Abstract
An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.
Highlights
Different from traditional disaster rescue applications [1] which utilize 3D terrain reconstruction or ground target recognition, the multiple unmanned aerial vehicles (UAVs)based disaster rescue plan [2] utilizes the UAVs with distinct functions to realize people searching, complete injury identification, and provide medical assistance in the disaster zones [3]
It is supposed that the 2D shape of weather threat source is a circle; the circle center is set in the weather station and the circle radius comes from the expert advice which consider the terrain and local historical climate
Regarding genetic algorithm (GA) and adaptive genetic algorithm (AGA), the fitness function means the difference between the cost function and revenue function (see Equation (16)); the smaller the fitness function is the better the optimal computation effect will be
Summary
Different from traditional disaster rescue applications [1] which utilize 3D terrain reconstruction or ground target recognition, the multiple unmanned aerial vehicles (UAVs)based disaster rescue plan [2] utilizes the UAVs with distinct functions to realize people searching, complete injury identification, and provide medical assistance in the disaster zones [3]. Many factors will affect the rescue task including the environment factor [5] (such as the weather, terrain, and artificial facilities), UAV performance factor [6] (such as the maximum flight distance, oil consumption, or typical rescue function), and mission complexity itself. When implementing the disaster rescue using the multi-UAV-based system, how to assign the amount and combination mode of UAVs reasonably [7,8] and how to plan their flight paths are the issues that should be researched carefully. Much research has been done to solve the UAV-based mission assignment and path planning tasks. In [9], a distributed control law was developed to solve the issue of mission assignment of multi-UAV. The research on mission assignment and path planning can be transferred into the optimization modelling problem [12]. The proper cost-revenue function should be designed firstly, and some optimization methods can be developed to solve it
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