Abstract
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.
Highlights
At present, unmanned aerial vehicles (UAVs) are widely used by the military and civilians
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
In order to integrated optimize the task allocation and path planning of fixedwing UAV, the steady wind environment was introduced into the optimization model, and the VS-DP-vehicle routing problem (VRP) model was established considering the dynamic constraints of UAV
Summary
At present, unmanned aerial vehicles (UAVs) are widely used by the military and civilians. The integrated optimization of UAV task allocation and path planning under steady wind can be described as follows: In a windy environment, UAVs depart from the common starting point to visit multiple targets, and each target can only be visited once by one UAV. Constrained by factors such as physical characteristics [13] of UAV, the task allocation scheme and the optimal flight path are obtained, which enable UAVs to complete all the tasks and return to the starting point in the shortest time.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
