Ni Jiamin
1. Institute of Vehicles and Construction Machinery, Nanjing Tech University, Nanjing 211816; 2. Joint Institute of Excavator Key Technology, Nanjing Tech University -Sany Heavy Machinery, Suzhou 215300, ChinaMa Wei
1. Institute of Vehicles and Construction Machinery, Nanjing Tech University, Nanjing 211816; 2. Joint Institute of Excavator Key Technology, Nanjing Tech University -Sany Heavy Machinery, Suzhou 215300, ChinaTong Xin
1. Institute of Vehicles and Construction Machinery, Nanjing Tech University, Nanjing 211816; 2. Joint Institute of Excavator Key Technology, Nanjing Tech University -Sany Heavy Machinery, Suzhou 215300, ChinaXie Wenxin
1. Institute of Vehicles and Construction Machinery, Nanjing Tech University, Nanjing 211816; 2. Joint Institute of Excavator Key Technology, Nanjing Tech University -Sany Heavy Machinery, Suzhou 215300, ChinaFeng Hao
Joint Institute of Excavator Key Technology, Nanjing Tech University -Sany Heavy Machinery, Suzhou 215300, ChinaYin Chenbo
1. Institute of Vehicles and Construction Machinery, Nanjing Tech University, Nanjing 211816; 2. Joint Institute of Excavator Key Technology, Nanjing Tech University -Sany Heavy Machinery, Suzhou 215300, China1. Institute of Vehicles and Construction Machinery, Nanjing Tech University, Nanjing 211816; 2. Joint Institute of Excavator Key Technology, Nanjing Tech University -Sany Heavy Machinery, Suzhou 215300, China
TP183
In view of the problems of poor measurement reliability and low accuracy caused by traditional pull-wire displacement sensors due to collisions, bad weather, etc., a virtual displacement sensor for excavator working devices that establishes the mapping relationship between the cylinder displacement length and the pixel coordinates of the marking point through a neural network is proposed system. Using image processing technology to extract the center pixel coordinates of the hydraulic cylinder marking point, taking the pixel coordinates and the actual cylinder displacement signal as input, and establishing the mapping relationship between the cylinder displacement and marking point center coordinates through the neural network optimized by genetic algorithm, predicting the cylinder displacement and obtaining mining The attitude of the machine working device. Experiments show that the accuracy of the cylinder displacement predicted by this method is as high as 99.5%, and the predicted mean square error of the working device's attitude is 1.1329, which meets the requirements of practical applications and can be used in the actual measurement of the excavator's attitude.
