Heterogeneous unmanned systems consisting of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) have great application potential in marine environments. At present, the fully autonomous recovery of UAVs is a key problem that restricts any significant application of a heterogeneous unmanned system. This paper presents a novel fully autonomous recovery system, covering the entire process of recovery of small fixed-wing UAVs on mobile platforms at sea. We describe methods or solutions for the key problems encountered by the current system, including active modeling of the UAV–USV heterogeneous platform motion model, accurate estimation of the highly dynamic relative motion of the heterogeneous platform, dynamic analysis of the arresting cable system, and compliance control of the manipulator recovery system. Based on these methods, a physical simulation platform for the fully autonomous recovery system, including an actively adjustable arresting cable, manipulator compliance recovery system, and other subsystems, is developed and verified through experiments. The experiments show that the system proposed in this study can achieve full autonomous recovery of a small ship-based fixed-wing UAV with a high success rate in a short period. This system is the foundation for practical applications of UAV–USV heterogeneous unmanned systems in the marine environment.

Ferroresonance is an important factor affecting the security and stability operation of Gas Insulated Switchgear (GIS) substation. The nonlinearity of excitation characteristic of inductance elements with iron cores is the fundamental cause for exciting ferroresonance. A 500kV GIS substation is taken into account in this paper, a variety of operating conditions which may excite ferroresonance during the commissioning process are analyzed by a numerical simulation method. The comparison with actual commissioning results shows that ferroresonance will be excited by opening circuit breakers to switch off no-load buses during the commissioning process. For single-phase electromagnetic potential transformer (PT), ferroresonance can be effectively suppressed by connecting damping resistance in parallel with residual winding temporarily.

Many problems in the real world can be attributed to the problem of multiagent. The study on the issue of multiagent is of great significance to solve these social problems. This paper reviews the research on multiagent based real-time strategy game environments, and introduces the multiagent learning environment and related resources. We choose a deep learning environment based on the StarCraft game as a research environment for multiagent collaboration and decision-making, and form a research mentality focusing mainly on reinforcement learning. On this basis, we design a verification platform for the related theoretical research results and finally form a set of multiagent research system from the theoretical method to the actual platform verification. Our research system has reference value for multiagent related research.

Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieving long-term localization under varying environmental conditions and changing viewpoints. It depends on a brute-force, time-consuming sequential matching method. We propose MRS-VPR, a multi-resolution, sampling-based place recognition method, which can significantly improve the matching efficiency and accuracy in sequential matching. The novelty of this method lies in the coarse-to-fine searching pipeline and a particle filter-based global sampling scheme, that can balance the matching efficiency and accuracy in the long-term navigation task. Moreover, our model works much better than SeqSLAM when the testing sequence has a much smaller scale than the reference sequence. Our experiments demonstrate that the proposed method is efficient in locating short temporary trajectories within long-term reference ones without losing accuracy compared to SeqSLAM.