Adaptive stable grasping control strategy based on slippage detection

Purpose The authors want to design an adaptive grasping control strategy without setting the expected contact force in advance to maintain grasping stable, so that the proposed control system can deal with unknown object grasping manipulation tasks. Design/methodology/approach The adaptive grasping control strategy is proposed based on bang-bang-like control principle and slippage detection module. The bang-bang-like control method is designed to find and set the expected contact force for the whole control system, and the slippage detection function is achieved by dynamic time warping algorithm. Findings The expected contact force can adaptively adjust in grasping tasks to avoid bad effects on the control system by the differences of prior test results or designers. Slippage detection can be recognized in time with variation of expected contact force manipulation environment in the control system. Based on if the slippage caused by an unexpected disturbance happens, the control system can automatically adjust the expected contact force back to the level of the previous stable state after a given time, and has the ability to identify an unnecessary increasing in the expected contact force. Originality/value Only contact force is used as feedback variable in control system, and the proposed strategy can save hardware components and electronic circuit components for sensing, reducing the cost and design difficulty of conducting real control system and making it easy to realize in engineering application field. The expected contact force can adaptively adjust due to unknown disturbance and slippage for various grasping manipulation tasks.

On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine

In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machine in combination with feature extraction and classification methods may be utilized.This paper presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning (CBR) for on-board clutch slippage detection and diagnosis in heavy duty equipment. The feature extraction methods used are Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the signals implemented as continuous queries over data streams. The anomaly detection module has two components, the Gaussian Mixture Model (GMM) and the Logistics Regression classifier. CBR is a learning approach that classifies faults by creating a new solution for a new fault case from the solution of the previous fault cases. Through use of a data stream management system and continuous queries (CQs), the anomaly detection module continuously waits for a clutch slippage event detected by the feature extraction methods, the query returns a set of features, which activates the anomaly detection module. The first component of the anomaly detection module trains a GMM to extracted features while the second component uses a Logistic Regression classifier for classifying normal and anomalous data. When an anomaly is detected, the Case-Based diagnosis module is activated for fault severity estimation.