Search Results (8)

Natural latex harvest is pushing the boundaries of unmanned agricultural production in rubber milk collection via integrated robots in hilly and mountainous regions, such as the fixed and mobile tapping robots widely deployed in forests. As there are bad working conditions and complex natural environments surrounding rubber trees, the real-time and precision assessment of rubber milk yield status has emerged as a key requirement for improving the efficiency and autonomous management of these kinds of large-scale automatic tapping robots. However, traditional manual rubber milk yield status detection methods are limited in their ability to operate effectively under conditions involving complex terrain, dense forest backgrounds, irregular surface geometries of rubber milk, and the frequent occlusion of rubber milk bowls (RMBs) by vegetation. To address this issue, this study presents an unmanned aerial vehicle (UAV) imagery rubber milk yield state detection method, termed YOLOv8n-RMB, in unstructured field environments instead of manual watching. The proposed method improved the original YOLOv8n by integrating structural enhancements across the backbone, neck, and head components of the network. First, a receptive field attention convolution (RFACONV) module is embedded within the backbone to improve the model’s ability to extract target-relevant features in visually complex environments. Second, within the neck structure, a bidirectional feature pyramid network (BiFPN) is applied to strengthen the fusion of features across multiple spatial scales. Third, in the head, a content-aware dynamic upsampling module of DySample is adopted to enhance the reconstruction of spatial details and the preservation of object boundaries. Finally, the detection framework is integrated with the BoT-SORT tracking algorithm to achieve continuous multi-object association and dynamic state monitoring based on the filling status of RMBs. Experimental evaluation shows that the proposed YOLOv8n-RMB model achieves an AP@0.5 of 94.9%, an AP@0.5:0.95 of 89.7%, a precision of 91.3%, and a recall of 91.9%. Moreover, the performance improves by 2.7%, 2.9%, 3.9%, and 9.7%, compared with the original YOLOv8n. Plus, the total number of parameters is kept within 3.0 million, and the computational cost is limited to 8.3 GFLOPs. This model meets the requirements of yield assessment tasks by conducting computations in resource-limited environments for both fixed and mobile tapping robots in rubber plantations. Full article

Rubber tapping robots represent a significant research direction in modern robotics in agricultural automation. Nevertheless, natural rubber tapping robots encounter considerable challenges in achieving precise tapping, particularly in controlling tapping depth, due to the lack of suitable control algorithms. To solve this problem, an improved Particle Swarm Optimization/Proportional–Integral–Derivative (PSO-PID) control method has been proposed in this paper. It enhances the inertia weight of the particle swarm by introducing adaptive inertia weight, solving the shortcomings of the traditional PSO algorithm, such as insufficient local search ability and early convergence. The experimental results show that the rubber tapping depth system based on the improved PSO-PID algorithm has high responsiveness and robustness, with an average settling time of 0.419 s and an overshoot that can be kept below 2.5%. The depth control accuracy, robustness and convergence speed of the system are significantly better than other well-known optimization algorithms. At a tapping depth of 3.0 mm, the injury rate was reduced to 2%, surpassing the level of skilled manual tapping workers. It has been proven that this method can effectively solve the key problem of accurate depth control in current rubber tapping. Full article

Natural rubber is a critical material that is essential to industry and transportation. In order to reduce the cost of rubber tapping and improve the efficiency and profitability of rubber production, the 4GXJ-2 portable electric rubber cutter and automatic rubber tapping robot have been developed. In their vibration tool holder, the planetary rotor with variable speed self rotation and uniform eccentric revolution is the most important transmission component, and its instability will cause irregular vibration of the tapping tool, thereby reducing the accuracy of vibration cutting and increasing noise. Base on the ANCF (Absolute Nodal Coordinate Formulation) 3D-beam element and 3D REF (3D Ring on Elastic Foundation), a novel eccentric 3D REF model of a planetary rotor is proposed. By introducing multiple coordinate systems, the coupled motion of uniform eccentric revolution, variable speed self rotation and flexible deformation is decomposed and the influences of these motions on the centrifugal force and Coriolis force are more clearly derived. The model is degraded and validated by comparing with other examples of a rotating circular ring model and uniformly eccentrically revolving annular plate. According to the Floquet theory and Runge−Kutta method, the unstable region of revolution speed of a planetary rotor in rubber tapping machinery is predicted as [817 rad/s, 909 rad/s], [1017 rad/s, 1095 rad/s] and [1263 rad/s,1312 rad/s]. Compared with the rubber-tapping experiment of rubber tapping machinery, the validity of the proposed model is further verified. This model provides important design references for the speed settings of those rubber tapping machines. Full article

