Double-arm picking robots are widely used in agricultural production for their high collaborative efficiency. While picking, area planning and collision detection between the mechanical arms is a crucial challenge for the double-arm robot, which needs to establish a collision-free path for fruit picking. In this study, we developed a double-arm cooperation method for robotic picking of kiwifruit. Firstly, the problem of dividing the picking area was simplified into a multiple traveling salesmen problem (MTSP) to be solved. The picking sequence of each robotic arm was formulated by the principle of similar picking numbers, and combined with the brainstorming optimization algorithm (BSO). Secondly, a double-arm parameter model was built to solve the forward and backward movements of the robotic arms and to figure out the joint position. The spatial mathematical relationship of the bounding boxes between the robotic arms was used to detect the collision between the two robotic arms, in order to achieve the avoidance between the robotic joints. Then, simulation software was applied to the simulation and analyzed the availability of picking area planning and collision detection. The simulation results showed that the optimized picking sequence planning using BSO was more efficient; the smooth joint trajectory during the movement of the robotic arms met the limits on the range of movement and on the angular velocity of the robotic arm joints. Finally, based on the simulation result, a double-arm collaboration platform was tested. The double-arm collaboration platform harvesting trials showed that the average picking success rate was 86.67%, and collision detection time was 3.95 ± 0.83 s per fruit. These results indicated that the proposed method could plan the operation tasks of the double-arm picking robot system, and effectively implement the collision-free picking operation.

Kiwifruit harvesting with robotics can be troublesome due to the clustering feature. The gripper of the end effector will easily cause unstable fruit grasping, or the bending and separation action will interfere with the neighboring fruit because of an inappropriate grasping angle, which will further affect the success rate. Therefore, predicting the correct grasping angle for each fruit can guide the gripper to safely approach, grasp, bend and separate the fruit. To improve the grasping rate and harvesting success rate, this study proposed a grasping detection method for a kiwifruit harvesting robot based on the GG-CNN2. Based on the vertical downward growth characteristics of kiwifruit, the grasping configuration of the manipulator was defined. The clustered kiwifruit was mainly divided into single fruit, linear cluster, and other cluster, and the grasping dataset included depth images, color images, and grasping labels. The GG-CNN2 was improved based on focal loss to prevent the algorithm from generating the optimal grasping configuration in the background or at the edge of the fruit. The performance test of the grasping detection network and the verification test of robotic picking were carried out in orchards. The results showed that the number of parameters of GG-CNN2 was 66.7 k, the average image calculation speed was 58 ms, and the average grasping detection accuracy was 76.0%, which ensures the grasping detection can run in real time. The verification test results indicated that the manipulator combined with the position information provided by the target detection network YOLO v4 and the grasping angle provided by the grasping detection network GG-CNN2 could achieve a harvesting success rate of 88.7% and a fruit drop rate of 4.8%; the average picking time was 6.5 s. Compared with the method in which the target detection network only provides fruit position information, this method presented the advantages of harvesting rate and fruit drop rate when harvesting linear clusters, especially other cluster, and the picking time was slightly increased. Therefore, the grasping detection method proposed in this study is suitable for near-neighbor multi-kiwifruit picking, and it can improve the success rate of robotic harvesting.

In order to reduce the mechanical damage during the kiwifruit picking process, the fruit rate of the picked fruit should be improved. The mechanical properties of the epidermis and interior of the fruit during the harvesting process were studied, so as to analyze the damage principle of the fruit. Firstly, a three-dimensional model of kiwifruit was constructed by point cloud scanning, and the flesh and placenta were filled in order to become a complete kiwifruit model. The elastic modulus, failure stress, and density of the kiwifruit skin, flesh, and placenta were obtained experimentally, and the material properties of the kiwifruit model were endowed with properties. Secondly, the finite element method was used to analyze the epidermis and internal stress of the kiwifruit by simulating the two processes of grabbing kiwifruit and picking to fruit boxes. The results show that the relative error of the simulation and test of the simulated grasping of kiwifruit was 6.42%, and the simulation and test of picking to fruit box confirmed the existence of damage, and the reflectivity of the damaged point in the detection was 6.18% on average, and the hardness value decreased to 8.30 $\mathrm{kg}/{\mathrm{cm}}^2$ on average. The results from this study can provide a reference for control strategies and damage avoidance during grasping.

To address the issue of low positioning accuracy of mobile robots in trellis kiwifruit orchards with weak signal environments, this study investigated an outdoor integrated positioning method based on ultra-wideband (UWB), light detection and ranging (LiDAR), and odometry (ODOM). Firstly, a dynamic error correction strategy using the Kalman filter (KF) was proposed to enhance the dynamic positioning accuracy of UWB. Secondly, the particle filter algorithm (PF) was employed to fuse UWB/ODOM/LiDAR measurements, resulting in an extended Kalman filter (EKF) measurement value. Meanwhile, the odometry value served as the predicted value in the EKF. Finally, the predicted and measured values were fused through the EKF to estimate the robot’s pose. Simulation results demonstrated that the UWB/ODOM/LiDAR integrated positioning method achieved a mean lateral error of 0.076 m and a root mean square error (RMSE) of 0.098 m. Field tests revealed that compared to standalone UWB positioning, UWB-based KF positioning, and LiDAR/ODOM integrated positioning methods, the proposed approach improved the positioning accuracy by 64.8%, 13.8%, and 38.3%, respectively. Therefore, the proposed integrated positioning method exhibits promising positioning performance in trellis kiwifruit orchards with potential applicability to other orchard environments.

Usually the planters used in agricultural machinery face two problems: The planters have to spread the soil twice using the tray seed planter, and it is difficult for them to dig the holes before planting. This study has designed a spiral digging end-effector to dig holes in parallel effectively and quickly so that the seeds can be planted in the holes easily. A spiral digging end-effector was designed with five spiral bits, a synchronous belt, a cylinder, a gear motor, and a connecting plate based on the tray size and the pumpkin seed characteristics. Before the optimization of the end-effector’s parameters, the substrate discrete element model parameters were calibrated using the method of “material funneling” applied for the EDEM discrete element model simulation. The contact parameters and model parameters (substrate–substrate static friction coefficient, substrate–substrate rolling friction coefficient, and surface energy) that have significant impacts on the substrate AOR (angle of repose) were selected by applying the Plackett–Burman Design and the path of steepest ascent method, and their respective optimal value range were determined. The optimal parameter values were obtained through the Central Composite Design response surface analysis test, and the parameters of the spiral digging end-effector were optimized combining the substrate particle simulation. The verification test results indicate that the substrate discrete element model parameters were accurate and reliable if the substrate static friction coefficient was 0.427; the substrate rolling friction coefficient was 0.039, and the surface energy was 0.228. When the cone angle is 30°, the spiral angle is 80°, and the rotational speed is 240 r/min, the section width at the hole depth of 13 mm is 23.1 mm, and the particle overflow proportion is about 2.05%. Compared with the protrusions penetrating end-effector, soil porosity is increased, and soil aeration is improved. Therefore, the spiral digging end-effector can effectively and quickly dig holes in the seedling substrate and can sow pumpkin seeds, which provides a research basis for the design and improvement of automatic pumpkin seed sowing equipment.