Research On Indoor Uav Autonomous Flight Based On ROS System

The autonomous flight of Unmanned Aerial Vehicle（UAV）in a small indoor space has become a hot issue for many researchers.In addition to good control methods,UAV must have the ability of accurate positioning,reliable mapping and autonomous planning.In this paper,the autonomous indoor flight trajectory planning of UAV is studied by using the four-rotor UAV of Robot Operating System（ROS）.The UAV motion model shall be established,the data of the LIDAR and inertial measurement unit shall be fused,the attitude of the UAV shall be modified,and the autonomous positioning accuracy shall be improved.The dynamic path planning within the global scope shall be realized through the Hybrid Potential Based Probabilistic Roadmap（HPPRM）algorithm,and the indoor autonomous flight shall be completed.The main work of this paper is as follows:Firstly,the kinematics equations of UAV with four rotors are derived,the mathematical model of airborne lidar ranging is established,and the Monte Carlo localization algorithm is analyzed.Combined with PX4 flight control system and MAVROS communication protocol,a semi-physical simulation platform based on ROS for autonomous flight of indoor UAV is established.Secondly,according to the errors caused by the UAV tilt,the extended Kalman filter algorithm is used to fuse the radar and inertial measurement unit data to correct the positioning errors,and the fused data is used as the input of the Simultaneous Localization and Mapping（SLAM）algorithm to construct the global map,optimize the Monte Carlo algorithm,improve the positioning accuracy,and enable the UAV to achieve autonomous positioning in flight.Thirdly,the Artificial Potential Field（APF）method is optimized to solve the dynamic obstacle avoidance problem of UAV in three-dimensional space,and the Probabilistic Roadmap（PRM）algorithm is optimized to improve the planning speed and search for the optimal path.Combining the optimized APF and PRM algorithm,a HPPRM algorithm is designed to solve the global dynamic programming problem in UAV 3D space,and the complexity experiment is designed to verify the effectiveness of HPPRM algorithm.Finally,in the UAV simulation platform,three different paths are designed to verify the effectiveness of the optimized Monte Carlo algorithm,and indoor dynamic simulation environment is constructed to verify the global dynamic planning ability of HPPRM algorithm.Simulation results show that the optimized Monte Carlo positioning algorithm and HPPRM algorithm can accurately locate and plan the path of the UAV in an indoor environment.