*5.2.1 Available sensors*

The built-in sensors attached to the robo-mower are listed in **Table 1**. Six builtin sensors are available for measuring the corresponding parameters shown in **Table 1**. The objectives of the experiments are first to evaluate the accuracy of lawn grass height estimation and ground condition estimation and, second, to compare the SNN's results with those of the RF algorithm.

#### *5.2.2 Measurement data*

The data measured by sensors are collected while driving the robo-mower on a field with long and short lawn grasses as well as without lawn grasses. The three types of lawn grasses are shown in **Figure 9(a)**–**(c)** are used for the experiments. In each of these cases, the long lawn grass case and short lawn grass case are performed by adjusting the lawn grass cutting blade height from the ground. Similarly, several ground conditions without lawn grasses are adopted as shown in **Figure 9(d)**–**(f)**, which are asphalt, gravel, and stone pavement, respectively.

The collected data are categorized into three groups. The first group is for long lawn grasses, that is, the height of lawn grass is larger than the specified one. The second group is for the short lawn grasses, that is, the lawn grass is shorter than or equal to the specified one. The third group is the field without lawn grasses, that is, dirt, gravel, stone pavement, tiled, asphalt, or concrete fields. The measurement data are collected for a total driving time of 2.3 h for each group with various fields.

*5.2.3 Fusion of sensors for SNN*

cutting motor.

*5.2.4 Features for classifying data*

SNN2.

**Figure 10.**

**135**

*Fusion of sensors for SNN.*

differ with these two types of estimations.

*DOI: http://dx.doi.org/10.5772/intechopen.97530*

The fusions of sensors for two SNNs SNN1 and SNN2 are experimentally determined. Ten types of sensors, as shown in **Table 1**. As **Figure 10** shows, SNN1 and SNN2 are cascade connected, where SNN1 estimates if a target area is with or without lawn grasses, and SNN2 provides the result that an area is with long/short lawn grasses. The basic idea behind this configuration is that sensor fusion may

*AI-Based Approach for Lawn Length Estimation in Robotic Lawn Mowers*

For SNN1, the Horizontal/Vertical Acceleration sensor seems adequate, and the combinations of *x*, *y,* and *z*-axis sensor data are experimented with and evaluated on some datasets. The obtained accuracy is shown in **Figure 10**. For the estimation of lawn lengths, sensor fusion indicating the load of the lawn grass cutter seems effective. Therefore, the measurement data obtained from the combinations of the battery sensor and duty ratio given to the cutting motor are evaluated with some datasets. As shown in **Figure 10**, the multiplication of battery voltage and the duty ratio for the cutting motor achieves maximum accuracy. The differences are minor; however, this multiplication is probably the reason for showing the load of grass

As a result, the *x*-axis and *z*-axis values obtained from the Horizontal/Vertical Acceleration sensor are used as inputs for SNN1. Similarly, the multiplication of battery voltage and the duty ratio for the grass cutting motor, respectively, obtained from Battery Voltage and Rotation of Grass Cutting Motor are used as inputs for

The input data features are (i) maximum value, (ii) minimum value, (iii) average value, (iv) median value, (v) standard deviation value, (vi) peakedness value, and (vii) skewness value. In signal recognition based on machine learning, some

**Figure 9.** *Variations of target areas.*

## *AI-Based Approach for Lawn Length Estimation in Robotic Lawn Mowers DOI: http://dx.doi.org/10.5772/intechopen.97530*
