**1. Introduction**

Recently, automated driving algorithms and systems for work vehicles such as robotic lawn grass or grass mowers (robo-mowers) [1–3], autonomous snow blowers [4], automatic guided vehicles (AGVs) [5], autonomous delivery vehicles [6], and autonomous mobile robots (AMRs) [7], have attracted much attention. These vehicles are made possible by significant advances in sensor fusion technology, high-performance embedded systems, AI algorithms and advanced modelbased design or development methods. Particularly, Industry 4.0 or Society 5.0 needs the digital transformation or smart factory in industry and, now, AGVs and AMRs perform some tasks that are essential for constructing the automatic

production lines. As these vehicles share a battery for their work and mobility, the interaction between their functions should be effectively controlled to reduce battery charging frequency and time, as well as working time. The authors in [4] attempted to optimize the skidding control of a snow blower, which has a motor for mobility and an engine for blowing snow. In this case, the engine is also used to generate electric power, which is then used to charge the battery that powers the motor. In this sense, its work and mobility share the energy, thus the two functions require effective energy management. Precise control handling of work load in these work vehicles is critical for optimizing its energy management.

several experiments on actual lawn grass areas. In addition, the application of SNN attained 95.0%. Moreover, the accuracy of SNN is 94.0% in such trials in which sensing data are continuously obtained while the robo-mower is in operation. The proposed estimation system is being developed by integrating it with two types of motor control systems for grass cutting and robot mobility, respectively. At present, the authors are promoting the research and development of robo-mowers for com-

The outline of this chapter is as follows. Section 2 describes the Hybrid Twin ™,

The Hybrid Twin ™ approach [14] is efficient for real-time object control. The proposed estimation method is useful for controlling the operation of lawn grass cutter motor and a mobility motor. When the robot is operating in an area with long lawn grasses, the motor should be set to the maximum rotation speed. On the other hand, the rotation speed should be reduced or stopped when the robo-mower is operating in an area with short lawn grasses or in an area without any lawn grasses, respectively. As a result, battery consumption will decrease. Furthermore, if it is possible to control the robo-mower's speed so that it decreases or increases according to the length of lawn grasses, the working time will be greatly reduced. When a ground without lawn grasses is identified, laying the electric cable that defines the boundary of the area is no more necessary and, as a result, the required

Digital Twin has become popular for implementing smart factory, and it has been

The Digital Twin only obtains data from a fusion of sensors, and measurement data with some abstractions are transferred to the Virtual Twin. The Virtual Twin is a precise co-simulator consisting of subsystems obtained using a model-based design method. This simulator must be sufficiently fast, and it is usually a 1-D simulator, which is a high-speed version of the original 3-D simulator is used. The Hybrid is a combination of the Virtual Twin and Digital Twin, and the optimized state of the real system on time ð Þ *t* þ Δ*t* must be fedback to the real system from the state on time ð Þ*t* . This loop is iterated over with the time interval ð Þ Δ*t* . As a result, the state of the real system is optimized in real time. Measurement data are extracted from the fusion of sensors for robo-mower operations, and noise reduction is applied to the obtained parameters in the Digital Twin. This means that the Digital Twin is an accurate numerical model of real objects. The Virtual Twin receives the obtained data s(t) at

used [15] for controlling mission-critical systems, such as nuclear plant, airplane control, or rocket control in the aerospace industry. The Digital Twin constructed in the virtual space means a twin of a real space object. The twin is a precise model, and its behaviors are reproduced in the virtual space. The Hybrid Twin ™ is an extension of Digital Twin. As the target system has become large and complicated, the Virtual

Twin has been separated from the Digital Twin, as shown in **Figure 1**.

which is the basic idea for controlling the robo-mower in real time. Section 3 describes robo-mower used in this chapter; however, the discussions are not limited to this robo-mower. Moreover, the estimation problem of lawn grass lengths is also defined in this section. Section 4 describes the proposed RF and SNN algorithms. Section 5 describes the experimental results based on the big data obtained from sensor fusion and a set of features for classifying the sensor data are. Furthermore, the set of necessary sensors and performance evaluations of the proposed algorithms are stated. Section 6 describes the evaluation of the proposed SNN algorithm when applied to the consecutive sensor data obtained in real-world use. Finally, the

mercial use by collaborating with an automobile company.

