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Focusing on the problem that the visual detection algorithm of navigation path line in intelligent harvester robot is susceptible to interference and low accuracy, a navigation path detection algorithm based on improved random sampling consensus is proposed.

First, inverse perspective mapping was applied to the original images of rice or wheat to restore the three-dimensional spatial geometric relationship between rice or wheat rows. Second, set the target region and enhance the image to highlight the difference between harvested and unharvested rice or wheat regions. Median filter is used to remove the intercrop gap interference and improve the anti-interference ability of rice or wheat image segmentation. The third step is to apply the method of maximum variance to thresholding the rice or wheat images in the operation area. The image is further segmented with the single-point region growth, and the harvesting boundary corner is detected to improve the accuracy of the harvesting boundary recognition. Finally, fitting the harvesting boundary corner point as the navigation path line improves the real-time performance of crop image processing.

The experimental results demonstrate that the improved random sampling consensus with an average success rate of 94.6% has higher reliability than the least square method, probabilistic Hough and traditional random sampling consensus detection. It can extract the navigation line of the intelligent combine robot in real time at an average speed of 57.1 ms/frame.

In the precision agriculture technology, the accurate identification of the navigation path of the intelligent combine robot is the key to realize accurate positioning. In the vision navigation system of harvester, the extraction of navigation line is its core and key, which determines the speed and precision of navigation.

This paper aims to propose a new autonomous driving controller to calibrate the absolute heading adaptively. Besides, the second purpose of this paper is to propose a new angle-track loop with a mass regulator to improve the adaptability of the autonomous driving system under different loads and road conditions.

In this paper, the error model of heading is built and a new autonomous driving controller with heading adaptive calibration is designed. The new controller calculates the average lateral error by the self-adjusting interval window and calibrates the absolute heading through the incremental proportional–integral–derivative (PID) controller. A window-size adjustment strategy, based on the current lateral error and the derivative of lateral error, is proposed to improve both the transient and the steady-state responses. An angle-tracking loop with mass regulator is proposed to improve the adaptability of autonomous steering system under different loads and road conditions.

The experiment results demonstrate that this method can compensate the heading installation error and restrain the off-track error from 13.8 to 1.30 cm. The standard error of new controller is smaller than fuzzy-PID calibration controller and the accuracy of autonomous driving system is improved.

The accuracy of heading calibrated by the new controller is not affected by external factors and the efficiency of calibration is improved. As the model parameters of steering system can be obtained manually, the new autonomous steering controller has more simple structure and is easy to implement. Mass regulator is adjusted according to the road conditions and the mass of harvester, which can improve the system adaptability.

The purpose of this paper is to examine the potential of robots to contribute to producing food for the growing global population.

Following an introduction which provides an historical background to agricultural production and strategies, this paper considers today's use of robots in traditional agricultural practices. It then introduces precision farming (PF) concepts and discuses the potential role of robots in PF and cites a number of recent research activities. Finally, conclusions are drawn.

This paper shows that robots have so far played a minor role in traditional agriculture but new families of small, intelligent and autonomous robots could conduct a range of PF tasks. These would boost productivity and also yield economic and environmental benefits and contribute to more sustainable food production practices.

This paper puts forward the case for the use of small, intelligent robots in PF and argues that they could contribute significantly to global food production.

This work presents a robot prototype designed and built for a new aided fruit‐harvesting strategy in highly unstructured environments, involving human‐machine task distribution. The operator drives the robotic harvester and performs the detection of fruits by means of a laser range‐finder, the computer performs the precise location of the fruits, computes adequate picking sequences and controls the motion of all the mechanical components (picking arm and gripper‐cutter). Throughout this work, the specific design of every module of the robotized fruit harvester is presented. The harvester has been built and laboratory tests with artificial trees were conducted to check range‐finder’s localization accuracy and dependence on external conditions, harvesting arm’s velocity, positioning accuracy and repeatability; and gripper‐cutter performance. Results show excellent range‐finder and harvesting arm operation, while a bottleneck is detected in gripper‐cutter performance. Some figures showing overall performance are given.

This paper aims to illustrate the growing importance of agricultural robots by providing details of recent product developments and their applications.

Following a short introduction, this first discusses a range of agricultural applications of drones. It then provides details of a selection of mobile field robots and their applications. Finally, concluding comments are drawn.

Commercially available aerial and terrestrial robots are playing a rapidly growing role in a diversity of agricultural practices. Key capabilities and benefits include detecting crop stress and disease, predicting crop yields, reducing agrochemical use, overcoming manpower shortages and reducing labour costs and facilitating precision agricultural practices such as highly localised pesticide and herbicide application and the replacement of large, heavy agricultural machines by fleets of small, lightweight robots.

