Robust navigation control and headland turning optimization of agricultural vehicles

Autonomous agricultural robots have experienced rapid development during the last decade. They are capable of automating numerous field operations such as data collection, spraying, weeding, and harvesting. Because of the increasing demand of field work load and the diminishing labor force on the contrary, it is expected that more and more autonomous agricultural robots will be utilized in future farming systems. The development of a four-wheel-steering (4WS) and four-wheel-driving (4WD) robotic vehicle, AgRover, was carried out at Agricultural Automation and Robotics Lab at Iowa State University. As a 4WS/4WD robotic vehicle, AgRover was able to work under four steering modes, including crabbing, front steering, rear steering, and coordinated steering. These steering modes provided extraordinary flexibilities to cope with off-road path tracking and turning situations. AgRover could be manually controlled by a remote joystick to perform activities under individual PID controller of each motor. Socket based software, written in Visual C#, was developed at both AgRover side and remote PC side to manage bi-directional data communication. Safety redundancy was also considered and implemented during the software development. One of the prominent challenges in automated navigation control for off-road vehicles is to overcome the inaccuracy of vehicle modeling and the complexity of soil-tire interactions. Further, the robotic vehicle is a multiple-input and multiple-output (MIMO) high-dimensional nonlinear system, which is hard to be controlled or incorporated by conventional linearization methods. To this end, a robust nonlinear navigation controller was developed based on the Sliding Mode Control (SMC) theory and AgRover was used as the test platform to validate the controller performance. Based on the theoretical framework of such robust controller development, a series of field experiments on robust trajectory tracking control were carried out and promising results were achieved. Another vitally important component in automated agricultural field equipment navigation is automatic headland turning. Until now automated headland turning still remains as a challenging task for most auto-steer agricultural vehicles. This is particularly true after planting where precise alignment between crop row and tractor or tractor-implement is critical when equipment entering the next path. Given the motion constraints originated from nonholonomic agricultural vehicles and allowable headland turning space, to realize automated headland turning, an optimized headland turning trajectory planner is highly desirable. In this dissertation research, an optimization scheme was developed to incorporate vehicle system models, a minimum turning-time objective, and a set of associated motion constraints through a direct collocation nonlinear programming (DCNLP) optimization approach. The optimization algorithms were implemented using Matlab scripts and TOMLAB/SNOPT tool boxes. Various case studies including tractor and tractor-trailer combinations under different headland constraints were conducted. To validate the soundness of the developed optimization algorithm, the planner generated turning trajectory was compared with the hand-calculated trajectory when analytical approach was possible. The overall trajectory planning results clearly demonstrated the great potential of utilizing DCNLP methods for headland turning trajectory optimization for a tractor with or without towed implements

Robust navigation control and headland turning optimization of agricultural vehicles

Autonomous agricultural robots have experienced rapid development during the last decade. They are capable of automating numerous field operations such as data collection, spraying, weeding, and harvesting. Because of the increasing demand of field work load and the diminishing labor force on the contrary, it is expected that more and more autonomous agricultural robots will be utilized in future farming systems. The development of a four-wheel-steering (4WS) and four-wheel-driving (4WD) robotic vehicle, AgRover, was carried out at Agricultural Automation and Robotics Lab at Iowa State University. As a 4WS/4WD robotic vehicle, AgRover was able to work under four steering modes, including crabbing, front steering, rear steering, and coordinated steering. These steering modes provided extraordinary flexibilities to cope with off-road path tracking and turning situations. AgRover could be manually controlled by a remote joystick to perform activities under individual PID controller of each motor. Socket based software, written in Visual C#, was developed at both AgRover side and remote PC side to manage bi-directional data communication. Safety redundancy was also considered and implemented during the software development. One of the prominent challenges in automated navigation control for off-road vehicles is to overcome the inaccuracy of vehicle modeling and the complexity of soil-tire interactions. Further, the robotic vehicle is a multiple-input and multiple-output (MIMO) high-dimensional nonlinear system, which is hard to be controlled or incorporated by conventional linearization methods. To this end, a robust nonlinear navigation controller was developed based on the Sliding Mode Control (SMC) theory and AgRover was used as the test platform to validate the controller performance. Based on the theoretical framework of such robust controller development, a series of field experiments on robust trajectory tracking control were carried out and promising results were achieved. Another vitally important component in automated agricultural field equipment navigation is automatic headland turning. Until now automated headland turning still remains as a challenging task for most auto-steer agricultural vehicles. This is particularly true after planting where precise alignment between crop row and tractor or tractor-implement is critical when equipment entering the next path. Given the motion constraints originated from nonholonomic agricultural vehicles and allowable headland turning space, to realize automated headland turning, an optimized headland turning trajectory planner is highly desirable. In this dissertation research, an optimization scheme was developed to incorporate vehicle system models, a minimum turning-time objective, and a set of associated motion constraints through a direct collocation nonlinear programming (DCNLP) optimization approach. The optimization algorithms were implemented using Matlab scripts and TOMLAB/SNOPT tool boxes. Various case studies including tractor and tractor-trailer combinations under different headland constraints were conducted. To validate the soundness of the developed optimization algorithm, the planner generated turning trajectory was compared with the hand-calculated trajectory when analytical approach was possible. The overall trajectory planning results clearly demonstrated the great potential of utilizing DCNLP methods for headland turning trajectory optimization for a tractor with or without towed implements.</p

