Entity

Time filter

Source Type


Liu H.,Jiangsu University | Zhao D.,Jiangsu University | Zhao D.,Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry | Sun Y.,Jiangsu University | And 2 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2014

In order to meet the requirements of the periodic cutting and cleaning of aquatic plants in river crab aquaculture, a small and medium-sized intelligent automatic aquatic plant cleaning ship based on ARM was designed. Then the mechanical structure and working principle of the integration of harvest were introduced, and the ship's main structure characteristics were also put forward, such as the paddle propeller without a rudder, rotary cutting device, cut deep automatic adjustment regulator and so on. The closed loop control system of GPS navigation for the ship was designed with PD and PI control technology of intelligent mobile robot, and high precision GPS navigation control technology. The experiment results showed that the control precision of linear track could be controlled precisely within the scope of ±30 cm. On the basis of meet harvest requirement, the control system can avoid the repeat cutting or miss cutting caused by yaw effectively. ©, 2014, Chinese Society of Agricultural Machinery. All right reserved. Source


Jia W.,Jiangsu University | Jia W.,Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry | Zhao D.,Jiangsu University | Zhao D.,Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry | And 6 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2015

In order to improve the recognition precision and speed for apple, and further improve the harvesting efficiency of apple harvesting robot, an apple recognition method based on combining K-means clustering segmentation with genetic radial basis function (RBF) neural network is proposed. Firstly, the captured apple image is transformed into L* a* b* color space, and then under this color space, the K-means clustering algorithm is used to segment the apple image. The color feature components and shape components of segmented image are extracted respectively. The color features include R, G, B, H, S and I, a total of 6 feature components; and the shape features include circular variance, density, ratio of perimeter square to area, and 7 Hu invariant moments, a total of 10 shape components. These extracted 16 features are used as the inputs of neural network to train RBF neural network, and get the apple recognition model. Due to some inherent defects the RBF neural network has, such as low learning rate, easily causing over fitting phenomenon, genetic algorithm (GA) is introduced to optimize the connection weights and the number of hidden layer neurons. In this study, a new optimization way is adopted, that is, the hybrid encoding of the number of hidden layer neurons and connection weights is carried out simultaneously. This moment, the learning of weights is not completed, and the least mean square (LMS) is used to further learn the connection weights. Finally, an optimized neural network model (GA-RBF-LMS) is established, which is to improve the operating efficiency and recognition precision. In the experiments, there are 150 images captured, and they have 229 apples; among them 50 images are selected as training samples, and the rest as testing samples. Every image for training sample has only one apple, so the testing samples have 179 apples. In order to get the precise model, fruits of apple are together with branches and leaves for training during the training process, which avoids the influence of branches or leaves shade on the recognition to some extent. So the training samples have 50 apples, 50 branches and 50 leaves, which are a total of 150 training samples, and the outputs of neural network include 3 classes. In order to compare with the traditional back propagation (BP) and RBF neural network, and GA-RBF algorithm, a series of experiments are carried out. After repeated trainings of 50 times, the results show that the successful training rate of the GA-RBF-LMS is the highest, which can reach 100% and get the minimum training error; but its running time is the longest, because the 2 optimizations of genetic algorithm and LMS are at the expense of the time. The recognition rates of the fruits with different growth postures, such as fruit without obscuration, overlapping fruit and covered fruit, are calculated respectively. After repeated experiments of 50 times, the results show that these 4 recognition models can achieve very good effect for recognizing the fruit without obscuration. For covered fruit and overlapping fruit, the recognition rate of GA-RBF-LMS is the highest, which can reach 95. 38% and 96. 17%, respectively. Looking from the overall, the recognition rate reaches 96. 95%, recognizing 179 apples consumes 1. 75 s, and the sum of square of error is the smallest. From the training time, the GA-RBF-LMS algorithm is the longest, whose average training time is 4. 412 s for 150 training samples, but the training success rate can reach 100%, which saves the time wasted in human trying to construct the network structure. All of these illustrate that the GA-RBF-LMS neural network model has the higher operating efficiency and recognition precision, and it can be applied in target recognition for apple harvesting robot. ©, 2015, Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering. All right reserved. Source


Ji W.,Jiangsu University | Ji W.,Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry | Lu X.,Jiangsu University | Zhao D.,Jiangsu University | And 3 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2016

