The rapid assessment of earthquake-stricken regions immediately after a seismic event is crucial for earthquake relief operations. Since unmanned aerial vehicles (UAVs) can quickly reach the affected areas and obtain images, they are widely used in the post-earthquake rapid assessment. However, sensor noise and other unavoidable errors can affect the quality of images acquired by sensors attached to the UAVs, which can, in turn, reduce the quality of the assessment. We defined a new problem in the application of multiple UAVs in the rapid assessment of earthquake-stricken regions. The rapid-assessment task-assignment problem (RATAP) was used to construct the assignment plan for multiple UAVs in a rapidassessment task while considering the weights of potential targets, the endurance of the UAVs, and the sensor errors. The RATAP was formulated as a variant of the team orienteering problem (TOP) called the revisitallowed TOP with reward probability (RTOP-RP). We then developed an efficient hybrid particle swarm optimization with simulated annealing (HPSO-SA) algorithm, which produced a high-quality solution for the RATAP, and confirmed the effectiveness and rapidity of our algorithm through numerical experiments. Finally, we conducted a case study based on real-world data from the 2008 Wenchuan earthquake in China to demonstrate our approach. INDEX TERMS Post-earthquake, multiple unmanned aerial vehicles, rapid-assessment task-assignment problem, target-revisit-allowed strategy.
Task allocation is the key factor in the spraying pesticides process using unmanned aerial vehicles (UAVs), and maximizing the effects of pesticide spraying is the goal of optimizing UAV pesticide spraying. In this study, we first introduce each UAV's kinematic constraint and extend the Euclidean distance between fields to the Dubins path distance. We then analyze the two factors affecting the pesticide spraying effects, which are the type of pesticides and the temperature during the pesticide spraying. The time window of the pesticide spraying is dynamically generated according to the temperature and is introduced to the pesticide spraying efficacy function. Finally, according to the extensions, we propose a team orienteering problem with variable time windows and variable profits model. We propose the genetic algorithm to solve the above model and give the methods of encoding, crossover, and mutation in the algorithm. The experimental results show that this model and its solution method have clear advantages over the common manual allocation strategy and can provide the same results as those of the enumeration method in small-scale scenarios. In addition, the results also show that the algorithm parameter can affect the solution, and we provide the optimal parameters configuration for the algorithm.
Wind has a significant effect on the control of fixed-wing unmanned aerial vehicles (UAVs), resulting in changes in their ground speed and direction, which has an important influence on the results of integrated optimization of UAV task allocation and path planning. The objective of this integrated optimization problem changes from minimizing flight distance to minimizing flight time. In this study, the Euclidean distance between any two targets is expanded to the Dubins path length, considering the minimum turning radius of fixed-wing UAVs. According to the vector relationship between wind speed, UAV airspeed, and UAV ground speed, a method is proposed to calculate the flight time of UAV between targets. On this basis, a variable-speed Dubins path vehicle routing problem (VS-DP-VRP) model is established with the purpose of minimizing the time required for UAVs to visit all the targets and return to the starting point. By designing a crossover operator and mutation operator, the genetic algorithm is used to solve the model, the results of which show that an effective UAV task allocation and path planning solution under steady wind can be provided.
In the last decade, with the wide application of UAVs in post-earthquake relief operations, the images and videos of affected areas obtained by UAVs immediately after a seismic event have become an important source of information for post-earthquake rapid assessment, which is crucial for initiating effective emergency response operations. In this study, we first consider the kinematic constraints of UAV and the Dubins curve is introduced to fit the shortest flyable path for each UAV that meets the maximum curvature constraint. Second, based on the actual requirements of post-earthquake rapid assessment, heterogeneous UAVs, multi-depot launching, and targets allowed access to multiple times, the paper proposes a multi-UAV rapid-assessment routing problem (MURARP). The MURARP is modeled as the multi-depot revisit-allowed Dubins TOP with variable profit (MD-RDTOP-VP) which is a variant of the team orienteering problem (TOP). Third, a hybrid genetic simulated annealing (HGSA) algorithm is developed to solve the problem. The result of numerical experiments shows that the HGSA algorithm can quickly plan flyable paths for heterogeneous UAVs to maximize the expected profit. Finally, a case study based on real data of the 2017 Jiuzhaigou earthquake in China shows how the method can be applied in a post-earthquake scenario.
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