Headland Turning Optimisation for Agricultural Vehicles and Those with Towed Implements

As an essential part of field coverage path planning process, mobile agricultural field equipment headland turning is a process that should be done in a manner that can maximise the equipment’s operational efficiency through minimising the time or travel distance during the turning. However, this headland turning trajectory optimisation task represents a challenging dynamic nonlinear optimisation problem which is difficult to solve by using traditional indirect numerical methods. In this research, we investigated the possibility of using direct numerical methods to solve such a nonlinear optimisation problem in a restricted parameter neighborhood with constraints. We developed the kinematic models of the tractor and the tractor-implement(s) systems and formulated their headland turning optimisation problems through incorporating their models and the operational constraints. A range of headland turning scenarios from symmetrical bulb turn to fishtail turn and to turns with single and double trailers. With integration of the tractor and trailer models and by implementing the optimization process with the TOMLAB/SNOPT software tool, results for diverse circumstances of the tractor/trailer headland turning scenarios were generated and illustrated in this paper.This is a manuscript of an article published as Tu, Xuyong, and Lie Tang. "Headland Turning Optimisation for Agricultural Vehicles and Those with Towed Implements." Journal of Agriculture and Food Research (2019): 100009. DOI: 10.1016/j.jafr.2019.100009. Posted with permission.</p

Robust navigation control of a 4WD/4WS agricultural robotic vehicle

This paper reports the development of a robust controller based on backstepping sliding mode control (SMC) for a four-wheel-steering (4WS) and four-wheel-drive (4WD) agricultural robotic vehicle. Two RTK-GPS receivers were installed on the vehicle to provide feedback of the robot’s location and orientation. Kinematic models of front wheel steering and coordinated steering modes and their corresponding navigation controllers were developed. Diverse reference trajectories were designed and used to validate the controller performance. The experimental results demonstrated the capability and robustness of the developed SMC navigation controller in controlling a nonholonomic system that has a high degree of freedom.This is a manuscript of an article published as Tu, Xuyong, Jingyao Gai, and Lie Tang. "Robust navigation control of a 4WD/4WS agricultural robotic vehicle." Computers and Electronics in Agriculture 164 (2019): 104892. DOI: 10.1016/j.compag.2019.104892. Posted with permission.</p

A real-time automated system for monitoring individual feed intake and body weight of group housed turkeys

Feed conversion is an important production trait in turkey breeding; the measurement of an individual bird's feed efficiency is important in identifying the most genetically superior individual. The development of a real-time automated feed intake and body weight monitoring system is described in this paper. The system integrated multiple feed and body weight weighing stations consisting of electronic scales, radio frequency identification (RFID) devices and data communication modules. A distributed and client-server-based system architecture with multi-threading software design was developed. This system architecture allowed for a real-time data acquisition capability when a large number of stations were required. A structured query language (SQL) database management system was developed to record and manage the dynamic feed intake and body weight gain data of individual birds. The developed system also offers a powerful research tool for studying poultry feeding behavior under group housing conditions. Published by Elsevier B.V