Abstract
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.
Highlights
Robots are replacing humans in performing primary motions such as grasping, carrying, and placing in dirty, chaotic, and dangerous working conditions [1]. e gripper is a core component of a robot
E gripper, which serves as the end-effector and performs actions in direct contact with agricultural products, is a vital component that allows the robotic device to interact with the external environment
It is necessary to calibrate the force curve and fit the force sensor so that accurate information on the contact surface can be obtained during the grasping process to characterize the grasping process, realize adaptive grasping, and distinguish the fruits
Summary
Robots are replacing humans in performing primary motions such as grasping, carrying, and placing in dirty, chaotic, and dangerous working conditions [1]. e gripper is a core component of a robot. E identification task can be performed by the learning algorithms based on the different variations of the geometric shapes and contact force of the soft fingers during the grasping process for different fruits. E fundamental purpose of this study is to classify fruits using a robotic gripper with machine learning algorithms and tactile sensing perception. To successfully achieve this objective, the following subobjectives must be fulfilled: (1) design and manufacture of the adaptive gripper; (2) building of the tactile sensing information acquisition system and calibration of the tactile sensors; (3) development of the robotic manipulator platform and collection of tactile sensing data; and (4) performance of grasping fruit identification experiments
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