In the swiftly evolving landscape of Internet of Things (IoT) technology, the demand for adaptive non-contact sensing has seen a considerable surge. Traditional human perception technologies, such as vision-based approaches, often grapple with problems including lack of sensor versatility and sub-optimal accuracy. To address these issues, this paper introduces a novel, non-contact method for human presence perception, relying on WiFi. This innovative approach involves a sequential process, beginning with the pre-processing of collected Channel State Information (CSI), followed by feature extraction, and finally, classification. By establishing signal models that correspond to varying states, this method enables the accurate perception and recognition of human presence. Remarkably, this technique exhibits a high level of precision, with sensing accuracy reaching up to 99 $$\%$$% . The potential applications of this approach are extensive, proving to be particularly beneficial in contexts such as smart homes and healthcare, amongst various other everyday scenarios. This underscores the significant role this novel method could play in enhancing the sophistication and effectiveness of human presence detection and recognition systems in the IoT era.

To reliably realize the functions of autonomous navigation and cruise of logistics robots in a complex logistics storage environment, this paper proposes a new robot navigation system based on vision and multiline lidar information fusion, which can not only ensure rich information and accurate map edges, but also meet the real-time and accurate positioning and navigation in complex logistics storage scenarios. Simulation and practical verification showed that the robot navigation system is feasible and robust, and overcomes the problems of low precision, poor robustness, weak portability, and difficult expansion of the mobile robot system in a complex environment. It provides a new idea for inspection in an actual logistics storage scenario and has a good prospective application.

The scene of large factories is complex, and the requirements for production safety are higher. At present, complex factories mainly use manual operation mode to carry out safety inspection, that is, manual inspection and manual recording mode to inspect factory equipment, which has the disadvantages of high labor intensity, low work efficiency and unstable inspection quality. In recent years, with the development of robot navigation related technologies, it has provided a new idea for solving autonomous navigation in large-scale and complex factory environment with variable obstacles. Therefore, we design a slam and path planning method based on multi-sensor fusion, including the establishment of complex and large-scale factory maps, dynamic obstacle avoidance and local path planning, which can achieve accurate and efficient autonomous positioning and effectively generate smooth and safe paths. The security, smoothness, flexibility and efficiency of this method are verified in various complex simulation scenarios and challenging real world. It is proved that this method can meet the performance requirements of positioning and path planning in large-scale complex industrial scenes, and has a good application prospect.