**4. Application experiments**

There are some application experiments to show the performance and application prospect of the USV system, such as rescuing rope throwing, rescuing of people, air–surface robots' cooperation, environment data collection, and water sampling. In this section, two typical experiments, that is, rescuing experiment and water sampling experiment, are introduced in detail.

### **4.1. Rescuing experiment**

yaw dynamics possess stronger nonlinearity since the sway and yaw speed are usually smaller

(a) Surge dynamics 4.6% 4.6% 2.3% (b) Sway dynamics 19.7% 17.9% 16.4% (c) Yaw dynamics 13.7% 13.4% 13.5%

To further reduce the model mismatch's influence and improve the estimation accuracy, the active modeling scheme is used. Using an UKF algorithm, the model error of the qLPV

( ) ( ) <sup>2</sup>

and we use the same data as that of the full state model identification experiment, that is, the throttle was set to 30%, and the rudder angle follows a sine wave with amplitude π/3. The prediction error computed by using Eq. (12) is shown in **Table 4**. Compared to the results from the qLPV model without active modeling in **Table 3**, the model accuracy improvement is significant. The active modeling enhanced qLPV model significantly reduces the prediction errors (only one-third of that for the qLPV model). This is intended to make the USV autono‐ mously adaptive to its internal and external uncertainties, that is, to achieve a robust tracking

There are some application experiments to show the performance and application prospect of the USV system, such as rescuing rope throwing, rescuing of people, air–surface robots' cooperation, environment data collection, and water sampling. In this section, two typical

<sup>3</sup> 0 ,0 ,

1 0,0,0,16,2,1 =1 16,2,1

*Q diag R diag*

performance for time-varying unknown disturbances in the vehicle dynamics.

**Surge Sway Yaw** 0.8% 4.8% 4.5%


**Linearized Nomoto with sideslip qLPV**

(13)

and easily influenced by external disturbances compared to surge dynamics.

**Table 3.** Modeling error for sine input.

40 Recent Advances in Robotic Systems

structured model is estimated online.

a

The parameters of the UKF algorithm are


3


**Table 4.** Modeling error for active modeling enhanced qLPV model.

**4. Application experiments**

=´ =

1 10 2

 b

, ,

The rescuing throwing experiment is aiming at searching and rescuing the trapped people (**Figure 9**). When trapped people were detected, the USV's direction would be adjusted by the ground station sub-system to launch a lifebuoy by using an automatic pneumatic. After the lifebuoy was exposed to water, it would be inflated automatically in 5 s.

The launch power is from the high-pressure (30 MPa) gas in pneumatic cylinders. The trigger servo (**Figure 10**) of the pneumatic is controlled by the PWM wave from the on-board control computer or the remote controller. The farthest distance of dumping is 150 m. Using the kayak carried by the USV, we can achieve trapped people dragging. After the trapped people reached the kayak, the ground station sub-system would send commands to USV to drag the kayak to the safe place automatically.

**Figure 10.** Automatic pneumatic.

**Figure 9.** Rescuing experiment. (a) Rescuing rope throwing and (b) rescuing of people.

### **4.2. Water sampling experiment**

The sampling equipment is composed of four sampling bottles, a water pump and some assist mechanical devices (**Figure 11**).

**Figure 11.** Water sampling equipment.

**Figure 12.** The minimum turning circle (radius 1.375 m).

Design, Implementation and Modeling of Flooding Disaster-Oriented USV http://dx.doi.org/10.5772/64305 43

**4.2. Water sampling experiment**

42 Recent Advances in Robotic Systems

mechanical devices (**Figure 11**).

**Figure 11.** Water sampling equipment.

**Figure 12.** The minimum turning circle (radius 1.375 m).

The sampling equipment is composed of four sampling bottles, a water pump and some assist

**Figure 13.** Four water sampling points and distance errors around the sampling points when water sampling.

First, the USV carried two sets of sampling equipment into a designed area (a 2.5 m radius circle around an GPS point). While the USV arriving at the interested point, it started to circle around the interested point at the minimum turning radius by using an GPS point tracking control algorithm (**Figure 12**). Then, the water pump protruded from the equipment and started drawing water into the bottle (each point for one bottle). After the work was done, the USV would return back automatically by using a course keeping control algorithm.

**Figure 13** shows the trajectory of USV and the GPS points tracking error at four sampling points when water sampling. In the left figure, the green line represents the trajectory of USV, and the red circles are the 2.5 m radius circles around the designed sampling points. From **Figure 13**, we can see that the maximum tracking error 1.2 m, far <2.5 m.
