Research On UAV Path Planning Method Based On Complex Mission Environment

With the continuous development of aviation,computer and electronic technologies,the application scenarios of UAVs have become diversified and started to be widely used in military,agriculture,environmental protection,logistics and other fields.At the level of UAVs accomplishing tasks,the core problem is how to reach the destination safely through path planning.The existing methods still have some room for improvement in terms of planning efficiency,adaptability,autonomous operation capability,versatility and reliability.Especially for applications in some complex environments,it is essential for UAVs how to perform efficient path planning for target missions in complex environments and adjust the path with mission changes.Therefore,planning a reachable path with strong optimization performance and high versatility is the primary task of UAV applications.Based on the above analysis,this paper takes UAV as the research object,takes 3D environment map as the application scenario,and combines a variety of complex task models to study the path planning problem of UAV,and does the following parts of research work:1.The application scenario studied in this thesis is created,and the constraints of UAV flight are given according to the task requirements to lay the foundation for the subsequent research contents.2.The path planning problem based on the ant colony algorithm is studied in a single-task static environment.To solve the problems of slow search speed and easy to fall into local extremes in high-dimensional environment,an improved adaptive twoway ant colony algorithm is proposed by dividing the work of ant colony and setting a two-way ant colony search mechanism,improving the pheromone update principle of ant colony algorithm,and designing a probabilistic transfer model by combining with UAV motion constraints.The experimental results show that the algorithm has significantly improved the search ability of the path.3.In complex dynamic multi-tasks,this paper transforms the path planning problem into a target Optimization problem under multiple constraints,and uses the Hunter Prey Optimization(HPO)algorithm to optimize the path.The Adaptive Golden SA Hunter Prey Optimization(AGSHPO)algorithm is proposed to solve the problem that HPO algorithm is easy to fall into local extreme values,and the adaptive golden sa Hunter Prey Optimization(AGSHPO)algorithm is tested using a benchmark function.In the experiment,two kinds of sudden task changes are simulated,which shows that AGSHPO algorithm has fast optimization speed and strong adaptability,and can quickly plan efficient path for UAV under dynamic task changes.4.In dynamic obstacle environment,artificial potential field method is applied for local path planning.By improving the repulsion function,the problems of local extreme value and unreachable target in the artificial potential field method are solved.At the same time,in order to improve the speed of obstacle avoidance,fuzzy rules are introduced into the artificial potential field,so that the UAV can select different strategies according to the characteristics of obstacles,so that it can quickly escape from the dangerous area.5.Finally,the UAV experimental environment is built through Gazebo platform to verify the effectiveness of the planning algorithm studied in this paper in real flight.