Position Estimation and Error Correction of Mobile Robots Based on UWB and Multisensors

摘要

Since there are many interferences in the indoor environment, it is difficult to achieve the precise positioning of the mobile robot using a single sensor. This paper presents a position estimation and positioning error correction method of mobile robots based on multisensor data. The robot’s positioning sensor includes ultra-wideband (UWB) components, inertial measurement unit (IMU), and encoders. UWB multipath interference causes more ranging errors, which can be reduced by the correction equation after data fitting. The real-time coordinates of the UWB robot tag can be calculated based on multiple UWB anchor data and the least squares method. The coordinate data <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mfenced open="(" close=")"> <a:mrow> <a:msub> <a:mrow> <a:mi>x</a:mi> </a:mrow> <a:mrow> <a:mi>c</a:mi> </a:mrow> </a:msub> <a:mo>,</a:mo> <a:msub> <a:mrow> <a:mi>y</a:mi> </a:mrow> <a:mrow> <a:mi>c</a:mi> </a:mrow> </a:msub> </a:mrow> </a:mfenced> </a:math> are acquired by UWB positioning subsystem, and the velocity data <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M2"> <e:mfenced open="(" close=")"> <e:mrow> <e:msub> <e:mrow> <e:mover accent="true"> <e:mi>x</e:mi> <e:mo>̇</e:mo> </e:mover> </e:mrow> <e:mrow> <e:mi>c</e:mi> </e:mrow> </e:msub> <e:mo>,</e:mo> <e:msub> <e:mrow> <e:mover accent="true"> <e:mi>y</e:mi> <e:mo>̇</e:mo> </e:mover> </e:mrow> <e:mrow> <e:mi>c</e:mi> </e:mrow> </e:msub> </e:mrow> </e:mfenced> </e:math> are collected by IMU together with encoders. The multisensor data continuously update Kalman filter and estimate robot position. In the positioning process, the positioning data of different sensors can be mutually corrected and supplemented. The results of UWB ranging correction experiments indicate that data fitting can improve the UWB positioning accuracy. In the multisensor positioning experiments, compared with a single sensor, the positioning method based on data fusion of UWB, IMU, and encoders has higher accuracy and adaptability. When UWB signals are interfered or invalid, other sensors can still work normally and complete the robot positioning process. The multisensor positioning method not only improves the robot positioning accuracy but also has stronger environmental adaptability.