Design Of The UAV Ground Station Software And Study On A Path Planning Algorithm

The military field is increasingly dominated by Unmanned Aerial Vehicles (UAVs). UAV ground control station, which is an important part of the UAV system, is one of the hot research fields of many universities, organizations and research institutions. For UAVs, path planning is to seek the optimal or sub-optimal path from the beginning to the end under some constraints, and is one of the most important technologies to achieve autonomous flight. This thesis focuses on the design and implement of UAV ground control station software and the research of the path planning algorithm.For the UAV ground control station software, this thesis presents the design and implementation of a UAV ground control station software, on the basis of some key programming technologies involved. There are four main functional modules in the UAV ground control station software:user interface, serial communication, navigation electronic maps and path planning. In this thesis, based on DotSpatial, we realize some basic functions and, based on ArcGIS, we realize the path planning. In addition, the thesis optimizes the user interface, realizes the task management functions, the task playback function, and the payload control function.For path planning, the A-Star algorithm is an important algorithm; however, there exist some issues in the traditional A-Star algorithm, such as possibility of local-loop, inability to meet the flight constraints of the aircraft, and the unsmoothed planned path. To address these issues, the thesis presents an improved A-Star algorithm by adding the yaw angle constraint to the cost function.The UAV ground control software has been applied to a series of experiments for two UAV projects. The application results show that the UAV ground control station software can transmit telecommands to the UAV, receive telemetry information from the UAV and demonstrate the telemetry information correctly, reliably, and timely. A series of simulations have been conducted for the improved A-Star algorithm and show that it improves the overall efficiency of the planning system, and has strong optimization ability, high search ability and adaptability.