Improved Magnetic Guidance Approach for Automated Guided Vehicles by Error Analysis and Prior Knowledge

Navigation accuracy and robustness are key performance indexes of automated guided vehicles (AGVs). In our previous study, the magnetic guidance approach based on magnetic dipole model and non-linear optimization algorithm was proposed, which has high positioning accuracy and could estimate the yaw angle of AGV directly. However, the localization accuracy of the magnetic guidance approach will deteriorate if the magnetic nails (MNs) buried in the ground have installation errors or the magnetic moments between the MNs are inconsistent. To overcome this problem, we propose an improved method based on error analysis and prior knowledge for the magnetic guidance approach. Firstly, the factors that affect the localization accuracy are analyzed, and the parameters ( $B_{\mathrm {T}}$ , $p$ , $c$ ), whose errors will deteriorate the localization accuracy, are combined with MN pose ( $a$ , $b$ , $m$ , $n$ ) as optimization variables. Then, the prior knowledge regarding ( $B_{\mathbf {T}}$ , $p$ , $c$ ) is employed to construct the constraint conditions for the magnetic guidance approach. Finally, the global convergence probability and convergence speed of the improved magnetic guidance approach are analyzed. Experimental results demonstrate the adaptability and robustness of the improved magnetic tracking approach, which diminishes the impact of MN installation errors and magnetic moment deviation. The parking accuracy of AGV is improved to 1.42±0.85 mm and 1.10±0.38°.