Advances in Forest Robotics: A State-of-the-Art Survey

A spraying path planning algorithm based on colour-depth fusion segmentation in peach orchards

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.

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Actuators and Sensors for Application in Agricultural Robots: A Review

With the increase of labor cost and the development of agricultural mechanization, standardized orchards suitable for autonomous operations of agricultural machinery will be a future development trend of the fruit-planting industry. For field-planting processes of standardized orchards, autonomous navigation of orchard vehicles in complex environments is the foundation of mechanized and intelligent field operations. In order to realize autonomous driving and path-tracking of vehicles in complex standardized orchards that involve much noise and interference between rows of fruit trees, an automatic navigation system was designed for orchard vehicles, based on 2D lasers. First, considering the agronomic requirements for orchard planting such as plant spacing, row spacing and trunk diameter, different filtering thresholds were established to eliminate discrete points of 2D laser point cloud data effectively. Euclidean clustering algorithm and the important geometric theorems of three points collinearity was used to extract the central feature points of the trunk, as the same time, navigation path was fitted based on the least square method. Secondly, an automatic navigation control algorithm was designed, and the fuzzy control was used to realize the dynamic adjustment of the apparent distance of the pure pursuit model. Finally, the reliability of the proposed approach was verified by simulation using MATLAB/Simulink, and field tests were carried out based on electric agricultural vehicle. Experimental results show that the method proposed in this study can effectively improve the precision of automatic navigation in complex orchard environment and realize the autonomous operation of orchard vehicles.

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Research on 2D Laser Automatic Navigation Control for Standardized Orchard

Cow identification in free-stall barns based on an improved Mask R-CNN and an SVM

Natural rubber is widely used in human life because of its excellent quality. At present, manual tapping is still the main way to obtain natural rubber. There is a sore need for intelligent tapping devices in the tapping industry, and the autonomous navigation technique is of great importance to make rubber-tapping devices intelligent. To realize the autonomous navigation of the intelligent rubber-tapping platform and to collect information on a rubber forest, the sparse point cloud data of tree trunks are extracted by the low-cost LiDAR and a gyroscope through the clustering method. The point cloud is fitted into circles by the Gauss–Newton method to obtain the center point of each tree. Then, these center points are threaded through the Least Squares method to obtain the straight line, which is regarded as the navigation path of the robot in this forest. Moreover, the Extended Kalman Filter (EKF) algorithm is adopted to obtain the robot’s position. In a forest with different row spacings and plant spacings, the heading error and lateral error of this robot are analyzed and a Fuzzy Controller is applied for the following activities: walking along one row with a fixed lateral distance, stopping at fixed points, turning from one row into another, and collecting information on plant spacing, row spacing, and trees’ diameters. Then, according to the collected information, each tree’s position is calculated, and the geometric feature map is constructed. In a forest with different row spacings and plant spacings, three repeated tests have been carried out at an initial speed of 0.3 m/s. The results show that the Root Mean Square (RMS) lateral errors are less than 10.32 cm, which shows that the proposed navigation method provides great path tracking. The fixed-point stopping range of the robot can meet the requirements for automatic rubber tapping of the mechanical arm, and the average stopping error is 12.08 cm. In the geometric feature map constructed by collecting information, the RMS radius errors are less than 0.66 cm, and the RMS plant spacing errors are less than 11.31 cm. These results show that the method for collecting information and constructing a map recursively in the process of navigation proposed in the paper provides a solution for forest information collection. The method provides a low-cost, real-time, and stable solution for forest navigation of automatic rubber tapping equipment, and the collected information not only assists the automatic tapping equipment to plan the tapping path, but also provides a basis for the informationization and precise management of a rubber plantation.

https://www.mdpi.com/1424-8220/19/9/2136/pdf

Song Wang

Papers

A Rubber-Tapping Robot Forest Navigation and Information Collection System Based on 2D LiDAR and a Gyroscope.

A method for organs classification and fruit counting on pomegranate trees based on multi-features fusion and support vector machine by 3D point cloud

Design, development, and field evaluation of a rubber tapping robot

Design, development and evaluation of latex harvesting robot based on flexible Toggle

Vision Servo Control Method and Tapping Experiment of Natural Rubber Tapping Robot