**1. Introduction**

Floods are among the most major climate-related disasters and have resulted in substantial losses including enormous property damage and human casualties [1]. Numbers of casualties and losses could be larger in the future in response to global warming. The biggest challenge lying in rescuing operations is the low efficiency and the high risk of the rescuers, which is also aproblemfocusedonbythiswork.Unmannedsystemisoneofthe solutions thatreplacepeople in the rescuing operations.

Unmanned surface vehicles (USVs), also called autonomous surface vehicles (ASVs), are often used to name the vehicles, which can run on the surface autonomously. Surface robot-assisted flood disaster rescue and inspection is a new research direction in the field of robotics. Here are some obvious advantages: (1) the smaller size allows the USVs to access to narrow and small space to get detailed information; (2) remote operation can avoid casualties of the rescuers caused by the unexpected potential dangers. After Hurricane Wilma in 2005, USVs have been used for emergency response by detecting damage to seawalls and piers, locating submerged debris, and determining safe lanes for sea navigation [2]. After the Fukushima nuclear accident in 2011, the United States and Japan have used robots to assess the damage jointly [3].

Also, in 2007, a new Trimaran unmanned surface vehicle (TUSV) as a test-bed to verify the robust motion control strategies has been designed in Shenyang Institute of Automation, Chinese Academy of Sciences (SIA, CAS) [4]. After that, in 2012, a water-jet propulsion USV equipped with different kind of sensors and ground control system has been designed and implemented in SIA to improve the performance of USV [5]. In 2015, to increase the reliability and real time of USV, the software architecture has been designed based on a real-time operating system QNX 6.5.0. Also, the selection of an appropriate platform and associated hardware as well as useful and sufficient sensors, and integrating these two entities has been taken into consideration. Modular design is adopted in the hardware and software structures to improve system scalability. The hardware structure comprises six sub-systems, including the on-board control computer sub-system, power sub-system, communication sub-system, sensor and perception sub-system, ground station sub-system, and execution sub-system. The software structure comprises six modules, the communicator module, GPS-IMU module, protocol module, tracker module, controller module, execute module, and engine module.

In general, the surface environment of flooding disasters, including fixed obstacles, floating obstacles, narrow canals, and the wind/wave/current disturbances, makes the target difficult to be inspected by an USV, because it presents a great limitation in trajectory tracking in complicated surroundings. The primary reason is the difficulty of obtaining accurate and applicable dynamical models. The hydrodynamic mechanism is very complex, and the dynamical model parameters change with Froude number *Fr* =*U* / *Lg*, where *U* is the operating speed of USV, *L* is the overall length of USV (the submerged length of USV), and *g* is the acceleration of gravity [6]. When the Froude number is <0.5, the main fluid forces exerted on USV are the hydrostatic pressure by replacing water with respect to hydrodynamic pressure, called *displacement area*; when the Froude number is >0.5 but <1, the main fluid forces exerted on USV are hydrostatic and hydrodynamic pressure, called *semi-displacement area*; when the Froude number is >1, the main forces exerted on USV are hydrodynamic pressure, called *planning area*. Since the model structure and parameters will change greatly from *displacement area* to *planning area*, there is no unified dynamics model of a surface robot.

To approximate the hydrodynamics, we propose an active quasi-linear parameter varying (qLPV) model to approach the dynamics of the USV system. The LPV model concerns linear models whose state-space representations depend on state independent parameters [7]. The qLPV model is obtained by making the varying parameter of the LPV system a function of the state [8]. To accommodate the unstructured model error, the model error is introduced into the qLPV structured model as a complementation, and with the active modeling technique, the model error online estimation is used to improve the modeling accuracy. There are many available algorithms for active model online estimation such as the extended Kalman filter (EKF) [9] and epsilon-support vector regression (ε-SVR) [10]. In this chapter, the Unscented Kalman Filter (UKF) is utilized to obtain the unstructured model error.

Finally, to show the performance of the USV systems and the modeling methods, extensive experiments have been done including rescuing rope throwing by using an automatic pneumatic, rescuing of people, air–surface robots' cooperation, environment data collection, model parameters identification, communication distance testing, and water sampling in a 2.5 m radius circle.
