As the core component of agricultural robots, robotic grippers are widely used for plucking, picking, and harvesting fruits and vegetables. Secure grasping is a severe challenge in agricultural applications because of the variation in the shape and hardness of agricultural products during maturation, as well as their variety and delicacy. In this study, a fruit identification method utilizing an adaptive gripper with tactile sensing and machine learning algorithms is reported. An adaptive robotic gripper is designed and manufactured to perform adaptive grasping. A tactile sensing information acquisition circuit is built, and force and bending sensors are integrated into the robotic gripper to measure the contact force distribution on the contact surface and the deformation of the soft fingers. A robotic manipulator platform is developed to collect the tactile sensing data in the grasping process. The performance of the random forest (RF), k-nearest neighbor (KNN), support vector classification (SVC), naive Bayes (NB), linear discriminant analysis (LDA), and ridge regression (RR) classifiers in identifying and classifying five types of fruits using the adaptive gripper is evaluated and compared. The RF classifier achieves the highest accuracy of 98%, while the accuracies of the other classifiers vary from 74% to 97%. The experiment illustrates that efficient and accurate fruit identification can be realized with the adaptive gripper and machine learning classifiers, and that the proposed method can provide a reference for controlling the grasping force and planning the robotic motion in the plucking, picking, and harvesting of fruits and vegetables.
Electroadhesion is an adhesion mechanism applying high voltage to generate adhesive force. The electroadhesion system can generate and maintain adhesive force on almost any object, solving the challenge of handling irregular and rough surface objects as well as fragile objects. The electroadhesive pad is a key component of the electroadhesion system for interacting with the target object. By optimizing the design of the electroadhesive pad, the electroadhesion system provides greater adhesive force and achieves better adhesion. In this study, a multiparameter theoretical model including the dimensional parameters of the electroadhesive pad has been developed and an optimization design strategy for specific applications has been proposed. By considering both the key parameters influencing the electroadhesive force and the practical constraints of equipment and materials, this strategy allows the optimization design methods of electroadhesive pads to be further extended to applications. The influence of each parameter on the optimization results has been evaluated by calculating and comparing the optimized values under different conditions, and it has been demonstrated that the size of the pad also has an effect on the optimized values. A 3D simulation model has been established to simulate the effect of electroadhesion, and the accuracy of the optimization results has been verified by comparing the theoretical and simulation results. An application example has been performed and the results have shown that the structure of the electroadhesive pad can be optimized by using this strategy, thus maximizing the generated electroadhesive force and improving the overall performance of the electroadhesion system.
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