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The middle and lower reaches of the Yangtze River are the main producing areas of winter rapeseed in China. However, due to the effects of harvesting agricultural crops, heavy soil compaction, small field, the mechanization level of rapeseed is low. The level of intelligent seeding needs to be improved. In this paper, a navigation controller for small crawler-type seeder based on immune PID was designed to solve the problem of intelligent seeding in the middle and lower reaches of the Yangtze River where the soil is sticky, the field is small. The small crawler-type rape seeder automatic navigation control system was designed. It used the small crawler-type rape seeder as the platform, and its steering system was modified by electromagnet. The high-precision BeiDou positioning module and the electronic compass was used for combined navigation to obtain the position and heading information of the crawler-type rape seeder as the input of the navigation controller. The immune PID controller was a nonlinear P controller that could adjust the proportional coefficient according to the feedback in real time. The control process was as follows. First, the plane coordinates of the start and end points of the straight path to be tracked are artificially set, and the main controller automatically calculated the distance of the target heading the start point and the end point. The weighted average algorithm was used to filter the longitude and latitude coordinates collected four times to get the current position. The controller calculated the distance deviation from the current position to the set route by the point-to-line distance formula. Taking the filtered distance deviation as an input, the target heading was calculated using the target heading correction formula distance deviation. Then, the course deviation (the deviation between the corrected target heading and the heading acquired by the electronic compass in real time) is used as an input of the immune PID controller, and the immune PID controller determines the Duty Ratio as the output u to act on the steering mechanism, achieving control of the steering of the track-type seeder. In addition, According to the characteristics of the tracked vehicle, the steering of the small crawler-type rape seeder was analyzed, and its steering model and steering angle transfer function were established. The immune PID controller was verified by Matlab simulation and experiment. Under the same parameters, the simulation showed that, compared with the conventional PID control, the immune PID control had the characteristics of fast response, small overshoot and small average tracking error. The pavement experiment showed that when the vehicle speed was 0.50 m/s, the average absolute deviation of the linear tracking of the immune PID controller was 4.2 cm, and the maximum deviation was not more than 12.0 cm. The field experiment showed that when the vehicle speed was 0.50 m/s, the average absolute deviation of the linear tracking of the immune PID controller was 5.8 cm, and the maximum deviation was not more than 15.2 cm, which was 32.6% and 18.1% higher than the conventional PID. It could better meet the requirements of the seeder navigation operation. This research provides a technical reference for the autonomous navigation of the crawler-type rape seeder. [ABSTRACT FROM AUTHOR]

It is difficult to establish Jacobian matrix and determine the coordinate frames of links for non-standard actuator. A new analytical method to establish the Jacobian matrix and determine the coordinate frames for joints and links are proposed in this paper. The proposed method made the positioning analysis of end-effector easier in space. At the same time, it is necessary to prove the effectiveness of the proposed method theoretically and verify the localization and configuration capabilities through simulations. First of all, forward kinematics model was set up based on a non-standard five Degree Of Freedom (5-DOF) actuator. A frame transformation is performed from base coordinate to end-effector coordinate. The relation between two adjacent joints is defined by a homogenous pose matrix. Secondly, the necessary and sufficient conditions for comprehensive localization are derived. They can guide the actuator to perform various tasks, such as tracking, assembly and autonomous grasping. A 5-DOF actuator is considered here as an example and this holds good for any N-DOF. Thirdly, inverse kinematics solutions are obtained by using artificial Neural Network (NN) based on back-propagation Multi-Layer Perceptron (MLP, multilayer perceptron NN) and are not unique. A unique solution using nonlinear minimization optimization is found. A NN based on supervisory learning method including three inputs, twenty neurons and five outputs has been used. Excitation function tansigmoid and linear excitation function pureline are in hidden and outer layers respectively. In Cartesian coordinate space, NN is trained by means of Levenberg Marquardt (LM) algorithm. The training sets used are Denav Hartenberg (DH) parameters and Cartesian coordinates. The weights are updated continuously which reduces the Mean Square Error (MSE) gradually. When MSE reaches the threshold set up, NN training will be terminated. After training, the test sets are used to examine the capability of NN. Fourthly, there are two evaluation functions viz., localization and cost functions. The localization function is defined to evaluate the positioning property of end-effector. At the same time, in task space, it will check whether the actuator has reached the target point along the direction needed or not. The cost function is defined to evaluate the kinematics configuration. There is a great relevance between cost function and Jacobian matrix. Velocity mapping from each joint to the end-effector was described by Jacobian matrix. So the cost function could give expression for kinematic configuration. At the end, simulations and experiments are conducted. The settings include industrial computer UNO2184G, 5-DOF non-standard actuator, Windows 7, MATLAB2012a. Coordinate frames for each joint are established and D-H parameters are determined. Then relative pose matrix is obtained between each of the two adjacent joints. Initial end-effector pose is obtained following right multiplication rule. The end-effector space range is formed under each joint operation range. Then, simulation is performed using NN, obtained localization and cost functions. The following results are obtained. The rank of Jacobian matrix is equal to 5. Therefore, this actuator met necessary and sufficient conditions for comprehensive positioning. NN method for solving inverse kinematics has reduced the computational complexity compared to conventional method. There are 21 groups of solutions when positioning to (41.4, 89.0, 104.5). The optimal solution obtained is (21.61, 91.44, 135.52, 221.42, 0) according to localization function rule. The optimal solution obtained according to cost function rule is (21.61, 125.73, 108.42, 221.99.41, 0). NN accuracy is 89.9% (approximately) while conventional method is 87.5%. By approximate estimation, the errors for θ1, θ2, θ3, θ4 and θ5 are 3.7°, 3.1°, 3.5°, 3.3°and 4.5°respectively. NN used 1. 