Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Cooperation of unmanned systems for agricultural applications: A theoretical framework

Agriculture 4.0 comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management with the objective of optimising production by accounting for variabilities and uncertainties within agricultural systems. Autonomous ground and aerial vehicles can lead to favourable improvements in management by performing in-field tasks in a time-effective way. In particular, greater benefits can be achieved by allowing cooperation and collaborative action among unmanned vehicles, both aerial and ground, to perform in-field operations in precise and time-effective ways. In this work, the preliminary and crucial step of analysing and understanding the technical and methodological challenges concerning the main problems involved is performed. An overview of the agricultural scenarios that can benefit from using collaborative machines and the corresponding cooperative schemes typically adopted in this framework are presented. A collection of kinematic and dynamic models for different categories of autonomous aerial and ground vehicles is provided, which represents a crucial step in understanding the vehicles behaviour when full autonomy is desired. Last, a collection of the state-of-the-art technologies for the autonomous guidance of drones is provided, summarising their peculiar characteristics, and highlighting their advantages and shortcomings with a specific focus on the Agriculture 4.0 framework. A companion paper reports the application of some of these techniques in a complete case study in sloped vineyards, applying the proposed multi-phase collaborative scheme introduced here

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

Optimizing the performance of a wheeled mobile robots for use in agriculture using a linear-quadratic regulator

Use of wheeled mobile robot systems could be crucial in addressing some of the future issues facing agriculture. However, robot systems on wheels are currently unstable and require a control mechanism to increase stability, resulting in much research requirement to develop an appropriate controller algorithm for wheeled mobile robot systems. Proportional, integral, derivative (PID) controllers are currently widely used for this purpose, but the PID approach is frequently inappropriate due to disruptions or fluctuations in parameters. Other control approaches, such as linear-quadratic regulator (LQR) control, can be used to address some of the issues associated with PID controllers. In this study, a kinematic model of a four-wheel skid-steering mobile robot was developed to test the functionality of LQR control. Three scenarios (control cheap, non -zero state expensive; control expensive, non -zero state cheap; only non -zero state expensive) were examined using the characteristics of the wheeled mobile robot. Peak time, settling time, and rising time for cheap control based on these scenarios was found to be 0.1 s, 7.82 s, and 4.39 s, respectively

Visual Servoing in Robotics

Visual servoing is a well-known approach to guide robots using visual information. Image processing, robotics, and control theory are combined in order to control the motion of a robot depending on the visual information extracted from the images captured by one or several cameras. With respect to vision issues, a number of issues are currently being addressed by ongoing research, such as the use of different types of image features (or different types of cameras such as RGBD cameras), image processing at high velocity, and convergence properties. As shown in this book, the use of new control schemes allows the system to behave more robustly, efficiently, or compliantly, with fewer delays. Related issues such as optimal and robust approaches, direct control, path tracking, or sensor fusion are also addressed. Additionally, we can currently find visual servoing systems being applied in a number of different domains. This book considers various aspects of visual servoing systems, such as the design of new strategies for their application to parallel robots, mobile manipulators, teleoperation, and the application of this type of control system in new areas

An efficient headland-turning navigation system for a safflower picking robot

This study proposes a navigation system for the headland autonomous turning of a safflower picking robot. The proposed system includes binocular cameras, differential satellites, and inertial sensors. The method of extracting the headland boundary line combining the hue, saturation, and value-fixed threshold segmentation method and random sample consensus algorithm and planning the headland-turning trajectory of a robot based on the multiorder Bezier curve are used as control methods. In addition, a headland-turning tracking model of a safflower picking robot is designed, and a path-tracking control algorithm is developed. A field test verifies the performance of the designed headland-turning navigation system. The test results show that the accuracy of the judgment result regarding the existence of a headland is higher than 96%. In headland boundary detection, the angle deviation is less than 1.5˚, and the depth value error is less than 50 mm. The headland-turning path tracking test result shows that at a turning speed of 0.5 km/h, the average lateral deviation is 37 mm, and the turning time is 24.2 seconds. Compared to the 1 km/h, the turning speed of 0.5 km/h provides a better trajectory tracking effect, but the turning time is longer. The test results verify that this navigation system can accurately extract the headland boundary line and can successfully realise the headland-turning path tracking of a safflower picking robot. The results presented in this study can provide a useful reference for the autonomous navigation of a field robot