As one of the representative industries of new-quality productive forces, the low-altitude economy can not only foster new economic growth points, but also drive the development of multiple related industries such as low-altitude manufacturing, low-altitude flight, low-altitude support and integrated services. It is of great significance in boosting economic growth and promoting technological innovation. Intelligent obstacle avoidance decision-making and safety control of low-altitude Unmanned Aerial Vehicle (UAV) are prerequisites for ensuring the efficient and safe transportation of low-altitude UAV, and have thus become one of the research hotspots in the field of low-altitude economy in recent years. Based on existing research achievements at home and abroad, this paper reviews the research status of intelligent obstacle avoidance decision-making and safety control technologies for low-altitude UAV and prospects their future development directions. Specifically, it summarizes the key technologies that have been addressed from the aspects of safety obstacle avoidance decision-making for low-altitude UAV, safe path planning, safety control of low-altitude UAV under satellite signal denial, safety control of low-altitude UAV under disturbances, and safety control of low-altitude UAV under multiple constraints. Finally, the future research directions of intelligent obstacle avoidance decision-making and safety control for low-altitude UAVs are prospected.

This paper investigates the issue of fault-tolerant control for swarm systems subject to switched graphs, actuator faults and obstacles. A geometric-based partial differential equation (PDE) framework is proposed to unify collision-free trajectory generation and fault-tolerant control. To deal with the fault-induced force imbalances, the Riemannian metric is proposed to coordinate nominal controllers and the global one. Then, Riemannianbased trajectory length optimization is solved by gradient’s dynamic model–heat flow PDE, under which a feasible trajectory satisfying motion constraints is achieved to guide the faulty system. Such virtual control force emerges autonomously through this metric adjustments. Further, the tracking error is rigorously proven to be exponential boundedness. Simulation results confirm the validity of these theoretical findings.

With the increase of the number of agents in multi-agent systems and the rapid increase of the complexity of the overall structure of the system, the fault detection and diagnosis work has brought great challenges. Researchers have carried out considerable research work on fault detection and diagnosis of multi-agent systems, but there is no research on fault state estimation and diagnosis based on the information and state of the whole multi-agent system. Based on the global perspective of information geometry theory, this paper presents two new physical quantities of the information manifold of multi-agent systems, as Lagrangian and energy–momentum tensor, to express the state of the overall information of multi-agent systems, and to characterize the energy state and development trend of faults. In this paper, two new physical parameters are introduced into the research of multi-agent fault detection and diagnosis, and the fault state and trend of multi-agent system are evaluated from the global perspective, which provides more comprehensive theoretical support for designing more scientific and reasonable fault diagnosis and fault recovery strategies. Simulation of the application example confirms the competitive performance of the proposed method.