In harvesting operations, simulation verification of hand–eye coordination in a virtual canopy is critical for harvesting robot research. More realistic scenarios, vision-based driving motion, and cross-platform interaction information are needed to achieve such simulations, which are very challenging. Current simulations are more focused on path planning operations for consistency scenarios, which are far from satisfying the requirements. To this end, a new approach of visual servo multi-interaction simulation in real scenarios is proposed. In this study, a dual-arm grape harvesting robot in the laboratory is used as an example. To overcome these challenges, a multi-software federation is first proposed to establish their communication and cross-software sending of image information, coordinate information, and control commands. Then, the fruit recognition and positioning algorithm, forward and inverse kinematic model and simulation model are embedded in OpenCV and MATLAB, respectively, to drive the simulation run of the robot in V-REP, thus realizing the multi-interaction simulation of hand–eye coordination in virtual trellis vineyard. Finally, the simulation is verified, and the results show that the average running time of a string-picking simulation system is 6.5 s, and the success rate of accurate picking point grasping reached 83.3%. A complex closed loop of “scene-image recognition-grasping” is formed by data processing and transmission of various information. It can effectively realize the continuous hand–eye coordination multi-interaction simulation of the harvesting robot under the virtual environment.
Pollination is essential to maintain ecosystem balance and agricultural production. Domesticated bee pollination, which is easy to feed and manage, and mechanized pollination, which is not restricted by the environment, are considered the main technical means to alleviate the “pollinating insect crisis”. By analyzing differences in pollination methods for different crops, this review summarizes the research progress for efficient pollination technology from the perspectives of bee pollination and mechanized pollination in fields, orchards, and greenhouses. The selection of pollination method should be based on the physiological characteristics of crops and the actual environmental conditions of natural pollination. The pollination ability of bees is closely related to the status of the bees. Maintaining the pollination ability of bees in a reasonable range is the goal of bee pollination services. Colony state control technology needs to develop in two directions. First, there is a need to develop colony state monitoring technology based on multi-feature information fusion and to explore the self-regulation mechanisms of the colony in response to various factors. Second, based on these self-regulation mechanisms, there is a need to develop a low-cost and non-invasive bee colony state and pollination capacity estimation model, monitoring technology, and equipment based on single feature information. The goals of mechanized pollination are “efficiency” and “precision”. Mechanized pollination technology needs to be developed in two directions. First, the mechanisms of pollen abscission, transport, and sedimentation in different crops and mechanized pollination conditions should be explored. Second, research and development of efficient and accurate pollination equipment and technology based on the integration of multiple technologies such as pneumatic assistance, auxiliaries, static electricity, target, variables, and navigation, are needed.
It is extremely necessary to achieve the rapid harvesting of table grapes planted with a standard trellis in the grape industry. The design and experimental analysis of a dual-arm high-speed grape-harvesting robot were carried out to address the limitations of low picking efficiency and high grape breakage rate of multijoint robotic arms. Based on the characteristics of the harvesting environment, such as the small gap between grape clusters, standard trellis, and vertical suspension of clusters, the configuration of the dual-arm harvesting robot is reasonably designed and analyzed, and the overall configuration of the machine and the installation position of key components are derived. Robotic arm and camera view analysis of the workspace harvesting process was performed using MATLAB, and it can be concluded that the structural design of this robot meets the grape harvesting requirements with a standard trellis. To improve the harvesting efficiency, some key high-speed harvesting technologies were adopted, such as the harvesting sequence decision based on the “sequential mirroring method” of grape cluster depth information, “one-eye and dual-arm” high-speed visual servo, dual arm action sequence decision, and optimization of the “visual end effector” large tolerance combination in a natural environment. The indoor accuracy experiment shows that when the degree of obscuration of grape clusters by leaves increases, the vision algorithm based on the geometric contours of grape clusters can still meet the demands of harvesting tasks. The motion positioning average errors of the left and right robotic arms were (X: 2.885 mm, Y: 3.972 mm, Z: 2.715 mm) and (X: 2.471 mm, Y: 3.289 mm, Z: 3.775 mm), respectively, and the average dual-arm harvesting time in one grape cluster was 8.45 s. The field performance test verifies that the average harvesting cycle of the robot with both arms reached 9 s/bunch, and the success rate of bunch identification and harvesting success rate reached 88 and 83%, respectively, which were significantly better than those of existing harvesting robots worldwide.
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