Path Planning Method For Forestry Monitoring UAV In Below-canopy

Unmanned aerial vehicle(UAV)is a monitoring platform,which can carry various monitoring equipment,with high mobility and low cost.It has broad application prospects in forestry information monitoring.Compared with the open upper-canopy environment,UAV faces the big challenges including large uncertainty,uneven distribution of obstacles and local area with dense obstacles when flying in below-canopy.In order to satisfy the actual requirements of forestry monitoring mission for UAV in below-canopy,this paper focuses on path planning algorithm which can achieve efficient and safe path for UAV in dynamic and unstructured below-canopy environment.The main contributions of this paper are as follows:1.The UAV model is established and the UAV path planning problem in below-canopy is formulated.The rotation model and rotation matrix of UAV are deduced by Newton-Euler method,and the kinematics model of UAV is finally obtained.According to the characteristics of the flight environment in below-canopy,the flight performance of UAV is constrained,which provides a reliable precondition for the path planning of UAV in below-canopy.By analyzing the characteristics and problems in below-canopy,the simplified environmental model is established and the corresponding objective function of path planning is proposed in this paper.The path length,path cost and running time of UAV path planning algorithm in below-canopy are analyzed experimentally.The results show that the global path length generated by AOQPIO algorithm is 4.3%shorter than that of other conventional algorithms,and the cost value is much smaller.2.A global path planning algorithm for UAV in below-canopy based on AOQPIO algorithm is proposed.The population initialization process of Quantum-behaved Pigeon-inspired Optimization(QPIO)is optimized by Logistic mapping and the map and compass factor R is modified to balance the global and local search ability.In the landmark operator,new population updating and pigeon position Xi updating strategies are given to improve the search diversity.With the application of AOQPIO to path planning for UAV in below-canopy,the quality and efficiency of global path planning is improved.3.An online path planning algorithm for UAV in below-canopy based on improved RRT*algorithm is proposed.In this paper,an improved RRT*algorithm with improved node sampling,selection,expansion and re-selection strategies is applied to UAV online path planning combining with the Receding Horizon Control(RHC)planning framework.After generating the path points,Minimum Snap Trajectory Generation(MSTG)algorithm is used to generate smooth flight trajectory,which improves the tracking performance.The online path length generated by improved RRT*algorithm is at least 7.9%shorter than that generated by other RRT related algorithms,and the robustness of the path is better.4.A bilayer path planning algorithm for UAV in below-canopy is proposed.The bilayer path planning algorithm for UAV in below-canopy is based on the above two different path planning algorithms.Firstly,AOQPIO algorithm is used for pre-planning,and an off-line global path point sequence is generated.Then,the improved RRT*algorithm is used to access each path point one by one.Through the simulation experiments of the bilayer path planning algorithm,it is proved that it can achieve a good planning effect in both static and dynamic environments in below-canopy.The bilayer path planning algorithm in below-canopy can combine the advantages of the two algorithms.Compared with the two single scene algorithms mentioned above,the algorithm has better results in static and dynamic environments.