To address the demands of efficient forest fire detection and suppression, an adaptive multiple UAV swarm collaborative firefighting strategy considering dynamic forest fire spread and resource constraints was proposed in this paper. The multiple UAV swarm adaptive information-driven collaborative search (MUSAIDCS) algorithm and the resource-limited firefighting model were established. A temperature change-driven adaptive step-length search strategy is proposed to improve the accuracy of the search and detection of fire spots. The critical water flow rate required for fire suppression is calculated to evaluate the firefight performance, and an emergency bidding algorithm is applied to enable multiple UAV swarms collaborative firefighting under limited resources, including different payloads per UAV and swarm number. The comparative simulations for four search strategies indicate that the MUSAIDCS search strategy can significantly reduce task completion time and improve firefighting efficiency compared with the other traditional search strategies. Increasing payload quantity per UAV and swarm number can further enhance task completion efficiency and firefighting effectiveness. This study demonstrates that a resource-constrained collaborative firefighting strategy enables the dynamic allocation of UAV swarm resources under limited conditions and then optimizes firefighting performance within constraints.

The application of drones provides a powerful solution for “the last-mile” logistics services, while the large-scale implementation of logistics drone services will threaten the safety of buildings, pedestrians, vehicles, and other elements in the urban environment. The balance of risk cost and service benefit is accordingly crucial to managing logistics drones. In this study, we proposed a cost-benefit assessment model for quantifying risk cost and service benefit in the urban environment. In addition, a global heuristic path search rule was developed to solve the path planning problem based on risk mitigation and customer service. The cost-benefit assessment model quantifies the risk cost from three environmental elements (buildings, pedestrians, and vehicles) threatened by drone operations based on the collision probability, and the service benefit based on the characteristics of logistics service customers. To explore the effectiveness of the model in this paper, we simulate and analyse the effects of different risk combinations, unknown risk zones, and risk-benefit preferences on the path planning results. The results show that compared with the traditional shortest-distance method, the drone path planning method proposed in this paper can accurately capture the distribution of risks and customers in the urban environment. It is highly reusable in ensuring service benefits while reducing risk costs and generating a cost-effective path for logistics drones. We also compare the algorithm in this paper with the A* algorithm and verify that our algorithm improves the solution quality in complex environments.

The frequent occurrence of forest fires in mountainous regions has posed severe threats to both the ecological environment and human activities. This study proposed a multi-target firefighting task planning method of forest fires by multiple UAVs (Unmanned Aerial Vehicles) integrating task allocation and path planning. The forest fire environment factors such high temperatures, dense smoke, and signal shielding zones were considered as the threats. The multi-UAVs task allocation and path planning model was established with the minimum of flight time, flight angle, altitude variance, and environmental threats. In this process, the study considers only the use of fire-extinguishing balls as the fire suppressant for the UAVs. The improved multi-population grey wolf optimization (MP–GWO) algorithm and non-Dominated sorting genetic algorithm II (NSGA-II) were designed to solve the path planning and task allocation models, respectively. Both algorithms were validated compared with traditional algorithms through simulation experiments, and the sensitivity analysis of different scenarios were conducted. Results from benchmark tests and case studies indicate that the improved MP–GWO algorithm outperforms the grey wolf optimizer (GWO), pelican optimizer (POA), Harris hawks optimizer (HHO), coyote optimizer (CPO), and particle swarm optimizer (PSO) in solving more complex optimization problems, providing better average results, greater stability, and effectively reducing flight time and path cost. At the same scenario and benchmark tests, the improved NSGA-II demonstrates advantages in both solution quality and coverage compared to the original algorithm. Sensitivity analysis revealed that with the increase in UAV speed, the flight time in the completion of firefighting mission decreases, but the average number of remaining fire-extinguishing balls per UAV initially decreases and then rises with a minimum of 1.9 at 35 km/h. The increase in UAV load capacity results in a higher average of remaining fire-extinguishing balls per UAV. For example, a 20% increase in UAV load capacity can reduce the number of UAVs from 11 to 9 to complete firefighting tasks. Additionally, as the number of fire points increases, both the required number of UAVs and the total remaining fire-extinguishing balls increase. Therefore, the results in the current study can offer an effective solution for multiple UAVs firefighting task planning in forest fire scenarios.

Aircraft cargo compartment fires are one of the main threats to the safety of civil aircraft. In this study, a series of numerical simulations on the fire suppression performance of water mist in cargo compartments was carried out to examine the effects of the spray pattern, droplet size, and nozzle layout. The fire dynamics simulator (FDS) code was used to construct a fire suppression scenario in a full-scale aircraft cargo compartment. The results show that the extinguishment time of a corner fire was longer compared with center and sidewall fires due to the relatively larger distance from the nozzle and, therefore, a lower effective number of droplets reaching the flame area. Solid and hollow spray patterns showed significant differences in the spray coverage area. For a fixed flow rate, the hollow spray showed better fire suppression performance than solid spray. When the droplet size varied from 50 to 400 µm, the fire extinguishment time first increased and then decreased, corresponding to the dominant mechanism of the smothering effect of small droplets and the cooling effect of large droplets. In addition, the nozzle layout affected the water coverage on the ground of the cargo compartment. With an increase in nozzle number, the water mist flux was more evenly distributed and the fire extinguishment effect also increased.