Then, the working time can also be reduced. 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. Therefore, if the rotation speed of the motor for a lawn grass cutter is precisely controlled, battery wastage can be avoided. As a result, the motor for cutting lawn grasses operates at a constant rotation speed from start to finish. Current robo-mowers do not recognize the length of lawn grasses or ground conditions such as dirt, gravel, or concrete. Mobility control is the next research theme for optimizing the energy management of robo-mowers. In the following sections, because commercial robo-mowers are popular and readily available for experiments, a robo-mower is used as an example for optimizing energy management in work vehicles. Precise control handling of work load in these work vehicles is critical for optimizing its energy management. In this sense, its work and mobility share the energy, thus the two functions require effective energy management. In this case, the engine is also used to generate electric power, which is then used to charge the battery that powers the motor. The authors in attempted to optimize the skidding control of a snow blower, which has a motor for mobility and an engine for blowing snow. 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. 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. These vehicles are made possible by significant advances in sensor fusion technology, high-performance embedded systems, AI algorithms and advanced model-based design or development methods. Recently, automated driving algorithms and systems for work vehicles such as robotic lawn grass or grass mowers (robo-mowers), autonomous snow blowers, automatic guided vehicles (AGVs), autonomous delivery vehicles, and autonomous mobile robots (AMRs), have attracted much attention. Presently, the proposed estimation system is being developed by integrating two motor control systems into a robotic lawn mower, one for lawn grass cutting and the other for the robot’s mobility. Furthermore, the accuracy of the SNN is 94.0% in experiments where sensor data are continuously obtained while the robotic lawn mower is operating. The RF algorithm evaluated on data from the fusion of sensors achieved 92.3% correct estimation ratio in several experiments on real-world lawn grass areas, while the SNN achieved 95.0%. To this end, two AI algorithms, namely, random forest (RF) and shallow neural network (SNN), are developed and evaluated on observation data obtained by a fusion of ten types of sensor data. Based on these requirements, this chapter is focused on developing an estimation system for estimating lawn grass lengths or ground conditions in a robotic lawn mower. At the same time, it is important to conserve the battery that is used for both work execution and mobility. In terms of efficiency, the traveling speed of a lawn mower, for example, should be reduced when the workload is high, and vice versa. These two functions, work sensing and mobililty control, have a close correlation. Particularly, this chapter focuses on autonomous work sensing and mobility control of a commercial electric robotic lawn mower, and proposes an AI-based approach for work vehicles such as a robotic lawn mower. This type of autonomous driving consists of work sensing and mobility control. This chapter describes a part of autonomous driving of work vehicles.
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