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

Autonomous guided vehicle for agricultural application

With the world's population expected to reach nine billion by 2050, agricultural production will have to double to meet this growing demand. Hence, a need for better infrastructure to enhance farming efficiency becomes apparent. There are a number of solutions that have been developed to date that are commercially available. They range from genetically modified seeds and bio/green fertilizers to advanced farming machinery amongst others. However most of the farming equipment developed has drawbacks such as: heavy weight – this leads to reduced yields due to soil compacting; human dependency – constant monitoring and controlling is needed; light dependency – excludes usage during the night or when visibility is poor. Therefore, a possible solution will be researched to enhance the evolution of farming equipment. Furthermore, a model will be developed for testing and verifying the research

IMPLEMENTATION OF KALMAN FILTER TO TRACKING CUSTOM FOUR-WHEEL DRIVE FOUR-WHEEL-STEERING ROBOTIC PLATFORM

Vehicle tracking is an important component of autonomy in the robotics field, requiring integration of hardware and software, and the application of advanced algorithms. Sensors are often plagued with noise and require filtering. Additionally, no single sensor is sufficient for effective tracking. Data from multiple sensors is needed in order to perform effective tracking. The Kalman Filter provides a convenient and efficient solution for filtering and fusing sensor data as well as estimating noise error covariances. Consequently, it has been essential in tracking algorithms since its introduction in 1960. This thesis presents an application of the Kalman filter to tracking of a custom four-wheel-drive four-wheel-steering vehicle using a limited sensor suite. Sensor selection is discussed, along with the characteristics of the sensor noise as related to meeting the requirements of the Kalman filter for guaranteeing optimality. The filter requires the development of a dynamical model, which is derived using empirical data methods and evaluated. Tracking results are presented and compared to unfiltered data

Navigation Errors Introduced By Ground Vehicle Dynamics

An analysis of navigational accuracy when influenced by ground vehicle dynamics is presented. Tests beds outfitted with various sensor suites were used to collect data when normal and extreme driving maneuvers are executed. The data was run through an extended Kalman filter to produce a navigation solution. The Kalman filter inputs varied on each test bed, using both automotive and tactical grade Inertial Measurement Units (IMU). The position, velocity, and course measurements were obtained from a DGPS unit mounted on the vehicles and used as a truth measurement when exploring dead reckoning error. Additional measurements, such as wheel speed, radar speed, and magnetometer heading, were added to improve the robustness and reliability of the solution. The results of the work show the effect of both longitudinal and lateral vehicle slip on the navigation solution. In addition, the attempt of the various sensors to correct the errors is investigated. Reprinted with permission from The Institute of Navigation (http://ion.org/) and The Proceedings of the 18th International Technical Meeting of the Satellite Division of The Institute of Navigation, (pp. 302-316). Fairfax, VA: The Institute of Navigation

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

The integration of GPS and visual navigation for autonomous navigation of an Ackerman steering mobile robot in cotton fields

Autonomous navigation in agricultural fields presents a unique challenge due to the unpredictable outdoor environment. Various approaches have been explored to tackle this task, each with its own set of challenges. These include GPS guidance, which faces availability issues and struggles to avoid obstacles, and vision guidance techniques, which are sensitive to changes in light, weeds, and crop growth. This study proposes a novel idea that combining GPS and visual navigation offers an optimal solution for autonomous navigation in agricultural fields. Three solutions for autonomous navigation in cotton fields were developed and evaluated. The first solution utilized a path tracking algorithm, Pure Pursuit, to follow GPS coordinates and guide a mobile robot. It achieved an average lateral deviation of 8.3 cm from the pre-recorded path. The second solution employed a deep learning model, specifically a fully convolutional neural network for semantic segmentation, to detect paths between cotton rows. The mobile rover then navigated using the Dynamic Window Approach (DWA) path planning algorithm, achieving an average lateral deviation of 4.8 cm from the desired path. Finally, the two solutions were integrated for a more practical approach. GPS served as a global planner to map the field, while the deep learning model and DWA acted as a local planner for navigation and real-time decision-making. This integrated solution enabled the robot to navigate between cotton rows with an average lateral distance error of 9.5 cm, offering a more practical method for autonomous navigation in cotton fields

Sensors and Actuators Communication and Synchronization for a Mobile Manipulator

Modern mobile manipulator hardware architecture is combination of mechanical, electrical, software and control units. Integrating numbers of mechanical and electrical components in the system rise the number of parameters to control. Hence the issues of controlling such systems to achieve the best performance is critical. A key aspect for this purpose is integration of sensors and actuators to provide a low level of mobile manipulator control and my thesis involve in providing a low level of control for iMoro mobile manipulator. To operate such a mobile manipulator several intelligent components needs to be installed and cooperate at same time. Due to the application that iMoro made for, the platform is equipped with 8 actuators and number of sensors. Therefore control of such a system is complicated and demands accurate synchronization and communication among four legs. Since iMoro robot is equipped with IMU, in following chapter IMU is calibrated and the process of providing meaningful data out of raw data explained by modeling the IMU. In addition, an equation is developed for robust calibration of IMU and camera

