Development of the electric automatic steering system for agricultural vehicles

Automatic guidance of agricultural vehicles requires automatic execution of operation commands received from the navigation controller by using electronically controlled mechanisms for wheel steering,speed changing and work implementing.Automatic steering contributes as a prerequisite technique in automatic and semi-automatic agricultural navigation.This research aimed to develop an electric automatic steering system that was compact in its structure and integrated into original steering mechanism in a simply and convenient way for aftermarket modification.A brushless motor and reducer assembly was utilized to provide an adequate steering torque instead of manual maneuver.A rapid assembling approach was proposed by passing the steering shaft through the hollow output shaft.A digital proportional-integral-differential(PID)algorithm was implemented to calculate the rotation speeds and directions by comparing the desired angle and the actual angle,which was implemented in a printed circuit board with a microcontroller unit(MCU)and interface chips.An unmanned wheeled tractor was applied as test platform to integrate the newly developed electric automatic steering system.Tests were conducted to evaluate its performance in terms of stability and responsiveness.An autonomous navigation system guided the tractor along target paths in the field by sending steering commands to the electric automatic steering system.The results show that the steering angle error was less than 0.81°when desired steering angle was less than 10°.The lateral error difference was no more than 4.76 cm when repeating following the same target path,which indicated that the electric automatic steering system responded accurately and robustly to steering commands.

Digital twins in smart farming:An autoware-based simulator for autonomous agricultural vehicles

Digital twins can improve the level of control over physical entities and help manage complex systems by integrating a range of technologies.The autonomous agricultural machine has shown revolutionary effects on labor reduction and utilization rate in field works.Autonomous vehicles in precision agriculture have the potential to improve competitiveness compared to current crop production methods and have become a research hotspot.However,the development time and resources required in experiments have limited the research in this area.Simulation tools in unmanned farming that are required to enable more efficient,reliable,and safe autonomy are increasingly demanding.Inspired by the recent development of an open-source virtual simulation platform,this study proposed an autoware-based simulator to evaluate the performance of agricultural machine guidance based on digital twins.Oblique photogrammetry using drones is used to construct threedimensional maps of fields at the same scale as reality.A communication format suitable for agricultural machines was developed for data input and output,along with an inter-node communication methodology.The conversion,publishing,and maintenance of multiple coordinate systems were completed based on ROS(Robot Operating System).Coverage path planning was performed using hybrid curves based on Bézier curves,and it was tested in both a simulation environment and actual fields with the aid of Pure Pursuit algorithms and PID controllers.

Review of research on agricultural vehicle autonomous guidance

A brief review of research in agricultural vehicle guidance technologies is presented.The authors propose the conceptual framework of an agricultural vehicle autonomous guidance system,and then analyze its device characteristics.This paper introduces navigation sensors,computational methods,navigation planners and steering controllers.Sensors include global positioning systems(GPS),machine vision,dead-reckoning sensors,laser-based sensors,inertial sensors and geomagnetic direction sensors.Computational methods for sensor information are used to extract features and fuse data.Planners generate movement information to supply control algorithms.Actuators transform guidance information into changes in position and direction.A number of prototype guidance systems have been developed but have not yet proceeded to commercialization.GPS and machine vision fused together or one fused with another auxiliary technology is becoming the trend development for agricultural vehicle guidance systems.Application of new popular robotic technologies will augment the realization of agricultural vehicle automation in the future.

