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Electromyography (EMG) signal analysis is one of the key determinants of the effectiveness of prosthetic devices. Modern researchers provide various methods of detection of different hand movements and postures. In this work, we examined the possibility to produce efficient detection of hand movement to a specific posture with the minimum possible number of electrodes. The data acquisition is produced with 1 channel BiTalino EMG sensor based on bipolar differential measurement. Using feature extraction and artificial neural network we achieved 82% of offline classification accuracy for 8 hand motions and 91% accuracy for 6 hand motions based on 200ms of EMG signal. Also, the motion detection algorithm was developed and successfully tested that allowed to implement the algorithm for real-time classification that showed sufficient accuracy for 2 and 4 motion classes cases.

Accurate object classification and position estimation is a crucial part of executing autonomous pick-and-place operations by a robot and can be realized using RGB-D sensors becoming increasingly available for use in industrial applications. In this paper, we present a novel unified framework for object detection and classification using a combination of point cloud processing and deep learning techniques. The proposed model uses two streams that recognize objects on RGB and depth data separately and combines the two in later stages to classify objects. Experimental evaluation of the proposed model including classification accuracy compared with previous works demonstrates its effectiveness and efficiency, making the model suitable for real-time applications. In particular, the experiments performed on the Washington RGB-D object dataset show that the proposed framework has 97.5% and 95% fewer parameters compared to the previous state-of-the-art multimodel neural networks Fus-CNN, CNN Features and VGG3D, respectively, with the cost of approximately 5% drop in classification accuracy. Moreover, the inference of the proposed framework takes 66.11%, 32.65%, and 28.77% less time on GPU and 86.91%, 51.12%, and 50.15% less time on CPU in comparison to VGG3D, Fus-CNN, and CNN Features. The potential applicability of the developed object classification and position estimation framework was then demonstrated on an experimental robot-manipulation setup realizing a simplified object pick-and-place scenario. In approximately 95% of test trials, the system was able to accurately position the robot over the detected objects of interest in an automatic mode, ensuring stable cyclic execution with no time delays.

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Recognizing objects and estimating their poses have a wide range of application in robotics. For instance, to grasp objects, robots need the position and orientation of objects in 3D. The task becomes challenging in a cluttered environment with different types of objects. A popular approach to tackle this problem is to utilize a deep neural network for object recognition. However, deep learning-based object detection in cluttered environments requires a substantial amount of data. Collection of these data requires time and extensive human labor for manual labeling. In this study, our objective was the development and validation of a deep object recognition framework using a synthetic depth image dataset. We synthetically generated a depth image dataset of 22 objects randomly placed in a 0.5 m × 0.5 m × 0.1 m box, and automatically labeled all objects with an occlusion rate below 70%. Faster Region Convolutional Neural Network (R-CNN) architecture was adopted for training using a dataset of 800,000 synthetic depth images, and its performance was tested on a real-world depth image dataset consisting of 2000 samples. Deep object recognizer has 40.96% detection accuracy on the real depth images and 93.5% on the synthetic depth images. Training the deep learning model with noise-added synthetic images improves the recognition accuracy for real images to 46.3%. The object detection framework can be trained on synthetically generated depth data, and then employed for object recognition on the real depth data in a cluttered environment. Synthetic depth data-based deep object detection has the potential to substantially decrease the time and human effort required for the extensive data collection and labeling.

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This study investigates how different motion planning algorithms, implemented on a collaborative robot (cobot), are perceived by 48 human subjects. The four implemented algorithms ensure human safety based on the concept of speed and separation monitoring, but differ based on the following characteristics: (a) the cobot motion happens either along a fixed path or with a trajectory that is continuously planned in real time via nonlinear model predictive control, to increase cobot productivity; (b) the cobot speed is further reduced—or not—in real time based on heart rate measurements, to increase perceived safety. We conclude that (1) using a fixed path—compared to real-time motion planning—may reduce productivity and, at least when heart rate measurements are not used to modify the cobot speed, increases perceived safety; (2) reducing cobot speed based on heart rate measurements reduces productivity but does not improve perceived safety; (3) perceived safety is positively affected by habituation during the experiment, and unaffected by previous experience.

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