Autonomous Target Docking of Nonholonomic Mobile Robots Using Relative Pose Measurements

This article investigates the target docking problem for nonholonomic mobile robots using relative position and orientation measurements of the target. Considering the fact that target docking operations generally happen in unstructured environments where obstacles are left lying around, the whole docking process is divided into two phases, namely the approaching phase and the autonomous docking phase. In the approaching phase, the robot navigates in the operational environment until it reaches a switching region around the target, which triggers the autonomous docking phase. To account for the noise-corrupted and intermittent target observations, an extended Kalman filter-based relative pose estimation algorithm is designed to estimate the position and orientation of the target in the local reference frame of the robot. To achieve a perfect docking operation in the autonomous docking phase, which requires a zero-impact angle and a zero-lateral offset at the final engagement stage, a practical solution is proposed where motion planning and tracking control of the robot are incorporated into a unified scheme. Unlike most of the existing docking approaches in the literature which only account for static targets, our proposed strategy addresses the moving target docking problem as well. Real robot experiments have been performed to demonstrate the effectiveness of the proposed method.

Automated summary

This article investigates the target docking problem for nonholonomic mobile robots using relative position and orientation measurements of the target. It proposes an extended Kalman filter-based relative pose estimation algorithm and a practical solution where motion planning and tracking control of the robot are incorporated into a unified scheme to achieve perfect docking operation. Real robot experiments have been performed to demonstrate the effectiveness of the proposed method.