In recent years, the collaborative operation of multiple unmanned aerial vehicles (UAVs) has been an important advancement in drone technology. The research on multi-UAV collaborative flight path planning has garnered widespread attention in the drone field, demonstrating unique advantages in complex task execution, large-scale monitoring, and disaster response. As one of the core technologies of multi-UAV collaborative operations, the research and technological progress in trajectory planning algorithms directly impact the efficiency and safety of UAV collaborative operations. This paper first reviews the application and research progress of path-planning algorithms based on centralized and distributed control, as well as heuristic algorithms in multi-UAV collaborative trajectory planning. It then summarizes the main technical challenges in multi-UAV path planning and proposes countermeasures for multi-UAV collaborative planning in government, business, and academia. Finally, it looks to future research directions, providing ideas for subsequent studies in multi-UAV collaborative trajectory planning technology.

The detachment of railway tunnel lining constitutes a grave danger to train operation safety and drastically curtails the tunnel’s service life. This study endeavors to efficiently detect the void defects in railway tunnel lining by creating a finite element model of tunnel lining structures. Utilizing this model, the study simulates the nonlinear acoustic wave propagation cloud maps for three representative tunnel lining structures: void-free, air void, and water void. This facilitates a thorough examination of the acoustic signal characteristics in the wavefield, time domain, and frequency domain. To satisfy the precision and efficiency demands of tunnel lining void detection, this study has devised and developed a portable acoustic detector that incorporates automatic analysis and processing capabilities and is furnished with a high-performance rare-earth magneto-strictive acoustic excitation device. This detection system can swiftly detect and assess typical void defects in tunnel lining. To further validate the effectiveness of this system, this study conducted lining defect detection in the Pingdao Railway Tunnel in the eastern Qinling Mountains. The test results show that the detection rate of this system for both air-filled and water-filled voids with a width of 1 m reached 100%, demonstrating its extremely high application value.