This provides a detailed insight into the many ways in which robots are transforming agricultural practices.

The food industry is a highly competitive manufacturing area, but with relatively little robotic involvement as compared to the automotive industry. This is due to the fact that food products are highly variable both in shape, sizes and structure which poses a major problem for the development of manipulators for its handling. This paper reviews the current state of development in robot manipulators for the food industry. Three main areas were covered. They are: the handling of non‐rigid food products – the processing of meat, poultry, fish and milking, the harvesting of food products – picking of fruits, asparagus and mushrooms, and the packaging of food products – secondary and tertiary.

SILVER, the special interest group on advanced robotics and intelligent automation, is holding a series of meetings on applications in different sectors of industry. The May meeting was held at the Silsoe Research Institute in Bedfordshire. Speakers from Silsoe, as well as from universities and industry, reviewed a number of applications, current and potential, and some systems were demonstrated during the lunch break.

Aims at developing vision‐based algorithms to improve efficiency and quality in agricultural applications. Two case studies are analyzed dealing with the harvest of radicchio and the post‐harvest of fennel, respectively.

Presents two visual algorithms, which are called the radicchio visual localization (RVL) and fennel visual identification (FVI). The RVL serves as a detection system of radicchio plants in the field for a robotic harvester. The FVI provides information to an automated cutting device to remove the parts of fennel unfit for the market, i.e. root and leaves. Laboratory and field experiments are described to validate our approach and asses the performance of our visual modules.

Both the visual systems presented showed to be effective in experimental trials, computational efficient, accurate, and robust to noises and lighting variations. Computer vision could be successfully adopted in the intelligent and automated production of fresh market vegetables to improve quality and efficiency.

Provides guidance in the development of vision‐based algorithms for agricultural applications.

Describes visual algorithms based on intelligent morphological and color filters which lends themselves very well to agricultural applications and allow robustness and real‐time performance.

A harvesting robot is developed as part of kiwifruit industry automation in New Zealand. This kiwifruit harvester is currently not economically viable, as it drops and damages too many kiwifruit in the harvesting task due to the positional inaccuracy of the gripper. This is due to the difficulties in measuring the exact effective dimensions of the gripper from the manipulator. The purpose of this study is to obtain the effective gripper dimensions using kinematic calibration procedures.

A setup of a constraint plate with a dial gauge is proposed to acquire the calibration data. The constraint plate is positioned above the robot. The data is obtained by using a dial gauge and a permanent marker. The effective dimensions of the gripper are used as error parameters in the calibration process. Calibration is exercised by minimizing the difference between target positions and measured positions iteratively.

The robot with the obtained effective dimensions is tested in the field. It is found that the fruit drops due to positional inaccuracy of the gripper are greatly reduced after calibration.

The kiwifruit industry in New Zealand is growing rapidly and announced plans in 2017 to double global sales by 2025. This growth will put extra pressure on the labour supply for harvesting. Furthermore, the Covid pandemic and resulting border restrictions have dramatically reduced seasonal imported labour availability. A robotic system is a potential solution to address the labour shortages for harvesting kiwifruit.

For kiwifruit harvesting, the picking envelope is well above the robot; the experimental data points obtained by placing a constraint plate above the robot are at similar positions to the target positions of kiwifruit. Using this set of data points for calibration yields a good effect of obtaining the effective dimension of the gripper, which reduces the positional inaccuracy as shown in the field test results.

To reduce the effect of parameter uncertainties and input saturation on path tracking control for autonomous combine harvester, a path tracking controller is proposed, which integrates an adaptive neural network estimator and a saturation-aided system.

First, to analyze and compensate the influence of external factors, the vehicle model is established combining a dynamic model and a kinematic model. Meanwhile, to make the model simple, a comprehensive error is used, weighting heading error and position error simultaneously. Second, an adaptive neural network estimator is presented to calculate uncertain parameters which eventually improve the dynamic model. Then, the path tracking controller based on the improved dynamic model is designed by using the backstepping method, and its stability is proved by the Lyapunov theorem. Third, to mitigate round-trip operation of the actuator due to input saturation, a saturation-aided variable is presented during the control design process.

To verify the tracking accuracy and environmental adaptability of the proposed controller, numerical simulations are carried out under three different cases, and field experiments are performed in harvesting wheat and paddy. The experimental results demonstrate the tracking errors of the proposed controller that are reduced by more than 28% with contrast to the conventional controllers.

An adaptive neural network-based path tracking control is proposed, which considers both parameter uncertainties and input saturation. As far as we know, this is the first time a path tracking controller is specifically designed for the combine harvester with full consideration of working characteristics.