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.

Defending against adversarial unmanned aerial vehicle (UAV) swarms presents a critical challenge for modern security systems, requiring coordinated strategies under partial observability. Existing multi-agent reinforcement learning (MARL) and spatio-temporal graph neural networks (ST-GCN) methods typically treat agents as homogeneous nodes, failing to effectively model large-scale, dynamic spatio-temporal dependencies to support cooperative decision-making, and lacking semantic interpretation of adversarial tactics. To address this, we propose the Spatio-Temporal Attention Graph-Enhanced policy optimization (STAGE). Unlike ST-GCN that uses fixed or predefined graph structures, STAGE models the diverse swarm topology through a learnable dynamic adjacency matrix and a multi-hop neighborhood aggregation mechanism to capture dependencies at different ranges. Moreover, to overcome the black-box nature of existing adversarial analysis, we design a dual-path intent recognition framework, which concatenates cluster-level features from graph attention networks with computed tactical metric vectors, and trains a classifier for four types of swarm tactical intents, enabling explicit recognition of enemy intent by the defenders. The policy is optimized end-to-end within the MAPPO framework through a structured multi-objective reward function. Extensive experiments in multi-scale area defense scenarios demonstrate that STAGE outperforms state-of-the-art methods in task performance and tactical comprehension robustness.

This paper reviews and analyses the research progress in the field of UAV path planning. Firstly, the importance of UAV path planning and the current research work related to UAV path planning are introduced. Then how UAV path planning is modelled is analysed and key issues to be considered are given. Finally, classical search algorithms, evolutionary algorithms, heuristic search-based algorithms and deep learning methods are analysed in UAV path planning. For each method, its principle, characteristics, advantages and disadvantages, and applicable scenarios are analysed. The aim of this paper is to provide a comprehensive overview for researchers and scholars in the field of UAV path planning in order to promote the development and application of related technologies.

Confronted with the challenges of hierarchical decision-making and dynamic strategy adaptation in UAV swarm defense, this paper introduces HO-SAP, a novel algorithm that synergizes hierarchical and opponent modeling. Our approach structures swarm decisions into tactical and execution layers and employs a learning-based strategy estimation mechanism to infer opponent behavioral patterns from observation sequences, embedding this awareness into the value function for superior generalization. We train a BiLSTM-based classifier to identify opponent strategy types through supervised learning, enabling defenders to adapt their tactics accordingly. Results across diverse adversarial scenarios reveal that HO-SAP significantly outperforms existing methods in critical metrics like defense success rate and interception efficiency, exhibiting exceptional adaptability and tactical planning. The hierarchical model ensures rational and scalable decisions, while opponent modeling enables dynamic responses, establishing an effective learning framework for swarm adversarial tasks.