| Citation: | Qinghua LI, Yue YOU, Yaqi MU, Zhao ZHANG, Chao FENG. Integrated Navigation Algorithm for Large Concave Obstacles[J]. Journal of Electronics & Information Technology, 2020, 42(4): 917-923. doi: 10.11999/JEIT190179
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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.
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王勇臻, 陈燕, 于莹莹. 求解多旅行商问题的改进分组遗传算法[J]. 电子与信息学报, 2017, 39(1): 198–205. doi: 10.11999/JEIT160211
WANG Yongzhen, CHEN Yan, and YU Yingying. Improved grouping genetic algorithm for solving multiple traveling salesman problem[J]. Journal of Electronics &Information Technology, 2017, 39(1): 198–205. doi: 10.11999/JEIT160211
|
|
黄长强, 赵克新. 基于改进蚁狮算法的无人机三维航迹规划[J]. 电子与信息学报, 2018, 40(7): 1532–1538. doi: 10.11999/JEIT170961
HUANG Changqiang and ZHAO Kexin. Three dimensional path planning of UAV with improved ant lion optimizer[J]. Journal of Electronics &Information Technology, 2018, 40(7): 1532–1538. doi: 10.11999/JEIT170961
|
|
KHATIB O. Real-time obstacle avoidance for manipulators and mobile robots[J]. The International Journal of Robotics Research, 1986, 5(1): 90–98. doi: 10.1177/027836498600500106
|
|
PARK M G, JEON J H, and LEE M C. Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing[C]. 2001 IEEE International Symposium on Industrial Electronics, Pusan, South Korea, 2001: 1530–1535. doi: 10.1109/ISIE.2001.931933.
|
|
况菲, 王耀南. 基于混合人工势场-遗传算法的移动机器人路径规划仿真研究[J]. 系统仿真学报, 2006, 18(3): 774–777. doi: 10.16182/j.cnki.joss.2006.03.061
KUANG Fei and WANG Yaonan. Robot path planning based on hybrid artificial potential field/genetic algorithm[J]. Journal of System Simulation, 2006, 18(3): 774–777. doi: 10.16182/j.cnki.joss.2006.03.061
|
|
LEE D, JEONG J, KIM Y H, et al. An improved artificial potential field method with a new point of attractive force for a mobile robot[C]. The 2nd International Conference on Robotics and Automation Engineering, Shanghai, China, 2017: 63–67. doi: 10.1109/ICRAE.2017.8291354.
|
|
ROSTAMI S M H, SANGAIAH A K, WANG Jin, et al. Obstacle avoidance of mobile robots using modified artificial potential field algorithm[J]. EURASIP Journal on Wireless Communications and Networking, 2019(1): No. 70, 1–19. doi: 10.1186/s13638-019-1396-2
|
|
HART P E, NILSSON N J, and RAPHAEL B. A formal basis for the heuristic determination of minimum cost paths[J]. IEEE Transactions on Systems Science and Cybernetics, 1968, 4(2): 100–107. doi: 10.1109/TSSC.1968.300136
|
|
DECHTER R and PEARL J. Generalized best-first search strategies and the optimality of A*[J]. Journal of the ACM, 1985, 32(3): 505–536. doi: 10.1145/3828.3830
|
|
田景文, 孔垂超, 高美娟. 一种车辆路径规划的改进混合算法[J]. 计算机工程与应用, 2014, 50(14): 58–63. doi: 10.3778/j.issn.1002-8331.1208-0319
TIAN Jingwen, KONG Chuichao, and GAO Meijuan. Improved hybrid algorithm of vehicle path planning[J]. Computer Engineering and Applications, 2014, 50(14): 58–63. doi: 10.3778/j.issn.1002-8331.1208-0319
|
|
胡中华, 潘洲, 王凯凯. 基于混合算法的动态路径规划[J]. 煤矿机械, 2015, 36(12): 243–245. doi: 10.13436/j.mkjx.201512103
HU Zhonghua, PAN Zhou, and WANG Kaikai. Dynamic path planning based on hybrid algorithm[J]. Coal Mine Machinery, 2015, 36(12): 243–245. doi: 10.13436/j.mkjx.201512103
|
|
唐志荣, 冀杰, 吴明阳, 等. 基于改进人工势场法的车辆路径规划与跟踪[J]. 西南大学学报: 自然科学版, 2018, 40(6): 174–182. doi: 10.13718/j.cnki.xdzk.2018.06.025
TANG Zhirong, JI Jie, WU Mingyang, et al. Vehicles path planning and tracking based on an improved artificial potential field method[J]. Journal of Southwest University:Natural Science Edition, 2018, 40(6): 174–182. doi: 10.13718/j.cnki.xdzk.2018.06.025
|
|
赵奇, 赵阿群. 一种基于A*算法的多径寻由算法[J]. 电子与信息学报, 2013, 35(4): 952–957. doi: 10.3724/SP.J.1146.2012.00983
ZHAO Qi and ZHAO Aqun. A multi-path routing algorithm base on A* algorithm[J]. Journal of Electronics &Information Technology, 2013, 35(4): 952–957. doi: 10.3724/SP.J.1146.2012.00983
|
|
DIJKSTRA E W. A note on two problems in connexion with graphs[J]. Numerische Mathematik, 1959, 1(1): 269–271. doi: 10.1007/BF01386390
|
|
沈文君. 基于改进人工势场法的机器人路径规划算法研究[D]. [硕士论文], 暨南大学, 2009.
SHEN Wenjun. Algorithm research of path planning for robot based on improved artifical potential field[D]. [Master dissertation], Jinan University, 2009.
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