Study Of Inspection Robot And Its Indoor Positioning Based On SINS Rotation Vector Three Sub-Sample Positioning Algorithm

Autonomous navigation is the key to inspection robots performing daily inspection tasks in harsh environments such as nuclear power stations.The core difficulty of autonomous navigation lies in precise positioning.Taking into account unpredictable nature of the indoor environment and the applicability of other indoor positioning methods,the Kalman filtering algorithm is used to fuse the three sub-sample algorithm based on the Strapdown inertial navigation system(sins)and the track estimation method in this thesis,which can make the inspection robot achieve high precision real-time Positioning location.The main work and innovations of this thesis are as follows:1.Rotation vector three sub-sample method based on strapdown inertial navigation is designed to obtain the robot's high-precision attitude,velocity,and position information.In the rotation vector three sub-sample algorithm,the initial alignment is performed first to obtain the system's precise initial attitude;Then the rotational vector differential equation Taylor expansion,the parabola fitting method is used to get the angular velocity and angle increment,and the attitude updating matrix of the robot is obtained according to the instruction angular velocity and the gyroscope data;Again,diagonal increments and velocity increments are sampled in any one cycle.Velocity differential equations are integrated and expanded,parabolic fitting is used to obtain specific force information,and the current time speed information is obtained from acceleration data,angle increments,and velocity increments.Finally,the position information at the current time is obtained by integrating the velocity information.2.The track estimation system based on encoder ranging is studied.In the path-tracking algorithm,the speed of the current moment is first measured by an encoder,and the position information of the current moment is obtained according to the speed.3.Kalman filter algorithm fusion is studied based on strapdown inertial navigation algorithm.The rotation vector three sub-sample algorithm and flight path estimation algorithm are used to obtain high-precision robot positioning information.First,the Strapdown inertial error model and the track estimation error model are established;Second,the error model of the SINS-based rotation vector three sub-sample algorithm and the error model of the track-reporting system are indirectly fused with Kalman filtering,and the observed value is the difference between the two speeds;Again,using the Kalman filter algorithm to obtain the optimal error estimate to correct the positioning information of the system,in order to achieve a more accurate positioning;Finally,in order to improve the accuracy of the initial attitude,the Kalman filtering precision alignment method based on the measured values error of the east and north directions of the specific force is designed,so that the initial posture information of the robot positioning system is more accurate.4.The hardware design of real-time location for inspection robot is studied and the robot positioning experiment platform based on STM32 microcontroller is built.Kalman filter algorithm is used to realize the fusion of three sub-sample of rotation vector based on strapdown inertial navigation and dead reckoning.Two sets of real-time positioning experiments are performed on the inspection robot,and the relative errors and absolute errors of the positioning w quantitatively analyzed.The experimental results show that the maximum positioning error is 2.59% when the robot motion trajectory is rectangular,and the maximum positioning error is 4.66% when the robot motion trajectory is circular.