2 seconds while conventional method completed in 0.9 seconds. Therefore, computation accuracy has improved by 20% and efficiency by 2.4%. If the system is linear, the conventional method is chosen when less demand in real-time. In contrast, if the system is nonlinear, new method proposed in this paper is chosen when more demand in real-time. The minimum value of localization function is 0.96. The maximum value of cost function is 4.0349×1014. These two parameters decide the comprehensive positioning and the kinematics configuration. From the results presented, it can be concluded that the non-standard actuator with MLP has better localization and optimal configuration. [ABSTRACT FROM AUTHOR]

Automatic driving for agricultural vehicle has become a research hotspot. Automatic driving technology includes vehicle position technology and automatic steering technology. Vehicle position technology usually uses the global positioning system (GPS) to realize. Automatic steering technology is realized by controlling vehicle lateral motion which can keep vehicle driving in a desired trajectory. So the result of automatic steering control directly embodies the intelligent behavior of agricultural vehicle. Recently more and more papers have proposed all kinds of algorithms to realize agricultural vehicle automatic driving. All of them show that in straight path tracking experiments, when the velocity is 1 m/s, the maximum error is 5 cm; in circular path tracking experiments, when the velocity is 1 m/s, the maximum error is 10 cm. With the increase of velocity, the tracking error is bound to further increase. To improve the precision, iterative learning control has been applied to path tracking. This method can achieve an almost perfect tracking performance in theory. But iterative learning control is an open-loop control which cannot effectively deal with interference. Model predictive control is a new control method which has attracted more and more researchers to study. The method can resolve the problem with constraints using the rolling optimization technique. It can not only predict the next-time system state, but also minimize tracking errors. With the rolling optimization technique and feedback adjustment, it can also overcome some uncertainty interferences. So to improve the precision of navigation control system for agricultural vehicle, an intelligent method of path tracking based on linear time-varying model predictive control is proposed. Although agricultural vehicle model usually has high nonlinearity, the method selects the linear timing-varying error model of the dynamic model as the prediction model of model predictive control to improve operation velocity. Objective function that selects control increment as state variable is established and relaxation factor is used to ensure the feasibility of solution. With the design of the constraints of control variable, control increment variable and state variable, the solution of objective function is changed into quadratic programming problem with some constraints which is solved using interior point method. After selecting the first element of control input increment as the input signal and repeating the above process to achieve the optimal control, model predictive controller is designed based on MATLAB/Simulink. Experimental results of straight and circular path tracking show that the controller can realize perfect tracking of straight path; the average error of lateral direction under 1 m/s is 7.5 cm and that under 3 m/s is 10 cm in circular path tracking; the front wheel angle is always in constraint dimensions, which shows the controller has not only a higher tracking precision but also a better stability. Experimental results of different controller parameters show that increasing prediction horizon and control cycle can reduce the tracking error and front wheel angle changes, and control horizon does not affect the controller's response speed. Meanwhile ground experiments based on the designed controller have been also completed. Results show that in straight path tracking, when the tracking velocity is 1 m/s, the average error of lateral direction is 0.865 cm; in circular path tracking, when the tracking velocity is less than 1 m/s, the average error of lateral direction is less than 10 cm; with the increase of velocity, the tracking error is further increased and the main reason is that when the velocity is lower, the experimental vehicle is linear, which is agreed well with the prediction model (using linear timing-varying error model), but when the velocity increases, the nonlinear features of vehicle is apparent gradually, which is not agreed well with the prediction model. The solution is using nonlinear model predictive control but it has larger computation. In practical path tracking, the velocity of agricultural vehicle is generally less than 1 m/s, so the designed controller can meet practical requirements. [ABSTRACT FROM AUTHOR]

