程中存在局部震荡的问题。基于运行速度和运行时间的双切换条件判定算法，可实现多状态间的平滑切换。实验结果表明，该算法在解决局部震荡问题的同时，还可降低避障时间，提升导航算法效率。
关键词:组合导航算法/
人工势场法/
A*算法/
大型凹形障碍物
Abstract:For the problem that mobile robot can not avoid large concave obstacles during navigation, this paper proposes a multi-state integrated navigation algorithm. The algorithm classifies the running state of mobile robot into running state, switching state and obstacle avoidance state according to different moving environment, and defines the state double switching conditions based on the running speed and running time of the mobile robot. The Artificial Potential Field Method (APFM) is used to navigate and observe the geometric configuration of adjacent obstacles in real time. When encountering an obstacle, the switching state is used to determine whether the state switching condition is satisfied, and the obstacle avoidance algorithm is executed to enter the obstacle avoidance state and enter the obstacle avoidance state to implement the obstacle avoidance algorithm. After the obstacle avoidance is completed, the state automatically switches back to the running state to continue the navigation task. The proposal of multi-state can solve the problem of local oscillation of traditional artificial potential field method in the process of avoiding large concave obstacles. Furthermore, the double-switching condition determination algorithm based on running speed and running time can realize smooth switching between states and optimize the path. The experimental results show that the algorithm can not only solve the local oscillation problem, but also reduce the obstacle avoidance time and improve the efficiency of the navigation algorithm.
Key words:Integrated navigation algorithm/
Artificial Potential Field Method (APFM)/
A* algorithm/
Large concave obstacle
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