International Core Journal of Engineering 2020-26 | Page 147

2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) An UAV Path Planning method in Complex Mountainous Area Based on a澳New澳Improved Ant Colony Algorithm Ma Ziyuan Gong Huajun Wang Xinhua Nanjing University of Aeronautics and Astronautics Automation College Nanjing, China [email protected] Nanjing University of Aeronautics and Astronautics Automation College Nanjing, China [email protected] Nanjing University of Aeronautics and Astronautics Automation College Nanjing, China [email protected] to improve the problems of orbit sand in recent years, and have achieved good results. The traditional optimization algorithm has clear physical significance, but with the expansion of the region, it will increase its arithmetic complexity on an exponential scale. "The algorithm is the best solution in computing and large-scale local land for ant colony compact cars that are prone to traps and need to be corrected. Therefore, in recent years, scientists have tried to combine traditional algorithms with intelligent algorithms to make full use of the advantages of algorithms and to make full use of the combination. In addition, finding layout algorithms is usually the shortest route to the network map, which is not enough. Depending on the planned route where the drone is difficult to fly, the drone considers itself bound by performance conditions. Abstract—The route planning of unmanned aerial vehicles (UAVs) is being studied to carry out emergency transport missions in mountainous areas. Based on performance analysis and drone comparison, the route planning constraints are discussed, and route safety is proposed. The ant colony algorithm first establishes Tyson polygons based on mountain positions and obtains feasible solutions for drones that avoid mountain obstacles. Second, in order to avoid dense mountain areas, the safety of the orbit is limited and feasible solutions are reduced. In addition, the shortest path is searched using the ant colony algorithm, and finally, eliminate unnecessary obstacles in the path to shorten the distance, and gain a comprehensive understanding of the drone performance criteria to smooth corners to optimize the actual stroke. safe way. The example analysis shows that the improved ant colony algorithm converges faster than the traditional algorithm and produces a shorter path. Based on the above analysis, an improved ant colony algorithm is proposed, which considers path safety. It leverages the advantages of Tyson Polygon physics concepts and simple programming to obtain the initial solution. Ant colony algorithms are used to calculate high efficiency and optimize search solutions. According to the characteristics of emergency handling in mountainous areas, the optimal path has been unmanned due to the limitation of aircraft performance, which can be used in actual flight, so as to improve the efficiency of material transmission and improve the formation of the ideal path of mountain emergency rescue. Keywords—air transport; path planning; improved ant colony algorithm; unmanned population; transportation of emergency supplies I. I NTRODUCTION A drone is a drone that can be used in its programs to perform a variety of tasks using a wireless remote control and control device. Compared with manned aircraft, the uav or has more maneuverability, light weight, small size and less space. In the mountain area sudden disasters, especially in the case of difficult to regulate earthquakes, mudslides and other website disasters, high utilization of significant advantages, in a short period of time, can be rapid response by drones and carry out emergency supplies, such as medical transport rescue kits. This mission is essential to saving lives and property. II. UAV PERFORMANCE RATIO AND PATH PLANNING CONSTRAINTS A. Drone Performance Analysis and Selection Depending on the function, the size of the drone varies greatly. In the aftermath of the mountain disaster, small items (e.g. medical equipment, food, tents, etc.) are closely related to the lives and lives of the affected population, and the demand for timing is becoming more and more intense. Therefore, this article focuses on light and small drones that can be used to transport these materials. Drone route planning refers to the best route according to the characteristics of the drone and its surrounding environment. Depending on the scenario, the UAV path mapping algorithm exists in the target and constraint. According to the algorithm, it can be divided into two types: traditional planning algorithm and intelligent optimization algorithm. Traditional layout algorithms include short path optimization algorithms and dynamic planning methods for weighted graphs. Planning classic path algorithms, including intelligent optimization algorithm genetic algorithms and neural network methods and improved particle and ant colony algorithms, especially ant colony algorithms, have been used 978-1-7281-4691-1/19/$31.00 ©2019 IEEE DOI 10.1109/AIAM48774.2019.00032 Depending on the flight platform structure, drones can be divided into fixed-wing drones and drones. Fixed-wing drones provide lift with extended wings. Wings are relatively stable, have long battery life and fly fast. Fast features, but take-off and landing, requires a lot of space. The various rotor aircraft used in civil aviation depend on the fan rotation on each lever to produce lift, right-angle turn and vertical climbing 125