Pneumatic systems are important fluid power transmission mechanisms in industrial automation. They use compressed air to transfer energy and signals. However, leaks, which are mainly from seal failures or degrading pipelines, cause many problems. These include unstable pressure, equipment failures, and lower production quality. To offset leaks, air compressors often run harder, wasting energy and raising costs. So, timely and reliable leak detection is crucial for system reliability, cost savings, and safety. This review systematically examines current pneumatic leak detection technologies. First, it covers system basics and common leak points. Then, it evaluates detection methods, from traditional sensors to advanced intelligent algorithms. These are judged on accuracy, speed, and ability to handle environmental interference. The article also discusses challenges in complex environments, the shift toward remote monitoring via multi-sensor fusion, and the Industrial Internet of Things (IIoT). This review provides a foundation for selecting the most effective detection methods and offers key insights for intelligent, energy-saving pneumatic maintenance systems.

In disaster relief operations, multiple UAVs can be used to search for trapped people. In recent years, many researchers have proposed machine le arning-based algorithms, sampling-based algorithms, and heuristic algorithms to solve the problem of multi-UAV path planning. The Dung Beetle Optimization (DBO) algorithm has been widely applied due to its diverse search patterns in the above algorithms. However, the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic, potentially leading to an inability to fully explore the search space and a tendency to converge to local optima, thereby not guaranteeing the discovery of the optimal path. To address these issues, we propose an improved DBO algorithm guided by the Landmark Operator (LODBO). Specifically, we first use tent mapping to update the population strategy, which enables the algorithm to generate initial solutions with enhanced diversity within the search space. Second, we expand the search range of the rolling ball dung beetle by using the landmark factor. Finally, by using the adaptive factor that changes with the number of iterations., we improve the global search ability of the stealing dung beetle, making it more likely to escape from local optima. To verify the effectiveness of the proposed method, extensive simulation experiments are conducted, and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm (GA), the Gray Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA) and the original DBO algorithm in the disaster search and rescue task set.