Autonomous docking and recharging are among the critical tasks for autonomous mobile robots that work continuously in manufacturing environments. This requires robots to demonstrate the following abilities: (i) detecting the charging station, typically in an unstructured environment and (ii) autonomously docking to the charging station. However, the existing research, such as that on infrared range (IR) sensor-based, vision-based, and laser-based methods, identifies many difficulties and challenges, including lighting conditions, severe weather, and the need for time-consuming computation. With the development of deep learning techniques, real-time object detection methods have been widely applied in the manufacturing field for the recognition and localization of target objects. Nevertheless, those methods require a large amount of proper and high-quality data to achieve a good performance. In this study, a Hikvision camera was used to collect data from a charging station in a manufacturing environment; then, a dataset for the wireless charger was built. In addition, the authors of this paper propose an autonomous docking and recharging method based on the deep learning model and the Lidar sensor for a mobile robot operating in a manufacturing environment. In the proposed method, a YOLOv7-based object detection method was developed, trained, and evaluated to enable the robot to quickly and accurately recognize the charging station. Mobile robots can achieve autonomous docking to the charging station using the proposed Lidar-based approach. Compared to other methods, the proposed method has the potential to improve recognition accuracy and efficiency and reduce the computation costs for the mobile robot system in various manufacturing environments. The developed method was tested in real-world scenarios and achieved an average accuracy of 95% in recognizing the target charging station. This vision-based charger detection method, if fused with the proposed Lidar-based docking method, can improve the overall accuracy of the docking alignment process.

Sharp, strong luminescence has been obtained from erbium in silicon-germanium quantum well structures. The quantum well layers were implanted with erbium, followed by amorphisation using a silicon implant at 77 K. The structures were then recrystallised by solid phase epitaxial regrowth at 550°C. Compared with bulk silicon systems, stronger erbium activity was observed in the quantum well host.

Rotary comb-drive electrostatic actuators with virtual pivotal point of rotation were designed and fabricated for their application in external cavity tunable lasers in Littman configuration. Silicon on insulator (SOI) wafers with device layer thickness of 50 μm and buried oxide layer of 2 μm were used. The structures were fabricated through bulk micromachining of the device layer followed by release of movable arms through wet etching. Six pairs of comb arms were used to symmetrically distribute electrostatic forces on an interconnected movable arm structure. The movable structure consisting of stiff truss connectors in arc shapes on inner and outer periphery was anchored through three beams. Actuation of the movable arms on a circular profile with a virtual pivotal point was achieved for the designed range of operation. A maximum rotation of ± 1.5 degrees at 190 V was achieved. The dimensions of the movable arm are on the order of 1.2-1.5 mm. The actuators have a physical clearance of up to 2.8 mm from the device structure to the virtual pivot point. The sidewalls of the movable arms are accessible for optical applications. The geometrical configuration of the fabricated structures supports the stringent requirements of optical path alignments in external cavity lasers. The fabricated structures can be reliably operated in the kilohertz range