In order to improve the adaptability and working efficiency of apple harvesting robot used to promptly pick the ripe apples, the harvesting robot has to work continuously at night. But the night vision image of apple has many dark spaces and shadows besides the low resolution. These negative factors bring difficulties for the harvesting robot to work at night. So this paper proposes an edge-preserving Retinex algorithm based on guided filtering to enhance apple night vision image. The illumination component is estimated by using the guided filtering which can be used as an edge-preserving smoothing operator, and then it is removed from the original image to obtain the reflection component with its own characteristics. After Gamma correction, the 2 parts of the image are synthesized into a new image. Finally the night vision image of apple is enhanced. The specific implementation process is stated as follows: firstly, an apple night vision image of the RGB (red, green, blue) is converted into HSI (hue, saturation, intensity) color space. Then the intensity of the image is processed by the guided filtering which has a function of edge-preserving. This algorithm is able to accurately estimate the illumination of the image in the edge area with high contrast. After that, a single scale Retinex algorithm is used for logarithmic transform to get the reflection image. Then, the Gamma corrections are used for reflection component and illumination component. The 2 parts of the image are synthesized into a new image and the resulting image is converted into RGB color space. Finally, the output of the apple image is the target enhancement image. This paper selects 30 apple night vision images collected under fluorescent lighting to make simulation experiment compared with histogram equalization algorithm, homomorphic filtering algorithm and Retinex algorithm based on bilateral filtering. From the visual effects, the 4 methods have a certain degree of enhancement. By using the histogram equalization algorithm, not only the brightness has improved greatly, but also the reflective part of the apple is magnified. And the apple in the dark area is not fully displayed. After using the homomorphic filtering algorithm, the night vision image is enhanced and the enhancement effect of the highlight reflective area is poorer. After using the bilateral filtering algorithm, the edge is also maintained as this proposed algorithm, but there is a circle of white halo at the area of the apple gradient mutation. However, after using the proposed algorithm to enhance the images, the apple fruit is more prominent. Its details are clearly visible in the dark areas and there is no phenomenon of over-enhancement. There are more obvious visual effect and clearer outline of the target fruit, and the halo part has been well suppressed. According to the objective performance indices of experiment results, it shows that the mean grey value of the 30 images after processed by the proposed method, compared with the original image, histogram equalization algorithms, homomorphic filtering algorithm and Retinex algorithm based on bilateral filtering, increases averagely by 230.34%, 251.16%, 14.56% and 7.75%, respectively, the standard deviation on average increases by 36.90%, -23.95%, 53.37% and 28.00%, respectively, the information entropy increases averagely by 65.88%, 99.68%, 66.85% and 17.53%, respectively, and the gradient on average increases by 161.70%, 64.71%, 139.89% and 17.70%, respectively. The enhancement effect of proposed algorithm is superior to other 3 algorithms. In addition, compared with the Retinex algorithm based on bilateral filtering, the proposed algorithm has an average reduction of 74.56% in processing time, which reflects the timeliness and efficiency. In conclusion, this algorithm has an unique advantage for night vision image enhancement. So it can satisfy the actual demands and realize the continuous operation of apple harvesting robot at night. © 2016, Chinese Society of Agricultural Engineering. All right reserved. Source


Zheng Z.Y.,Jiangsu University | Zhao D.A.,Jiangsu University | Zhao D.A.,Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry | Sun Y.P.,Jiangsu University | And 2 more authors.
Applied Mechanics and Materials | Year: 2014

This paper introduces an automatic feeding ship that applied in breeding crabs. For realizing autonomous navigation and making feed uniform on the surface of the aquaculture ponds, the GPS and automatic feeding machine are used on the ship. Bait scatters movement was analyzed by the method of physics and geometry which based on the structure and parameters of automatic feeding machine. For getting the mathematical model of bait distribution, the least square method is adopted to fit the experimental data. On this basis, the minimum variance of the bait thickness was selected as the objective function. An optimal trajectory was generated by using golden section method. © (2014) Trans Tech Publications, Switzerland. Source


Ji W.,Jiangsu University | Ji W.,Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry | Luo D.,Jiangsu University | Li J.,Jiangsu University | And 3 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2014

In order to minimize the harm due to robot end-effector grasp fruits and vegetables, a generalized proportional integral (GPI) output feedback control scheme is proposed in this paper. First, the study object is the end-effector with two fingers that mounted on the top of a rigid manipulator, force sensors are mounted at the root of the finger, the output force signal was obtained by a force sensor, treating the model of the end-effector as a particle model and gravity does not affect the dynamics of end-effector, so the effect of gravity can be disregarded in the modeling. The modeling of the motor and the end-effector respectively and combining them together, the integrated model of end-effector is obtained, the mathematical relationship between the motor input voltage, and the load torque is also obtained. Then, the designed integral reconstructor, taking ramp error into consideration, the system gains robust characteristics to the ramp error that consists of force tracking error and constant disturbance, the integral reconstructor does not change the characteristic of the closed-loop. The computation of time derivatives can cause many common problems, such as a noisy signal, output delays, etc. In order to avoid these problems, making full use of integral reconstructor to design GPI force feedback controller, the force deviation converted to the input voltage control of the motor, this way needn't to derivate torque tracking error, so avoiding system time-delay and noise problem results from derivation. Making use of simulation software of MATLAB, taking sine signal and ramp signal as reference forces respectively, the force trajectory tracking error and the force output are analysed, the simulation result shows that the unit of force tracking error is 10-3, the real output force trajectory is close to the reference force trajectory, only having a little disturbance in the beginning, the output force trajectory reach a steady tracking status within 1 s, then the real output force trajectory smoothly tracks the reference force trajectory. Simulation results show that the GPI torque control has good torque tracking capability, which can decrease the damage of fruits and vegetables caused by end-effector grasping, and it is suitable for compliance grasp control of fruit-vegetable. Generalized proportional integral controller was applied to a fruit-picking robot to grasp apples and pears, after several the experiment of grasping fruits, the experimental results indicated that the method has a good performance and the fruit-picking robot can grasp and release fruit steadily and harmlessly. The ratio of successful grasping is 90%. Comparing with the situation that adopted traditional proportional integral control, the damage of fruits and vegetables caused by the end-effector grasping is reduced, fulfilling the demand of fruit picking. The research can provide a reference for the nondestructive picking of fruit-vegetable picking robot. Source

Discover hidden collaborations