Development of autonomous navigation controller for agricultural vehicles

Agricultural vehicles are adopted to undertake farming tasks by traversing along crop rows in the field.Working quality depends significantly on the driving skills of the operator.Automatic guidance has been introduced into agriculture to achieve high-accuracy path tracking during the last decades,which contributes considerably to straight-line navigation.The objective of this research was to develop an autonomous navigation controller that allowed movement autonomy for various agricultural vehicles.Three wheel-type vehicles were used as the test platform featuring automatic steering,hydrostatic transmission and speed control,which included a rice transplanter,a high-clearance sprayer and a tractor.A dual-antenna RTK-GNSS receiver was attached to the vehicles to provide spatial information on both positioning and heading by using the RTX service from Trimble.A path planning method was proposed to create a straight-line reference path by giving two points,and the target path was determined according to the vehicle initial status and working assignment.Headland turning was comprehensively taken into account by listing different turn patterns in order to realize autonomous navigation at the headland.The navigation controller hardware was fabricated for program execution,data processing and information communication with peripherals.A human-machine interface was designed for the operator to complete basic setting,path planning and navigation control by providing controls.Field experiments were conducted to evaluate the performance and versatility of the newly developed autonomous navigation controller in guiding agricultural vehicles to follow straight paths and turn at the headland.Results showed that an appropriate turn pattern was automatically executed when finishing straight-line navigation.The lateral error in straight-line tracking was no more than 6 cm,6 cm and 5 cm for the rice transplanter,the high-clearance sprayer and the tractor,respectively.And the maximum lateral RMS error was 3.10 cm,4.75 cm,2.21 cm in terms of straight-line tracking,which indicated that the newly developed autonomous navigation controller was versatile and of high robustness in guiding various agricultural vehicles.

Cascaded navigation control for agricultural vehicles tracking straight paths

In precision agriculture(PA),an agricultural vehicle navigation system is essential and the navigation control accuracy is important in this system.As straight path tracking is the major operating mode of agricultural vehicles on large fields,a cascaded navigation control method for straight path tracking is proposed in this study.Firstly,a cascaded navigation control structure for the agricultural vehicle was discussed.Based on this structure,the navigation control task was decomposed into two cascaded control tasks,namely,the path tracking control task and the steering control task.Secondly,a relative kinematics model of agricultural vehicles was deduced,and an optimal Proportional-Derivative(PD)method based on the deduced model was developed in the path tracking control task.Then,an improved PD method based on a transition process was proposed in the steering control task to enhance the performance of the steering control subsystem.Finally,the effectiveness and the superiority of the proposed method were verified by a series of experiments.Results of the experimental data analysis show that mean value of the lateral position deviation is 0.02 m and standard deviation of the lateral position deviation is 0.04 m,which proves that the proposed method has achieved satisfactory effects on the straight path tracking of agricultural vehicles.

Improved inclination correction method applied to the guidance system of agricultural vehicles

This research introduces a new inclination correction method with increased accuracy applied to the guidance system of an agricultural vehicle.The method considers the geometry of a robot tractor and uses an Inertial Measurement Unit(IMU)to correct the lateral error of the RTK-GPS antenna measurements raised by the tractor's inclinations.A parameters optimization experiment and an automatic guidance experiment under real working conditions were used to compare the accuracy of a traditional correction method with the new correction method,by calculating the RMSE of the lateral error and the error reduction percentage.An additional tuned correction method was found by using a simple analytical method to find the optimal variables that reduced the lateral error to a minimum.The results indicate that the new correction method and the tuned correction method display a significant error reduction percentage compared to the traditional correction method.The methods could correct the GPS lateral error caused by the roll inclinations in real-time.The resulting lateral deviation caused by the tractor's inclinations could be reduced up to 23%for typical travelling speeds.

Extraction of straight field roads between farmlands based on agricultural vehicle-mounted LiDAR

The application of autonomous agricultural vehicles is gaining popularity as a way to increase production efficiency and lower operational costs.To achieve high performance,perception tasks(such as obstacle detection,road extraction,and drivable area extraction)are of great importance.Compared with structured roads,field roads between farmlands,including unstructured roads and semi-structured roads,are unfavorable for autonomous agricultural vehicle driving due to their bumpiness and unstructured nature.This study proposed an extraction method for the straight field roads between farmlands.The proposed method was based on the point cloud data acquired by LiDAR(Velodyne VLP-16)mounted on a John Deere 12046B-1204 tractor.The proposed method has three aspects:Euclidean Clustering-based extraction,boundary-based extraction,and road point cloud curve segment modification.Firstly,Euclidean Clustering with K-Dimensional(KD)-Tree data structure was adopted to extract the road curve segments close to the LiDAR composed of road points.Secondly,the boundary lines constraint was constructed to extract the distant road curve segments.Thirdly,the local distance ratio was used to modify the extracted road curve segments.The average extraction accuracy for both semi-structured and unstructured roads exceeded 98%,and the false positive rate(FPR)was less than 0.5%.These experimental findings demonstrated that the proposed road extraction method was precise and effective.The proposed method of this study can be applied to enhance the perception ability of autonomous agricultural vehicles thereby increasing the efficiency and safety of field road driving.

