Motion Planning and Compensation Approaches for Autonomous Surface Manipulator Systems in Grasping Tasks on Water Surfaces

Abstract

Autonomous surface manipulation systems (ASMSs) are novel robotics platforms composed of unmanned surface vehicles (USVs) and manipulators, and they can be used to recover floating objects on water surfaces. However, the improper positional relationship between the target object and the USV, and the dynamic coupling between the manipulator and the USV, may significantly reduce the grasping performance and stability of ASMS. To address this issue, this study proposes motion planning and compensation methods for ASMS. Firstly, an ASMS model considering the dynamic coupling between the USV and the manipulator is developed. Subsequently, a unified index for evaluating the positional relationship between the USV and target objects is introduced, and a modified visual servo control method is proposed to be used in manipulators with joint angles as motion commands. Motion compensation of the ASMS is achieved through a neural network model describing the motion coupling in ASMS. The hardware-in-the-loop simulation results illustrate that when the target object is located in regions with high values of the unified index, the grasping performance and stability of ASMS can be enhanced. After adopting motion compensation for ASMS, the average grasping success rate is improved from 66.63% to 75.11%, and the stability of ASMS is increased by 6.35%. Furthermore, the average grasping time is decreased by 0.65s, which is a 10.2% improvement in grasping efficiency.