Location-routing optimization for multifunctional unmanned aerial vehicles in post-earthquake scenarios

Multiple Objectives for UAVs Paths Optimization for a Disaster Area Reconnaissance

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.

3D Geodata Based Optimization of UAV Docking Stations in Mountainous Areas for Emergency Response

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.

Collaborative Disaster Rescue Strategy for Multi-UAV Systems Based on GOAPF-MARL and Proactive Replanning

To address multi-UAV task allocation and dynamic obstacle avoidance in post-disaster scenarios, this paper proposes a hierarchical GOAPF-MARL (Goal-Oriented Artificial Potential Field Guided Multi-Agent Reinforcement Learning) framework. In the perception layer, a task model is built to couple casualty level and disaster scale, forming a unified task weight W and enabling more accurate demand representation under varying conditions. For upper-layer allocation, an improved SADCK-Medoid method is used with the Balanced Residual Capacity (BRC) metric to enable nonuniform UAV deployment across five rescue regions. In the execution layer, a Goal-Oriented Artificial Potential Field (GOAPF) is embedded into multi-agent reinforcement learning (MARL), improving navigation around buildings, debris, and expanding fire zones while enhancing coordination and decision stability in complex scenes. Simulation results demonstrate improved efficiency and robustness compared with baseline methods under both ideal and challenging settings.

Priority-Aware Unmanned Aerial Vehicles Route Planning for Forest Fire Monitoring Using Bayesian-Tuned Hybrid Metaheuristics

Forest and land fires remain a recurring environmental challenge in large forested regions, where spatially dispersed hotspots make timely monitoring and mitigation difficult. Unmanned aerial vehicles (UAVs) provide a flexible platform for rapid aerial surveillance, but effective route planning must account for travel distance, endurance limits, hotspot distribution, and inspection priorities. This study proposes a priority-aware UAV route-planning framework for forest fire monitoring, with experimental evaluation based on hotspot data from Kalimantan, Indonesia. The framework includes hotspot organization, supports short-horizon replanning across two consecutive monitoring cycles, and generates map-based visual outputs to support operational interpretation. To efficiently solve the planning problem, this study adopts an evolutionary computation perspective by developing a Bayesian-optimized GA–VNS method that combines Genetic Algorithm search with Variable Neighborhood Search intensification. Experimental results on the Ketapang and Melawi datasets show that the Bayesian-tuned GA-VNS consistently delivers feasible routing performance, with the largest distance reduction reaching 19.4% over GA in the Melawi Cycle 2 case. Sensitivity analysis further shows that hotspot prioritization improves the inspection order of high-priority locations while preserving strong routing performance. Overall, the findings suggest that the proposed framework provides practical decision-support value for wildfire monitoring and UAV deployment planning.

Integrated Trajectory Planning, MEC Offloading, and Safety Coordination for Multi-UAV Disaster Response

Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments.

Station Deployment and Unmanned Aerial Vehicles Path Planning for Wind Farm Inspection Using Adaptive Bees Optimization Algorithm