This paper presents a UAV-based road illumination measurement system and evaluates its performance through experiments. The system employs a HUBSAN Zino 2+ UAV, STM32F103RCT6 microcontroller, BH1750 illuminance sensor, and GPS and integrates flight, processing, measurement, cloud platform, obstacle avoidance, communication, and power supply units via the OneNET cloud platform. Both hardware and software designs were implemented, using the Z-score algorithm to handle outliers in illumination data. The system showed a single-point measurement error rate of 1.14% and a MAPE of 5.08% for multi-point measurements. In experiments, the system’s horizontal and vertical illuminance RMSE were 1.92 lx and 1.75 lx, respectively. The real-time visualization interface improved operational efficiency, cutting labor costs by half and time costs by nearly four-fifths. UAV control and monitoring from the roadside ensured safety during measurements. The system’s efficiency and wide measurement range enabled extended experiments, collecting illuminance data across multiple horizontal and vertical planes. This resulted in the creation of both horizontal and innovative vertical-plane illuminance distribution maps. These findings provide valuable data for evaluating road lighting quality, enhancing road traffic safety, and improving road illumination design.

Road lighting is one of the largest consumers of electric energy in cities. Research into energy-saving street lighting is of great significance to city sustainable development and economies, especially given that many countries are now in a period of energy shortage. The control system is critical for energy-saving street lighting, due to its capability to directly change output power. Here, we propose a control system with high intelligence and efficiency, by incorporating improved YOLOv5s with terminal embedded devices and designing a new dimming method. The improved YOLOv5s has more balanced performance in both detection accuracy and detection speed compared to other state-of-the-art detection models, and achieved the highest cognition recall of 67.94%, precision of 81.28%, 74.53%AP${}_{50}$, and frames per second (FPS) of 59 in the DAIR-V2X dataset. The proposed method achieves highly complete and intelligent dimming control based on the prediction labels of the improved YOLOv5s, and a high energy-saving efficiency was achieved during a two week-long lighting experiment. Furthermore, this system can also contribute to the construction of the Internet of Things, smart cities, and urban security. The proposed control system here offered a novel, high-performance, adaptable, and economical solution to road lighting.

With the advancement of artificial intelligence technology, the product of its integration with robots:intelligent robots, is appearing more and more in people’s lives and work. Compared to the single function of industrial robots, intelligent robots have the ability to perceive, recognize and decide on the outside world. In order to achieve these functions, data needs to be collected by various types of sensors, and then processed by various algorithms. The introduction of these modules has increased the complexity of the system. Therefore, in order to reduce the complexity between the functional modules, reducing the coupling between modules, and improving the code reuse rate of the functional modules has become an urgent task. This paper proposes an improved FreeRTOS operating system have a driver layer, a middleware layer, and a functional module layer. The experimental results show that the scheme can support the maximum 2000hz module response frequency and the Topic data throughput rate of 2K Bytes per second.

In order to solve the problem that students have insufficient understanding of the full-field goniophotometer instrumentation due to the expensive equipment and the high requirements of the experimental environment, virtual reality technology is used to complete the virtual laboratory of a light intensity distribution measurement of the full-field goniophotometer through Unity 3D. The virtual laboratory is designed for near-field measurement, mid-field measurement, and far-field measurement of light intensity. It combines 3D MAX and C# to realize the model building of laboratory, luminaire and full-field goniophotometer, the dynamic drawing of light distribution curves, and the simulation system can also be run on mobile phones. The experimental process is mainly through the manipulation of the virtual experimenter and each module to generate collision detection, pop-up buttons, and click on the button triggers an event and the operation prompt page appears. The light distribution curve is generated dynamically by reading and processing the IES file data to generate 3D coordinates. This virtual simulation laboratory is demonstrated on multiple platforms. The results proved that the virtual simulation restored the experimental process vividly and can complete the experimental teaching remotely.