Simultaneous localization and mapping (SLAM) in rubber plantations is a challenging task for rubber-tapping robots. Due to the long-term stability of tree trunks in rubber plantations, a SLAM system based on semantic segmentation, called Se-LOAM, is proposed in this work. The 3D lidar point cloud datasets of trunks collected in rubber plantations of Hainan University are used to train the semantic model, and the model is used to extract features of trunk point clouds. After clustering the trunk point clouds, each single rubber tree instance is segmented based on the Viterbi algorithm. The point clouds of tree instances are fitted to the cylindrical trunk models for semantic cluster association and positional estimation, which are used for lidar odometry and mapping. The experimental results show that the present SLAM system is accurate in establishing online mapping, and the location of the trunk in the map is clearer. Specifically, the average relative pose error is 0.02 m, which is better than the positioning performance of LOAM and LeGO-LOAM. The average error of estimating the diameter at breast height (DBH) is 0.57 cm, and it only takes 401.4 kB to store a map of the area of approximately 500 ${\mathrm{m}}^2$, which is about 10% less than other classic methods. Therefore, Se-LOAM can meet the requirements of online mapping, providing a robust SLAM method for rubber-tapping robots. Full article

To address the practical navigation issues of rubber-tapping robots, this paper proposes an active navigation system guided by trunk detection for a rubber-tapping robot. A tightly coupled sliding-window-based factor graph method is proposed for pose tracking, which introduces normal distribution transform (NDT) measurement factors, inertial measurement unit (IMU) pre-integration factors, and prior factors generated by sliding window marginalization. To actively pursue goals in navigation, a distance-adaptive Euclidean clustering method is utilized in conjunction with cylinder fitting and composite criteria screening to identify tree trunks. Additionally, a hybrid map navigation approach involving 3D point cloud map localization and 2D grid map planning is proposed to apply these methods to the robot. Experiments show that our pose-tracking approach obtains generally better performance in accuracy and robustness compared to existing methods. The precision of our trunk detection method is 93% and the recall is 87%. A practical validation is completed in robot rubber-tapping tasks of a real rubber plantation. The proposed method can guide the rubber-tapping robot in complex forest environments and improve efficiency. Full article

Natural rubber is a crucial raw material in modern society. However, the process of latex acquisition has long depended on manual cutting operations. The mechanization and automation of rubber-tapping activities is a promising field. Rubber-tapping operations rely on the horizontal cutting of the leading edge and vertical stripping of the secondary edge. Nevertheless, variations in the impact acceleration of the blade can lead to changes in the continuity of the chip, affecting the stability of the cut. In this study, an inertial measurement unit (IMU) and a robotic arm were combined to achieve the real-time sensing of the blade’s posture and position. The accelerations of the blade were measured at 21 interpolated points in the optimized cutting trajectory based on the principle of temporal synchronization. A multiple regression model was used to establish a link between impact acceleration and chip characteristics to evaluate cutting stability. The R-squared value for the regression equation was 0.976, while the correlation analysis for the R-squared and root mean square error (RMSE) values yielded 0.977 and 0.0766 mm, respectively. The correlation coefficient for the Z-axis was the highest among the three axes, at 0.22937. Strict control of blade chatter in the radial direction is necessary to improve the stability of the cut. This study provides theoretical support and operational reference for subsequent work on end-effector improvement and motion control. The optimized robotic system for rubber tapping can contribute to accelerating the mechanization of latex harvesting. Full article

The object detection algorithm is one of the core technologies of the intelligent rubber tapping robot, but most of the existing detection algorithms cannot effectively meet the tapping trajectory detection of natural rubber trees in the complex forest environment. This paper proposes a tapping trajectory detection method for natural rubber trees based on an improved YOLOv5 model to accomplish fast and accurate detection. Firstly, the coordinate attention (CA) mechanism is added to the Backbone network to embed the location information into the channel attention, which effectively improves the detection accuracy. Secondly, a module called convolution and GhostBottleneck (CGB) is designed, based on the Ghost module, to substitute the Cross Stage Partial Network (CSP) module in the Neck network, which ensures the detection accuracy while reducing model parameters. Finally, the EIoU loss function is introduced to enable a more accurate regression of the model. The experimental results show that the overall performance of the YOLOv5-CCE model outperforms the original YOLOv5 and other classical lightweight detection algorithms. Compared with the original YOLOv5 model, the YOLOv5-CCE model has a 2.1% improvement in mAP value, a 2.5% compression of model parameters, and a 7.0% reduction in the number of floating point operations (FLOPs). Therefore, the improved model can fully meet the requirements of real-time detection, providing a robust detection method for rubber tapping robots. Full article

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. Full article