Thinning is required in fruit tree production to ensure high quality fruit and proper fruit size, which is one of the most labor-intensive practices. Mechanical blossom thinning is an environmental friendly alternative to chemical blossom thinning. Good tree training techniques are required to get high-level blossom removal for mechanical thinning. Electric flexible thinner can realize blossom thinning by rotating flexible strings. There is a challenge in mechanical thinning due to the varying shape of tree canopy. To improve the adaptability of the electric flexible thinner on fruit tree variety, canopy shape and size, it is important to develop a profiling control system. In this paper, the structure of the actuators for profiling action of the electric flexible thinner was proposed. The flexible thinner was suspended on the rear three-point hitch of a tractor and consisted of a vertical rotating spindle attached with several segments of nylon cords in horizontal direction. The spindle was rotated by a brushless direct current motor and its spatial position could be adjusted by translational screw and profile adjusting screw. The ball screws were driven and controlled via stepper motors, which could control the offset into the trees and the angle of the thinner. With the ball screws, the thinning spindle could be stretched and tilted to conform to the height and inclination of the tree canopy. The range measuring rod could map the canopy as it passed and was placed ahead of the spindle to avoid sensor collision with rotating cords. Two pairs of ultrasonic sensors suspended on the range measuring rod could provide the angle and lateral offset sensing of the flexible thinner position by sending infrared laser pulses out and measuring the round-trip flight time to the trees. The spindle rotation speed, screw displacement and thinning angle were adjusted by the electric motors. During the blossom thinning process, the centrifugal force on the panicles of the fruit tree was used to cause rotating nylon cords to thin blossom. The intensity of thinning could be controlled by changing the spindle rotation speed, tractor speed and string arrangement and other parameters. To get the key parameters of profiling control, the geometric relationship between flexible thinner and canopy edge was analyzed. Using the modular structure, the hardware and software of profiling control system were developed. The controller performed the planning task and calculated the number of pulses for stepper motors of ball screws between the current position of the thinner and the currently measured canopy. All field tests with the electric flexible thinner were conducted at a tractor speed of 1.93 km/h. The field test results of single litchi plant illustrated the position servo control performance of two ball screws of the electric flexible thinner. The measured dynamic mean errors of the translational screw and profile adjusting screw were 0.17 and 0.07 cm respectively. To improve the performance of profiling controller, it required to move the thinner quickly without affecting thinning speed and to reduce the control latency to acceptable levels. The experimental results show that the actuators have good position adjusting accuracy and the proposed profiling control system can meet the requirements of real-time control. [ABSTRACT FROM AUTHOR]

At present, egg collection device appears that the automatic degree is low, and cannot meet the needs of automatic egg production in China. In order to solve the problem, this paper designs an egg auto-picking device. Such device consists of egg transport device, shake correcting device, egg picking execution device and automatic control system. Egg transport device comprises silica gel tank conveyor belt, driving roller with optical encoder, driven roller, servo motor and conveyor belt clamping device. Egg transport device is used for collecting, storing and transferring eggs. Shake correcting device is composed of spring, pulse electromagnet and beat parts. Several shake correcting devices are installed in the correcting area under the conveyor belt with equal space. Shake correcting device is used for arranging eggs on conveyor belt by a disorderly state into an ordered arrangement state. Egg picking execution device includes parallel picking-up structure and control system. Egg picking execution device is used for picking up eggs and transferring them to plate. Parallel picking-up structure consists of active arm, slave arm and picking manipulator. Picking manipulator is composed of rotation steering gear, enhancement steering gear, silicone sucker, and vacuum holes. The working process of egg auto-picking device is as follows: when the eggs go into the picking area, the proximity sensor will sense the array of eggs on the conveyor belt. The received signals by MCU(microcontroller unit) are transferred to personal computer (PC) controller via RS232 serial port. PC controller receives signals and calculates real-time coordinates for each egg. The PCL-839 control card based on PC controls the stepper motor drive through a closed loop controller. When the driving arm motor moves, the vacuum sucker at the end of the picking manipulator will trace the eggs and eventually pick up them. Simultaneously, SCM(single chip microcomputer) receive signals sent by the PC controller, and the steering gear controls the vacuum sucker to rotate upward by 90°. In this way eggs can be made vertical. At last, the picking manipulator transfers eggs on a tray. The eggs auto-picking device achieves the automatically picking up the eggs on the assembly line, improves the efficiency of egg automatic picking and frees up the labor. Picking manipulator is a core component of egg auto-picking device, and directly affects the success rate of egg picking and the working efficiency. By analyzing the working process of picking manipulator, the enhancement steering gear’s output torque and the sucker’s suction pressure are determined. On this basis, suitable enhancement steering gear of picking manipulator and suction pump are selected. In the process of research, we focus on the structural design of picking manipulator, the manipulator path planning, the route tracking prediction and the picking-laying solutions. Finally, the prototype of automatic egg collection device is manufactured; at the same time, the test of the success rate of egg picking, the egg picking-up speed and the optimization picking-laying solution is conducted. The results show that automatic egg collection device has the advantages of simple working and reliable location. Egg picking success rate reaches 98.3%, and the fastest operating speed of picking up the eggs into the pan can reach 2.4 s. The shortest time of optimum picking-laying solution is 73.2 s when the execution device picks up 30 eggs. This conclusion provides the reference for the structural design of picking device in automatic egg production systems. [ABSTRACT FROM AUTHOR]