A multi-stage localization framework for accurate and precise docking of autonomous mobile robots (AMRs)

Abstract

Autonomous navigation has been a long-standing research topic, and researchers have worked on many challenging problems in indoor and outdoor environments. One application area of navigation solutions is material handling in industrial environments. With Industry 4.0, the simple problem in traditional factories has evolved into the use of autonomous mobile robots within flexible production islands in a self-decision-making structure. Two main stages of such a navigation system are safe transportation of the vehicle from one point to another and reaching destinations at industrial standards. The main concern in the former is roughly determining the vehicle’s pose to follow the route, while the latter aims to reach the target with high accuracy and precision. Often, it may not be possible or require extra effort to satisfy requirements with a single localization method. Therefore, a multi-stage localization approach is proposed in this study. Particle filter-based large-scale localization approaches are utilized during the vehicle’s movement from one point to another, while scan-matching-based methods are used in the docking stage. The localization system enables the appropriate approach based on the vehicle’s status and task through a decision-making mechanism. The decision-making mechanism uses a similarity metric obtained through the correntropy criterion to decide when and how to switch from large-scale localization to precise localization. The feasibility and performance of the developed method are corroborated through field tests. These evaluations demonstrate that the proposed method accomplishes tasks with sub-centimeter and sub-degree accuracy and precision without affecting the operation of the navigation algorithms in real time.

An Accurate and Rapid Docking Algorithm for Four-Way Shuttle in High-Density 3D Warehousing Environment

“…Despite its benefits, EKF can perform poorly with non-Gaussian noise or highly nonlinear dynamics and requires accurate initial state estimates. Yilmaz [22] applied a multi-stage localization method and a scan-matching-based method in the docking stage. Yu [23] addressed the nonlinear and time-varying uncertainties of motors by proposing an improved PID control method based on BP neural networks combined with an inertia factor.…”