Modeling and test on height adjustment system of electrically-controlled air suspension for agricultural vehicles

To reduce the damages of pavement,vehicle components and agricultural product during transportation,an electric control air suspension height adjustment system of agricultural transport vehicle was studied by means of simulation and bench test.For the oscillation phenomenon of vehicle height in driving process,the mathematical model of the vehicle height adjustment system was developed,and the controller of vehicle height based on single neuron adaptive PID control algorithm was designed.The control model was simulated via Matlab/Simulink,and bench test was conducted.Results show that the method is feasible and effective to solve the agricultural vehicle body height unstable phenomenon in the process of switching.Compared with other PID algorithms,the single neuron adaptive PID control in agricultural transport vehicle has shorter response time,faster response speed and more stable switching state.The stability of the designed vehicle height adjustment system and the ride comfort of agricultural transport vehicle were improved.

Autonomous trajectory tracking control method for an agricultural robotic vehicle

To address the nonlinearities and external disturbances in unstructured and complex agricultural environments,this paper investigates an autonomous trajectory tracking control method for agricultural ground vehicles.Firstly,this paper presents the design and implementation of a lightweight,modular two-wheeled differential drive vehicle equipped with two drive wheels and two caster wheels.The vehicle comprises drive wheel modules,passive wheel modules,battery modules,a vehicle frame,a sensor system,and a control system.Secondly,a novel robust trajectory tracking method was proposed,utilizing an improved pure pursuit algorithm.Additionally,an Online Particle Swarm Optimization Continuously Tuned PID(OPSO-CTPID)controller was introduced to dynamically search for optimal control gains for the PID controller.Simulation results demonstrate the superiority of the improved pure pursuit algorithm and the OPSO-CTPID control strategy.To validate the performance,the vehicle was integrated with a seeding and fertilizing machine to realize autonomous wheat seeding in an agricultural environment.Experimental outcomes reveal that the vehicle of this study completed a seeding operation exceeding 1 km in distance.The proposed method can robustly and smoothly track the desired trajectory with an accuracy of less than 10 cm for the root mean square error(RMSE)of the curve and straight lines,given a suitable set of parameters,meeting the requirements of agricultural applications.The findings of this study hold significant reference value for subsequent research on trajectory tracking algorithms for ground-based agricultural robots.

A dynamic tire model based on HPSO-SVM

In order to accurately describe the force mechanism of tires on agricultural roads and improve the life cycle of agricultural tires,a tire-deformable terrain model was established.The effects of tread pattern,wheel spine,tire sidewall elasticity,inflation pressure and soil deformation were considered in the model and fitted with a support vector machine(SVM)model.Hybrid particle swarm optimization(HPSO)was used to optimize the parameters of SVM prediction model,of which inertia weight and learning factor were improved.To verify the performance of the model,a tire force prediction model of agricultural vehicle with the improved SVM method was investigated,which was a complex nonlinear problem affected by many factors.Cross validation(CV)method was used to evaluate the training precision accuracy of the model,and then the improved HPSO was adopted to select parameters.Results showed that the choice randomness of specifying the parameters was avoided and the workload of the parameter selection was reduced.Compared with the dynamic tire model without considering the influence of tread pattern and wheel spine,the improved SVM model achieved a better prediction performance.The empirical results indicate that the HPSO based parameters optimization in SVM is feasible,which provides a practical guidance to tire force prediction of agricultural transport vehicles.