Grasping is one of the most common tasks related to robotics and manipulation which has received an extensive amount of contributions from the research community. From a design point of view, the robotic gripper systems are generally manufactured using a significant amount of small moving parts, in order to establish a balance between size, weight and performance. This balance leads to designs and components that are less robust than those of, for example, pneumatic grippers. To the best of our knowledge, most of the literature related to robotic grasping concentrates and focuses on grasping from a cognitive perspective. However, in order to ensure the execution of grasping tasks over extended periods of time, reducing down times and increasing gripper availability, even in demanding scenarios without access to maintenance, other phenomena such as component tear and degradation have to be monitored and analysed. This paper proposes an unsupervised learning model based approach for the estimation of the degradation states and the detection of abnormal working conditions of the actuator components for a class of robotic anthropomorphic hand. The approach allows an easy implementation and establishes the basis for the development of remaining useful life estimation algorithms for the components of other gripper systems. Our proposed architecture consists of an automatic degradation estimator and working condition detector, based on an unsupervised model combining K-means and Gaussian Mixture Models. The model estimates the hand's actuators degradation and determines its working condition from the online data collected during grasping tasks considering different objects. The proposed method was experimentally tested on a real Schunk SVH Hand used to assist humans during the assembly process in the automobile industry.
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Degradation, Grasping, Robotic Hand
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