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

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

chapter is summarized in Section 7.

maintenance is reduced.

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**2. Hybrid twin within work vehicle**

In the following sections, because commercial robo-mowers [8] are popular and readily available for experiments, a robo-mower is used as an example for optimizing energy management in work vehicles. Mobility control is the next research theme for optimizing the energy management of robo-mowers. Current robomowers do not recognize the length of lawn grasses or ground conditions such as dirt, gravel, or concrete. As a result, the motor for cutting lawn grasses operates at a constant rotation speed from start to finish. Therefore, if the rotation speed of the motor for a lawn grass cutter is precisely controlled, battery wastage can be avoided. Moreover, because the control of grass cutting and mobility is correlated, the mobility speed should be controlled according to the lawn grass lengths and ground conditions. Then, the working time can also be reduced. Therefore, the precise estimation of lawn grass lengths using effective sensor data is required in the first stage, i.e., preventing battery wastage. Then, in the second step, the mobility of robo-mowers is controlled according to the estimation results from the first stage. Finally, a cooperative control of a group of robo-mowers is researched [3] and implemented. In particular, the group control of robo-mowers becomes meaningful when the performance of each robo-mower is optimized.

In this study, an AI-based approach is adopted for the estimation of lawn grass lengths from the fusion of sensor data. A random forest (RF) algorithm and shallow neural network (SNN) are suggested. Ten measurement data types are obtained from sensors attached to a robo-mower. The combination of sensor data types is essential for lawn grass estimation, that is, a sensor fusion problem is discussed. In general, the sensor fusion and use of big data have attracted many researchers' interest. Recently, there have been detailed surveys on the combination of sensor fusion and big data analysis [9, 10]. Some applications to actual problems have also been reported [11–13]. The popular approach for big data analysis is the use of machine learning. Takami G., et al. [11] studied the observation of plant status. They used three kinds of sensors and a deep learning (DL) algorithm for big data analysis. The details of the DL are not described, and the processing time of the observation system is not known; however, it may be useful to learn that they predicted the deterioration of sensors performance through their combination. Alonso S., et al. [12] also adopted the same approach for observing a screw compressor in a chiller. They used five kinds of sensor data and a 1D convolutional neural network (CNN) for their analysis. The adoption of 1D CNN makes monitoring faster and real-time processing is realized. Their approach is probably suitable for data without any estimated features; however, in this study some features may be efficient for the estimation task in advance. Li C., et al. [13] performed the diagnosis of rotating machinery. They used vibration sensor signals, and the Gaussian-Bernoulli deep Boltzmann machine was used for their analysis. The accuracy of fault estimation was evaluated; however, its real-time processing requirement was not mentioned. Therefore, this approach cannot be applied to the problem dealt with in the following.

In the experiments of the proposed AI-based approach, the application of RF algorithm to the fusion of seven sensors attained a 92.3% correct estimation ratio in

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

several experiments on actual lawn grass areas. In addition, the application of SNN attained 95.0%. Moreover, the accuracy of SNN is 94.0% in such trials in which sensing data are continuously obtained while the robo-mower is in operation. The proposed estimation system is being developed by integrating it with two types of motor control systems for grass cutting and robot mobility, respectively. At present, the authors are promoting the research and development of robo-mowers for commercial use by collaborating with an automobile company.

The outline of this chapter is as follows. Section 2 describes the Hybrid Twin ™, which is the basic idea for controlling the robo-mower in real time. Section 3 describes robo-mower used in this chapter; however, the discussions are not limited to this robo-mower. Moreover, the estimation problem of lawn grass lengths is also defined in this section. Section 4 describes the proposed RF and SNN algorithms. Section 5 describes the experimental results based on the big data obtained from sensor fusion and a set of features for classifying the sensor data are. Furthermore, the set of necessary sensors and performance evaluations of the proposed algorithms are stated. Section 6 describes the evaluation of the proposed SNN algorithm when applied to the consecutive sensor data obtained in real-world use. Finally, the chapter is summarized in Section 7.
