Aug 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· Vol XLIX-B4-2026, pp. 399-406· 0 citations· 14 references
Abstract
Abstract. In recent years, the increasing frequency of natural disasters in remote and rugged areas has underscored the importance of unmanned aerial vehicles (UAVs) for rapid emergency response. This paper presents a novel approach for optimizing the placement of UAV docking stations in mountainous terrain for emergency operations. We develop a comprehensive, 3D Geodata framework that integrates 3D Digital Elevation Models (3D DEM), building infrastructure, and road network data to create a realistic three-dimensional optimization environment. The proposed system employs an Enhanced Adaptive Particle Swarm Optimization (EAPSO) algorithm with adaptive parameters, diversity maintenance mechanisms, and intelligent convergence detection to effectively handle the complex constraints of mountainous environments. Experimental results demonstrate that our 3D-aware EAPSO approach achieves superior performance in balancing coverage efficiency, energy consumption, and network connectivity compared to conventional optimization methods. The proposed system provides a scientific foundation for improving emergency response capabilities in challenging geographical environments.
The benefits of early reconnaissance in the event of emergency response can be invaluable for the success of the emergency teams present in the area. The use of unmanned aerial vehicles (UAVs), which are restricted only by weather conditions, introduces a path-planning optimization requirement. We model a disaster area as a set of rectangular regions, prioritized by the expected number of victims. UAVs, working as a team, provide complete coverage of all regions to gather information about conditions on the ground. This paper proposes a new set of objective functions to maximize the number of detected victims in the earliest stages of the search while minimizing completion time. A new solution representation is introduced together with problem-specific mutation operators. Experimental research using NSGA algorithms provides insight into how the model's features affect solution optimization.
S. Stachura, Jakub A. Grzeszczak, Artur Mikitiuk et al.· Proceedings of the Genetic a...· 0 citations
Damaged transportation infrastructure and delayed, making timely emergency response difficult. To address this, we develop a multifunctional UAV location–routing optimization model with time-window and energy constraints, where UAVs perform both relief delivery and damage reconnaissance. We propose an LLM-based adaptive large neighborhood search algorithm (LLM-ALNS-Par), in which an LLM configures hyperparameters to improve search efficiency and solution quality. Numerical experiments show that LLM-guided hyperparameter settings outperform Bayesian optimization and ant colony optimization. These results demonstrate the practical value of the proposed modeling and LLM-ALNS-Par framework for post-disaster UAV emergency planning.
Yan He, Pan He, Dongqing Zhang· International Conference on...· 0 citations
This paper, for the first time, applies the Divine Religions Algorithm (DRA) to three-dimensional UAV path planning. Targeting the complex terrain of urban-mountain mixed environments, we propose a novel method that incorporates multiple enhancements, including A* initialization, single-point disturbance mutation, and adaptive weighting. First, the A* algorithm is employed to generate high-quality initial paths, serving as the skeleton of the population. Innovative mechanisms such as terrain-adaptive disturbances and dynamic weight adjustment are integrated to achieve both efficiency and robustness in path optimization. Comparative experiments with Genetic Algorithm (GA) and Crowned Porcupine Optimization (CPO) show that the improved DRA algorithm exhibits significant advantages in terms of path length, safety margin, average altitude variation, average turning angle, and overall cost function. It consistently obtains superior paths and achieves faster convergence. The results demonstrate that the proposed approach provides an efficient, adaptive, and practical intelligent optimization tool for UAV path planning in urban-mountain mixed or similarly complex environments, offering promising prospects for engineering applications.
Lianyu Fang, Wenjun Yi· SAE technical paper series· 0 citations
Efficient path planning for multiple unmanned aerial vehicles (UAVs) is essential in disaster relief operations, where rapid response and improved survival rates are critical. Conventional metaheuristic algorithms frequently experience premature convergence and an imbalance between exploration and exploitation, especially in complex and densely constrained environments. This study introduces an Adaptive Particle Swarm Optimization (APSO) approach for cooperative multi-UAV trajectory planning in hazardous scenarios. The method utilizes a performance-driven adaptation mechanism that dynamically adjusts each particle’s inertia weight according to its fitness relative to the population average and the global best solution. This mechanism enhances exploration for low-performing particles and ensures precise exploitation for high-performing ones. The path planning problem is formulated as a multi-objective optimization task, incorporating trajectory smoothness, altitude stability, hazard avoidance, and path length. Simulation results in diverse and complex disaster environments demonstrate the effectiveness of the proposed approach. Specifically, the method achieves approximately 6% and 12% reductions in total path length compared to standard Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), respectively. In more challenging scenarios, it further surpasses conventional PSO, achieving up to an 11% improvement in mission efficiency. These findings indicate that the adaptive strategy substantially enhances trajectory safety and operational performance, establishing it as a robust and reliable solution for autonomous multi-UAV coordination in disaster response applications.
Muhammad Haris, Haewoon Nam· International Conference on...· 0 citations
Abstract. This study presents the first systematic field evaluation of dock-based UAV (Uncrewed Aerial Vehicle) systems for geohazard monitoring in mountainous terrain. We assess their potential to provide reliable, high-frequency, and automated monitoring of surface changes across three different hazard scenarios: (1) a fast-moving glacier icefall (Supphellebreen, Norway), (2) an unstable rock slope (Skjøld, Norway), and (3) a post-failure landscape resulting from a catastrophic rock-ice avalanche (Blatten, Switzerland). Effective hazard management requires timely detection of displacement patterns and terrain change. To address these issues, we introduce an automated workflow integrating multitemporal UAV dock data acquisition with an end-to-end processing pipeline for displacement field generation and change detection. The results show that this workflow has the potential to provide data at centimetre-level accuracy before, during, and after hazard events, supporting both precautionary risk assessments and timely decision-making in critical phases of potential hazard evolution. Wider adoption will depend on supportive regulatory frameworks, reliable power and communication infrastructure, and sufficient expertise to ensure effective operation, maintenance, data interpretation and risk management. Overall, dock-based UAV systems represent a significant technological advancement in efficient geohazard monitoring, facilitating rapid response in critical situations, thereby contributing to increased resilience of communities living in vulnerable mountain environments.
A. Maschler, S. Langes, Lukas Schild et al.· Natural Hazards and Earth Sy...· 0 citations
To address the limitations of traditional path planning methods in complex terrains, such as poor safety, low efficiency, and insufficient adaptability, this paper proposes a three-dimensional UAV path planning method based on the Manta Ray Foraging Optimization (MRFO) algorithm for rescue missions in complex mountainous environments. First, a three-dimensional safe map model is constructed by integrating terrain information and no-fly zone constraints. On this basis, multiple flight constraints including flight altitude, no-fly zone avoidance, climbing gradient, turning slope, and overload are comprehensively considered. A multi-objective weighted cost function is designed to evaluate path length, altitude stability, and path smoothness. Furthermore, the MRFO algorithm simulates three foraging behaviors of manta rays: chain foraging, spiral foraging, and somersault foraging. Combined with adaptive weight adjustment and boundary handling strategies, efficient path optimization is achieved. Simulation results demonstrate that the proposed method can rapidly generate safe, smooth, and energy-efficient rescue paths that satisfy UAV dynamic constraints. The approach significantly improves mission execution efficiency and safety in complex mountainous environments.
Xingkun Wu, Jingyi Huang· International Conference on...· 0 citations