|
|
|
| Obstacle identification method based on lidar data for warehousing logistics AGV |
| School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, China |
|
|
|
|
Abstract To solve the problem of AGV obstacle avoidance in intelligent warehousing, the method with lidar to identify the obstacle category in front of AGV was proposed to make reasonable decision by combining obstacle position information to assist AGV. The lidar data was filtered and clustered to obtain clusters with high purity. The feature vector was extracted by the proposed feature extraction method. The particle swarm optimization (PSO) algorithm was used to find the optimal parameters of the radial basis kernel (RBF) support vector machine (SVM) on the training set, and the model was trained. The method was tested on the data set of the intelligent warehousing simulation environment. The results show that the accuracy reaches 94.58%, which can accurately and effectively identify the categories of obstacles in front of the AGV.
|
|
Received: 19 July 2019
|
|
|
|
| ［1］ |
张涛. 仓储物流机器人室内定位与路径规划［D］.重庆:西南交通大学,2018.
|
| ［2］ |
JIN X, ZHONG M, QUAN X, et al. Dynamic scheduling of mobile-robotic warehouse logistics system［C］∥2016 35th Chinese Control Conference (CCC),2016:2860-2865.
|
| ［3］ |
HAO S, HUANG Z, WANG L, et al. An optimal task decision method for a warehouse robot with multiple tasks based on linear temporal logic［C］∥IEEE International Conference on Systems, Man and Cybernetics, 2017:1453-1458.
|
| ［4］ |
BOGDAN S, KOVACIC Z. Time windows based dyna-mic routing in multi-AGV systems［J］. Automation Science and Engineering, 2010, 7(1): 151-155.
|
| ［5］ |
GU B H. Research of multiple AGV system path optimization based on a time window［C］∥Proceedings of 2014 International Conference on Artificial Intelligence and Industrial Application,2014:9.
|
| ［6］ |
向亚军,严华.基于激光雷达的移动机器人避障策略研究［J］.四川大学学报(自然科学版),2017,54(3):529-534.
|
|
XIANG Y J, YAN H. Obstacle avoidance for mobile robots strategy based on laser radar［J］. Journal of Sichuan University(Natural Science Edition),2017,54(3):529-534.(in Chinese)
|
| ［7］ |
李文杰,闫世强,蒋莹,等.自适应确定DBSCAN算法参数的算法研究［J］.计算机工程与应用,2019,55(5):1-7.
|
|
LI W J, YAN S Q, JIANG Y, et al. Research on me-thod of self-adaptive determination of DBSCAN algorithm parameters［J］. Computer Engineering and Applications, 2019,55(5):1-7. (in Chinese)
|
| ［8］ |
刘方园,王水花,张煜东.支持向量机模型与应用综述［J］.计算机系统应用,2018,27(4):1-9.
|
|
LIU F Y, WANG S H, ZHANG Y D. Overview on mo-dels and applications of support vector machine［J］. Computer Systems ＆ Applications,2018,27(4):1-9. (in Chinese)
|
| ［9］ |
陈思羽,宁芊,周新志,等.DAG-SVM的结构优化研究及其在故障诊断中的应用［J］.四川大学学报(自然科学版),2015,52(2):299-305.
|
|
CHEN S Y, NING Q, ZHOU X Z, et al. Support vector machine with structure optimized decision directed acyclic graph and its application to fault diagnosis［J］. Journal of Sichuan University(Natural Science Edition),2015,52(2):299-305. (in Chinese)
|
| ［10］ |
张峰,胡向东,林家富,等.基于SVM的金融类钓鱼网页检测方法［J］.重庆邮电大学学报(自然科学版),2017,29(6):806-813.
|
|
ZHANG F, HU X D, LIN J F, et al. Method of detecting the financial phishing webpage based on SVM［J］.Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2017,29(6):806-813. (in Chinese)
|
| ［11］ |
KENNEDY J, EBERHART R. Particle swarm optimization［C］∥Proceedings of the IEEE International Confe-rence on Neural Networks,1995: 1942-1948.
|
| ［12］ |
周相兵.一种基于粒子群优化的虚拟资源分配方法［J］.重庆邮电大学学报(自然科学版),2014,26(5):686-693.
|
|
ZHOU X B.An optimization approach of virtual resources allocation base on particle swarm algorithm［J］.Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2014,26(5):686-693. (in Chinese)
|
|
|
|
2020, Vol. 41 
