UAV path planning, as a key technology in the field of automatic control and intelligent systems, has demonstrated significant potential in various applications, including logistics and distribution, environmental monitoring, and emergency rescue. A comprehensive reassessment of the existing representative literature reveals that most reviews in this field focus on specific aspects and are largely confined to methodological investigations, primarily qualitative analyses that lack empirical data to support their conclusions. To address this gap, this study employs the mapping knowledge domain (MKD) method of bibliometrics, utilizing CiteSpace, VOSviewer, and Bibliometrix R package to analyze a total of 4416 documents from the Web of Science Core Collection (WOSCC) spanning from 2000 to 2024. Through retrospective analysis and scientific knowledge mapping, we first review the development of UAV path planning and categorize it into four distinct stages. Secondly, we identify key external features of the field. Using techniques such as co-citation analysis and keyword clustering, we then identify research trends, burst papers, and hotspots. Finally, we highlight five typical application scenarios of UAV path planning. The results of the study indicate that the field of UAV path planning has made significant advancements over the past two decades, particularly since 2018. These studies encompass various disciplinary areas, underscoring the increasing necessity for the integration of multidisciplinary approaches to UAV path planning in recent years. The aim of this study is to provide researchers with a comprehensive reference and new research perspectives while offering technical guidelines for professionals working in related applications.

To tackle the challenges of path planning for unmanned aerial vehicle (UAV) in complex environments, a global–local balanced whale optimization algorithm (GLBWOA) has been developed. Initially, to prevent the population from prematurely converging, a bubble net attack enhancement strategy is incorporated, and mutation operations are introduced at different stages of the algorithm to mitigate early convergence. Additionally, a failure parameter test mutation mechanism is integrated, along with a predefined termination rule to avoid excessive computation. The algorithm’s convergence is accelerated through mutation operations, further optimizing performance. Moreover, a random gradient-assisted optimization approach is applied, where the negative gradient direction is identified during each iteration, and an appropriate step size is selected to enhance the algorithm’s exploration capability toward finding the optimal solution. The performance of GLBWOA is benchmarked against several other algorithms, including SCA, BWO, BOA, and WOA, using the IEEE CEC2017 test functions. The results indicate that the GLBWOA outperforms other algorithms. Path-planning simulations are also conducted across four benchmark scenarios of varying complexity, revealing that the proposed algorithm achieves the lowest average total cost for flight path planning and exhibits high convergence accuracy, thus validating its reliability and superiority.

Aiming at the calculation of secure multicast routing in multi-domain optical network, a new secure multicast routing algorithm based on distributed path calculation element was proposed. Firstly, the distributed PCE multicast routing model was constructed, and the intra-domain cost, inter-domain cost, maximum available wavelength and related constraints were defined. Secondly, the pheromone update strategy of ant colony algorithm with ant queen was locally simplified and modified to make it suitable for the distributed PCE architecture of multi-domain optical network. At the same time, by optimizing the trust value calculation method in the lightweight cross domain trust model, the algorithm can be applied to the calculation of trust value of multicast routing in multi domain optical networks. Finally, the specific implementation steps of the algorithm were given. The analysis and experimental results show that the algorithm can not only ensure the security of cross domain multicast routing, but also prevent the malicious nodes from destroying. Compared with the typical multicast routing algorithm, it has better performance in multicast tree cost and blocking rate in malicious node environment.

Proactive beam management is essential for high-mobility millimeter-wave communication systems, where transmission latency may cause beams selected from current observations to become mismatched with the optimal beams at future transmission times. This paper investigates radar-only causal multi-horizon proactive beam prediction, aiming to directly rank future communication beams from past radar observations. To this end, we propose Shared-Beam-GCN-TCN, a multi-task framework that learns a shared predictive sensing-communication representation and performs structured decoding in the beam space. Specifically, four consecutive range-angle radar maps are encoded by a frame-wise convolutional neural network and a causal temporal convolutional network to capture short-term motion dynamics. The shared temporal representation is jointly optimized for future beam prediction and short-term radar prediction, encouraging the extraction of predictive sensing dynamics relevant to future communication states. A beam-space graph convolutional decoder then exploits the adjacency structure among 64 beam classes to model correlations between neighboring beams and improve future beam ranking. The framework is evaluated in two high-mobility mmWave communication scenarios, including a trajectory-disjoint DeepSense 6G vehicular scenario and a simulated unmanned aerial vehicle (UAV) scenario. In the vehicular scenario, Shared-Beam-GCN-TCN achieves the best overall performance across different prediction horizons, improving the average Top-3 and Top-5 accuracies by 8.2 and 6.9 percentage points over Seq-CNN-TCN, while reducing the mean beam-index error. In the UAV scenario, the proposed framework achieves an accuracy of 93.4% and a beam-index error of 1.022. These results demonstrate the effectiveness of shared predictive representation learning and structured beam-space decoding for causal multi-horizon beam prediction in high-mobility environments.

In order to study the influence of Rayleigh and Brillouin scattering on the performance of bidirectional Wavelength Division Multiplexing (WDM) system. On the VPI simulation platform, a two-way WDM simulation system was built. Rayleigh scattering and Brillouin scattering were generated by setting the parameters of the optical fiber module and system parameters such as Brillouin Scattering, Rayleigh scattering, Rayleigh Distortion, and Rayleigh Distortions. Then, the frequency spectrum of the received signal and the bit error rate were observed and analysed. Experimental results show that Rayleigh and Brillouin scattering will cause the performance of the two-way WDM system to degrade. By separating the back propagation channel, the impact of these two effects on the performance of the two-way WDM system can be reduced.

In order to solve the problem that crosstalk attack propagation leads to a large number of interference alarms in multi domain optical network and difficult positioning, the detection and screening mechanism based on attack optical path state discrimination and the improved MD-PLVM (multi domain parallel and limited vector Matching) algorithm for multi domain optical network are used in this paper, a new crosstalk attack detection and location scheme based on distributed PCE called DP-CADL (distributed PCE crosstalk attack detection and location) is proposed. The experimental results show that this scheme can not only eliminate the interference alarm and screen the original attack optical path accurately, but also realize the fast location of multiple crosstalk attack sources between domains and cross domain optical path, and has high location accuracy.