基于UWB与惯导融合的室内导航系统研究

Abstract

With the continuous advancement of society, human demands for robots are not limited to traditional industrial robots, but to more intelligent robots. As a manifestation of intelligent robots, navigation systems still have problems of poor accuracy and adaptability. The intelligent robot mainly serves the indoor environment. Therefore, this article introduces the UWB indoor positioning system in the indoor navigation system to improve the accuracy and adaptability of the navigation system. The navigation system is divided into three parts: construction, positioning and path planning, so the main work of this paper is as follows:

Firstly, the principle of UWB positioning system is studied. For the problem that the system cannot determine the attitude of the robot, the traditional array positioning algorithm is introduced. The algorithm can solve the robot's attitude, but positioning error increases with increasing robot movement speed. So, Based on the array location algorithm, imu is introduced and an EKF-based UWB and imu fusion algorithm is proposed. The algorithm has high positioning accuracy and good adaptability, and does not significantly reduce with the increase of the robot speed.

Secondly, aiming at the problems existing in the existing SLAM methods such as poor adaptability and accuracy, this chapter first proposes a occupancy grid map accuracy evaluation method based on matching and realizes the evaluation of map accuracy. Then the performance of the three SLAM algorithms is tested in three different simulation environments, and the Gmapping algorithm with higher accuracy and better adaptability is selected. Finally, an improved Gmapping algorithm is proposed. The location error model obtained by the fusion of UWB and INS is used instead of the odometer error model in the original algorithm to sample. The cumulative error problem of Gmapping algorithm is solved, and the adaptability,real-time and accuracy of the algorithm are improved.

Then, based on AMCL algorithm's global positioning failure and kidnapping robot problems, an improved AMCL algorithm is proposed. This algorithm uses the error model of UWB and imu fusion as the particle sampling model, which solves the problem of global positioning failure and kidnapping of the original AMCL algorithm. Then, the algorithm of the path planning of the navigation system is studied. The cost map, global path planning and local motion planning algorithm are configured for the navigation system. The algorithm framework of the complete navigation system is implemented and the navigation is successfully completed in the simulation environment.

Finally, the actual experimental environment and platform are set up, and in

order to evaluate the accuracy of UWB and INS position fusion, a linear mapping-based visual positioning method is proposed to achieve accurate positioning of the robot's small-scale environment. The experimental results show that the EKF-based UWB and INS fusion algorithm has a higher positioning accuracy and adaptability. The improved Gmapping algorithm can effectively reduce the impact of cumulative error and improve the real-time performance of the algorithm. The improved AMCL algorithm can effectively solve the problem. Robot global positioning failure and kidnapping problems, the path planning algorithm configured for the navigation system can effectively achieve autonomous navigation of the robot, and the navigation system with improved AMCL algorithm has higher adaptability. Keywords: UWB, IMU, SLAM, AMCL, Navigation system