Sensor Signal and Information Processing II [Editorial]

This Special Issue compiles a set of innovative developments on the use of sensor signals and information processing. In particular, these contributions report original studies on a wide variety of sensor signals including wireless communication, machinery, ultrasound, imaging, and internet data, and information processing methodologies such as deep learning, machine learning, compressive sensing, and variational Bayesian. All these devices have one point in common: These algorithms have incorporated some form of computational intelligence as part of their core framework in problem solving. They have the capacity to generalize and discover knowledge for themselves, learning to learn new information whenever unseen data are captured

Vision-based human presence detection pipeline by means of transfer learning approach

Over the last century, industrial robots have gained an immense amount of popularity in replacing the human workers due to their highly repetitive nature. It was a twist to the industries when the concept of cooperative robots, known as cobots, has been innovated. Sharing space between the cobots and human workers has considered as the most effective way of utilizing the cobots. Keeping in mind that the safety of the human workers is always the top priority of the cobot applications in the industries, many time and efforts have been invested to improve the safeness of the cobots deployments. Yet, the utilization of deep learning technologies is rarely found in accordance with human detection in the field of research, especially the transfer learning approach, providing that the visual perception has shown to be a unique sense that still cannot be replaced by other. Hence, this thesis aimed to leverage the transfer learning approach to fine-tune the deep learning-based object detection models in the human detection task. In relation to this main goal, the objectives of the study were as follows: establish an image dataset for cobot environment from the surveillance cameras in TT Vision Holdings Berhad, formulate deep learning-based object detection models by using the transfer learning approach, and evaluate the performance of various transfer learning models in detecting the presence of human workers with relevant evaluation metrics. Image dataset has acquired from the surveillance system of TT Vision Holdings Berhad and annotated accordingly. The variations of the dataset have been considered thoroughly to ensure the models can be well-trained on the distinct features of the human workers. TensorFlow Object Detection API was used in the study to perform the fine-tuning of the one-stage object detectors. Among all the transfer learning strategies, fine-tuning has chosen since it suits the study well after the interpretation on the size-similarity matrix. A total of four EfficientDet models, two SSD models, three RetinaNet models, and four CenterNet models were deployed in the present work. As a result, SSD-MobileNetV2-FPN model has achieved 81.1% AP with 32.82 FPS, which is proposed as the best well-balanced fine-tuned model between accuracy and speed. In other case where the consideration is taken solely on either accuracy or inference speed, SSD_MobileNetV1-FPN model has attained 87.2% AP with 28.28 FPS and CenterNet-ResNet50-V1-FPN has achieved 78.0% AP with 46.52 FPS, which is proposed to be the model with best accuracy and inference speed, respectively. As a whole, it could be deduced that the transfer learning models can handle the human detection task well via the fine-tuning on the COCO-pretrained weights

Human-Robot Collaborations in Industrial Automation

Technology is changing the manufacturing world. For example, sensors are being used to track inventories from the manufacturing floor up to a retail shelf or a customer’s door. These types of interconnected systems have been called the fourth industrial revolution, also known as Industry 4.0, and are projected to lower manufacturing costs. As industry moves toward these integrated technologies and lower costs, engineers will need to connect these systems via the Internet of Things (IoT). These engineers will also need to design how these connected systems interact with humans. The focus of this Special Issue is the smart sensors used in these human–robot collaborations

Collision Detection and Identification on Robot Manipulators Based on Vibration Analysis

Robot manipulators should be able to quickly detect collisions to limit damage due to physical contact. Traditional model-based detection methods in robotics are mainly concentrated on the difference between the estimated and actual applied torque. In this paper, a model independent collision detection method is presented, based on the vibration features generated by collisions. Firstly, the natural frequencies and vibration modal features of the manipulator under collisions are extracted with illustrative examples. Then, a peak frequency based method is developed for the estimation of the vibration modal along the manipulator structure. The vibration modal features are utilized for the construction and training of the artificial neural network for the collision detection task. Furthermore, the proposed networks also generate the location and direction information about contact. The experimental results show the validity of the collision detection and identification scheme, and that it can achieve considerable accuracy