🤖 AI Summary
This work proposes QuadBoat, a bio-inspired quadrupedal unmanned surface vessel designed to overcome the limitations of existing water rescue robots in rapidly and accurately retrieving drowning victims. By introducing a quadrupedal configuration to surface rescue for the first time, QuadBoat achieves high adaptability and agile maneuverability through active posture regulation. The system integrates inverse kinematics-based motion control, a cascaded MPC-PID controller, and a vision-based tracking module to enable robust target detection, pursuit, and retrieval. Experimental results demonstrate that QuadBoat attains exceptional trajectory tracking accuracy, superior agility, and effective drowning victim recovery capabilities in both indoor and outdoor environments, significantly enhancing the efficiency and flexibility of aquatic rescue operations.
📝 Abstract
Prompt extraction of victims from water is crucial in water surface rescue missions. However, previous research on rescue robots has seldom addressed this issue. This paper presents QuadBoat, a bio-inspired unmanned surface vehicle (USV) designed to track and retrieve victims from water. QuadBoat features a quadrupedal robot configuration, enabling it with highly adaptable and agile maneuverability through its actively adjustable posture. Employing an inverse kinematics-based controller and cascaded model predictive control (MPC)-PID controller for overall movement, QuadBoat can accurately track and retrieve objects on the water surface. Maneuverability demonstrations validate QuadBoat's high agility, while a series of tracking experiments, including leg action tracking and trajectory tracking, confirm its high motion accuracy and system mobility. Finally, visual-based tracking and object pickup experiments further verify QuadBoat's target tracking capabilities and its effectiveness in executing rescues, both indoors and outdoors.