Um planejador de rotas para múltiplos veículos aéreos não-tripulados

Abstract

Planning a trajectory that consider limitations of aircraft maneuvers is an important feature of any Mission Planner. The complexity increases in the presence of multiple aircraft and scenarios with multiple targets. The problem how to decide the number of aircraft launched in order to efficiently cover all necessary points creates an interesting problem to be studied. Runtime mission, resources, and the number of vehicles to be launched are all minimized the Same time. The problem then becomes increasingly critical, when the scenario mission does not allow the aircraft back off or re-plan the path, and the flight plan onboard on autopilot Air Vehicle Unmanned (UAV) probably It will be the last in the case of failure. One of these application scenarios is monitoring both air of a region not explored the Amazon rainforest. The extent of forest, the complete lack of access to its interior and uniform standards of treetops define a mission without success usually means total loss of equipment. In such situations, careful planning for each vehicle is a factor critical to the overall success of the mission. A common problem is to consider limitations side manobas when the route is planned. Although a human pilot can act to radically change the direction of the path, when we consider UAVs, limiting abrupt actions is recommended because without it you can add a instability in both the laterals and longitudinal controls. Therefore, when planning the trajectory, it is desirable that the points that define consectivos a curve with acceptable angles, and acceptance related to the dynamics of aircraft. Another common problem is how to balance the mission runtime Large areas squadron in hazardous areas. This paper presents an approach based on Genetic Algorithms (GA) to solve the routing problem Vehicle (PRV) for multiple UAVs conducting a monitoring mission multiple points in a formulation bi criteria: minimize the amount vehicles air, while the mission time is minimized.