Robotic systems for research and development of factory automation are complex and unavailable for broad deployment in robotic laboratory settings. The usual robotic factory automation setup consists of series of sensors, robotic arms and mobile robots integrated and orchestrated by a central information system. Cloud-based integration has been gaining traction in recent years. In order to build such a system in a laboratory environment, there are several practical challenges that have to be resolved to come to a point when such a system can become operational. In this paper, we present the development of one such system composed of (i) a cloud-based system built on top of open platform for innovation in logistics, (ii) a prototyped mobile robot with a forklift to manipulate pallets in a “factory” floor, and (iii) industrial robot ABB IRB 140 with a customized gripper and various sensors. A mobile robot is designed as an autonomous four Mecanum wheels system with on-board LiDAR and RGB-D sensor for simultaneous localization and mapping. The paper shows a use case of the overall system and highlights the advantages of having a laboratory setting with real robots for the research of factory automation in a laboratory environment. Moreover, the proposed solution could be scaled and replicated in real factory automation applications.

This paper presents a human-machine interaction interface for the teleoperated control of a three-finger robot hand based on surface electromyography. Based on the recorded sEMG signals, motion intention of a human operator is recognized and mapped onto corresponding predefined robot hand grasps. First, the experimental setup and underlying methodology of dataset generation are presented. Namely, 4-channel EMG data were collected from the forearm muscles of 4 healthy subjects as they performed a sequence of predefined grasps. Data processing included data filtering and segmentation, feature extraction both in time and frequency domains, data annotation, and gestures classification. Two types of classifiers (with multiple variations of parameters) were implemented and compared: (i) Support Vector Machines with multiple different kernels and (ii) K-Nearest-Neighbors. The k-cross validation procedure was combined with the holdout method to generate training, validation, and test sets. The performance of each classifier is presented in the form of a confusion matrix and a set of statistical measures, including F-measure. Finally, the integration of the proposed sEMG-based HMI interface and the selected classifier with a robot hand motion controller is presented.