Toward a robot swarm protecting a group of migrants

Different geopolitical conflicts of recent years have led to mass migration of several civilian populations. These migrations take place in militarized zones, indicating real danger contexts for the populations. Indeed, civilians are increasingly targeted during military assaults. Defense and security needs have increased; therefore, there is a need to prioritize the protection of migrants. Very few or no arrangements are available to manage the scale of displacement and the protection of civilians during migration. In order to increase their security during mass migration in an inhospitable territory, this article proposes an assistive system using a team of mobile robots, labeled a rover swarm that is able to provide safety area around the migrants. We suggest a coordination algorithm including CNN and fuzzy logic that allows the swarm to synchronize their movements and provide better sensor coverage of the environment. Implementation is carried out using on a reduced scale rover to enable evaluation of the functionalities of the suggested software architecture and algorithms. Results bring new perspectives to helping and protecting migrants with a swarm that evolves in a complex and dynamic environment

Autonomous navigation of a wheeled mobile robot in farm settings

This research is mainly about autonomously navigation of an agricultural wheeled mobile robot in an unstructured outdoor setting. This project has four distinct phases defined as: (i) Navigation and control of a wheeled mobile robot for a point-to-point motion. (ii) Navigation and control of a wheeled mobile robot in following a given path (path following problem). (iii) Navigation and control of a mobile robot, keeping a constant proximity distance with the given paths or plant rows (proximity-following). (iv) Navigation of the mobile robot in rut following in farm fields. A rut is a long deep track formed by the repeated passage of wheeled vehicles in soft terrains such as mud, sand, and snow. To develop reliable navigation approaches to fulfill each part of this project, three main steps are accomplished: literature review, modeling and computer simulation of wheeled mobile robots, and actual experimental tests in outdoor settings. First, point-to-point motion planning of a mobile robot is studied; a fuzzy-logic based (FLB) approach is proposed for real-time autonomous path planning of the robot in unstructured environment. Simulation and experimental evaluations shows that FLB approach is able to cope with different dynamic and unforeseen situations by tuning a safety margin. Comparison of FLB results with vector field histogram (VFH) and preference-based fuzzy (PBF) approaches, reveals that FLB approach produces shorter and smoother paths toward the goal in almost all of the test cases examined. Then, a novel human-inspired method (HIM) is introduced. HIM is inspired by human behavior in navigation from one point to a specified goal point. A human-like reasoning ability about the situations to reach a predefined goal point while avoiding any static, moving and unforeseen obstacles are given to the robot by HIM. Comparison of HIM results with FLB suggests that HIM is more efficient and effective than FLB. Afterward, navigation strategies are built up for path following, rut following, and proximity-following control of a wheeled mobile robot in outdoor (farm) settings and off-road terrains. The proposed system is composed of different modules which are: sensor data analysis, obstacle detection, obstacle avoidance, goal seeking, and path tracking. The capabilities of the proposed navigation strategies are evaluated in variety of field experiments; the results show that the proposed approach is able to detect and follow rows of bushes robustly. This action is used for spraying plant rows in farm field. Finally, obstacle detection and obstacle avoidance modules are developed in navigation system. These modules enables the robot to detect holes or ground depressions (negative obstacles), that are inherent parts of farm settings, and also over ground level obstacles (positive obstacles) in real-time at a safe distance from the robot. Experimental tests are carried out on two mobile robots (PowerBot and Grizzly) in outdoor and real farm fields. Grizzly utilizes a 3D-laser range-finder to detect objects and perceive the environment, and a RTK-DGPS unit for localization. PowerBot uses sonar sensors and a laser range-finder for obstacle detection. The experiments demonstrate the capability of the proposed technique in successfully detecting and avoiding different types of obstacles both positive and negative in variety of scenarios

Design, Deployment, Navigation, and Control of Mobile Robots for Perception and Sensor Data Collection

Aerial robots, including rotary-wing and fixed-wing unmanned aerial vehicles or UAVs, have shown great capabilities in surveying as well as search and rescue from above. However, either rotary-wing or fixed-wing UAVs have nearly insoluble flaws. In order to overcome the under-actuating nature of multi-rotor UAVs, Chapter 2 proposes modeling methods and control schemes for fully-actuated hexacopters. Additionally, rotary-wing robots suffer from limited battery life as well as lack of fail-safe mechanism upon losing motors, while fixed-wing robots lacks the ability to take off and land vertically. Therefore, Chapter 4 proposes a bio-inpired hybrid aerial robot to extend mutli-rotor flight time and fail-safe capability and provide fixed-wing glider with vertical take-off and landing or VTOL capability. Moreover, to extend the flight time and optimize the energy consumption of multi-rotor UAVs, Chapter 3 proposes a multi-disciplinary design optimization based flight trajectory optimizer involving linear rotor inflow models to reduce flight time or energy consumption of specific missions.In terms of unmanned ground vehicles or UGVs used for perception and mapping, there has been a research gap to provide a low-cost, highly agile over-actuated chassis design. Chapter 5 proposes a 3D-printable double Ackermann steering chassis design with 2-wheel standing and balancing capability to fill in this gap. Chapter 6, on the other hand, proposes the system design of a UGV capable of performing perception and mapping in a limited lighting, unstructured, and GPS-denied environment based on a nevertheless nonholonomic chassis, where primary concern becomes the reliability in performing real-time mapping and preservation of solely static environment.The last but not least topic discussed in this dissertation is to promote the role of UAV imagery in earthquake response. In Chapter 7 we combine the traditional UAV plan view perspective with north and east elevation view video data to provide motion estimation in all 6 degrees of freedom, as well as proposing Video Transformer for motion tracking.All in all, with attempts to expand and promote the designs, deployment and control schemes of both aerial and ground mobile robots, this dissertation strives to provide case study results and state-of-the-art methods for future robotics studies