Dongguang Li

For complex tasks such as search and recovery in uncharted maritime areas, the use of heterogeneous unmanned swarms (UAVs and USVs) is highly promising, yet effective cross-domain cooperative trajectory planning remains a key challenge, often leading to mission delays. This paper proposes a rapid Cooperative Cross-domain Path Planning framework (CCPP) and its associated algorithm for heterogeneous UAV–USV swarms. The framework first establishes a visual-fusion modeling pipeline, converting visual pose estimation, uncertainties, and semantic dynamic obstacles into a planning representation with robust safety margins and time-varying risk fields. A hybrid velocity-path co-optimization algorithm is then designed to simultaneously generate curvature-feasible trajectories and speed profiles under heterogeneous kinematics and explicit temporal constraints. In the end, an adaptive interpretable decision tree acts as a meta-strategy for online replanning and real-time adjustment of modes and weights. To address the critical issue of uneven arrival time distribution, this paper introduces, inspired by economic inequality analysis, a normalized Gini coefficient-based arrival time consistency index to quantify and optimize coordination timing. Comprehensive experiments validate the effectiveness of the proposed approach in enhancing cooperative efficiency and real-time adaptability.

Geomagnetic navigation is a promising alternative for positioning and localization of UAV swarm systems in GNSS-denied environments. However, strong and heterogeneous electromagnetic interference generated by onboard power, propulsion, and electronic subsystems severely degrades magnetic measurement fidelity, limiting the achievable accuracy of cooperative UAV swarm navigation. To address this challenge, this paper proposes PG-TLNet, a physics-guided aeromagnetic interference compensation framework based on the extended Tolles–Lawson (T–L) model. By integrating onboard state information (current, voltage, and attitude) with magnetic measurements through physics-consistency constraints and a lightweight multi-branch convolutional neural network, the framework enables robust real-time compensation under strong and time-varying interference while remaining suitable for resource-constrained UAV nodes. Experimental validation using multiple scalar magnetometers under heterogeneous interference conditions, with amplitudes up to 1000 nT, shows that PG-TLNet consistently outperforms the conventional T–L model across all sensing nodes, maintaining residual magnetic interference at approximately 0–30 nT under long-duration and highly dynamic operations. The proposed method achieves an improvement ratio (IR) of up to 15 with an end-to-end inference latency below 94 μs. These results indicate that PG-TLNet meets the practical measurement fidelity requirements for geomagnetic navigation in GNSS-denied environments. By ensuring reliable and consistent magnetic measurements at the individual UAV node level, the proposed framework establishes a practical sensing foundation for geomagnetic navigation and distributed magnetic sensing in UAV swarm systems operating in GNSS-denied environments.

A lightweight aircraft visual navigation algorithm that fuses neural networks is proposed to address the limited computing power issue during the offline operation of aircraft edge computing platforms in satellite-denied environments with complex working scenarios. This algorithm utilizes object detection algorithms to label dynamic objects within complex scenes and performs dynamic feature point elimination to enhance the feature point extraction quality, thereby improving navigation accuracy. The algorithm was validated using an aircraft edge computing platform, and comparisons were made with existing methods through experiments conducted on the TUM public dataset and physical flight experiments. The experimental results show that the proposed algorithm not only improves the navigation accuracy but also has high robustness compared with the monocular ORB-SLAM2 method under the premise of satisfying the real-time operation of the system.

Technical motion recognition in cross-country skiing can effectively help athletes to improve their skiing movements and optimize their skiing strategies. The non-contact acquisition method of the visual sensor has a bright future in ski training. The changing posture of the athletes, the environment of the ski resort, and the limited field of view have posed great challenges for motion recognition. To improve the applicability of monocular optical sensor-based motion recognition in skiing, we propose a monocular posture detection method based on cooperative detection and feature extraction. Our method uses four feature layers of different sizes to simultaneously detect human posture and key points and takes the position deviation loss and rotation compensation loss of key points as the loss function to implement the three-dimensional estimation of key points. Then, according to the typical characteristics of cross-country skiing movement stages and major sub-movements, the key points are divided and the features are extracted to implement the ski movement recognition. The experimental results show that our method is 90% accurate for cross-country skiing movements, which is equivalent to the recognition method based on wearable sensors. Therefore, our algorithm has application value in the scientific training of cross-country skiing.

The occupancy guidance of Unmanned Aerial Vehicle (UAV) is one of the inevitable stages in the future air combat. Considering both constraints of missile launch condition and UAV flight performance, this paper established a multi-objective optimization model of occupancy guidance for UAV autonomous pursuit of enemy aircraft. The distance, angle and speed of occupancy guidance state are used as optimization variables to construct the advantage evaluation function. Based on the traditional heuristic algorithm, the Gradient Descent-Truncated Symbiotic Organizations Search (GDT-SOS) is designed to achieve rapid convergence of fitness values. By both numerical simulation and field test, the experiments demonstrated that UAV can autonomously achieve continuous, rapid and advantageous occupancy guidance under different dynamic initial conditions, which verifies that GDT-SOS has more effective than the comparison algorithm.

Liquid–liquid equilibrium data and the phase diagrams were measured for the ternary (neopentyl glycol + sodium formate + water) system at various temperatures of 303.15, 313.15 and 323.15 K. The effect of temperature on the conjugate phase and the tie-line length of the investigated system have been studied. The results showed that the length of the tie-line increases and the two-phase area was slightly increased by decreasing temperature. The fitting parameters of the Pirdashti and Merchuk equations were obtained with the temperature dependence expressed in the linear form, respectively. Compared with the Pirdashti equation, the binodal curves were satisfactorily described using the Merchuk equation at T = (303.15, 313.15 and 323.15) K, and the critical points at different temperatures were estimated by extrapolation. Furthermore, the Hand, Othmer-Tobias and Bachman models were used for the correlation of the phase behavior of the studied system. According to the reported correlation coefficients values, it was found that the measured tie-line data have acceptable consistency and the better fitness was with Othmer-Tobias equation. KEY WORDS: Liquid–liquid equilibrium, Neopentyl glycol, Phase diagram, Merchuk Bull. Chem. Soc. Ethiop. 2022, 36(4), 915-922. DOI: https://dx.doi.org/10.4314/bcse.v36i4.16

In preparation for the battlefields of the future, using unmanned aerial vehicles (UAV) loaded with multisensors to track dynamic targets has become the research focus in recent years. According to the air combat tracking scenarios and traditional multisensor weighted fusion algorithms, this paper contains designs of a new data fusion method using a global Kalman filter and LSTM prediction measurement variance, which uses an adaptive truncation mechanism to determine the optimal weights. The method considers the temporal correlation of the measured data and introduces a detection mechanism for maneuvering of targets. Numerical simulation results show the accuracy of the algorithm can be improved about 66% by training 871 flight data. Based on a mature refitted civil wing UAV platform, the field experiments verified the data fusion method for tracking dynamic target is effective, stable, and has generalization ability.