In view of the plugged-out end-effector that can adapt only to a specific size of the tray, the needle spacing and angle of the seedling needle are fixed. In this paper, a new type of plugged-out transplanting end-effector is proposed. The end-effector adopts a double-cam structure to automatically adjust the spacing and angle of the seedling needle, which solves the problem of picking seedlings for different sizes of trays. Firstly, the working principle of 72-hole, 128-hole, and 200-hole trays and a plugged-out end-effector was analyzed. The overall structure of the end-effector was designed. Subsequently, the EDEM software was used to construct the pot seedling model and conduct single-factor simulation experiments to identify the range of factors for the subsequent regression orthogonal experiment. Finally, a tray transplanting test platform was built. With the grasping acceleration, penetration angle, insertion depth, and insertion margin ratio as the test factors and the pot seedling breakage rate as the test evaluation indicators. A four-factor three-level orthogonal regression experiment was conducted to establish a regression model of the seedling breakage rate, and its parameters were optimized. The optimal combination is detailed as follows: a 72-hole tray grasping acceleration of 0.28 m/s², a penetration angle of 13°, an insertion depth of 40 mm, and an insertion margin ratio of 15%; a 128-hole tray grasping acceleration of 0.28 m/s², a penetration angle of 12°, an insertion depth of 36 mm, and an insertion margin ratio of 15%; a 200-hole tray grasping acceleration of 0.28 m/s², a penetration angle of 11°, an insertion depth of 32 mm, and an insertion margin ratio of 10%. Under the optimal combination, the breakage rate of 72 holes reached 2.92%. The breakage rate of 128 holes was stable at 1.76%, while that of 200 holes was stable at 0.68%, which is conducive to the study of a general end-effector. The device developed in this study provides an effective solution to taking and throwing different sizes of cavitation trays, thus providing a practical reference for the study of a generic end-effector.

Aiming at the problems of seedling injury and planting leakage due to the lack of seeding clamping force detection and real-time control in vegetable transplanting, a force feedback gripper was developed based on the linear Hall element. The mechanical properties of the stem of pepper cavity seedlings were first analyzed to provide a basis for the design of the gripper. A linear Hall sensor, a magnet, an elastic actuator, and an Arduino Uno development board make up the grasping force detecting system. Upon picking up a seedling, the elastic actuator, which is connected to the magnet, bends like a cantilever beam. As a result of the micro-displacement created by the elastic actuator, the Hall sensor’s voltage changes and can be used to determine the clamping force. Detection avoids direct contact between the sensor and the cavity seedlings, reducing the risk of sensor damage. Finite element method (FEM) simulations were used to determine the initial spacing between the magnet and Hall sensor and the effect of the elastic actuator. Control commands are sent to the servo based on the gripping force collected by the Arduino Uno board. Finally, the functions of accurate measurement, display, storage, and control of the clamping force of the cavity tray seedlings are realized, so that the damage rate of the cavity tray seedlings is reduced. In order to explore the influence of the elastic actuators on the clamping force detection system and the performance of the force feedback gripper, a calibration test of the clamping force detection system and a test of the indoor transplantation of pepper seedlings were carried out. Based on the calibration test, the clamping force detection system has a sensitivity of 0.0693 V/N, linearity of 3.21%, an average linear coefficient of determination of 0.986, and a range of 10 N, which fully meet the clamping force detection accuracy requirements during transplantation. Indoor tests showed that the force feedback gripper was stable and adaptable. This study can provide a reference for detecting and controlling clamping forces during transplantation.

Simultaneous Localization and Mapping (SLAM) is one of the key technologies with which to address the autonomous navigation of mobile robots, utilizing environmental features to determine a robot’s position and create a map of its surroundings. Currently, visual SLAM algorithms typically yield precise and dependable outcomes in static environments, and many algorithms opt to filter out the feature points in dynamic regions. However, when there is an increase in the number of dynamic objects within the camera’s view, this approach might result in decreased accuracy or tracking failures. Therefore, this study proposes a solution called YPL-SLAM based on ORB-SLAM2. The solution adds a target recognition and region segmentation module to determine the dynamic region, potential dynamic region, and static region; determines the state of the potential dynamic region using the RANSAC method with polar geometric constraints; and removes the dynamic feature points. It then extracts the line features of the non-dynamic region and finally performs the point–line fusion optimization process using a weighted fusion strategy, considering the image dynamic score and the number of successful feature point–line matches, thus ensuring the system’s robustness and accuracy. A large number of experiments have been conducted using the publicly available TUM dataset to compare YPL-SLAM with globally leading SLAM algorithms. The results demonstrate that the new algorithm surpasses ORB-SLAM2 in terms of accuracy (with a maximum improvement of 96.1%) while also exhibiting a significantly enhanced operating speed compared to Dyna-SLAM.