This chapter describes a part of autonomous driving of work vehicles. This type of autonomous driving consists of work sensing and mobility control. 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. These two functions, work sensing and mobililty control, have a close correlation. In terms of efficiency, the traveling speed of a lawn mower, for example, should be reduced when the workload is high, and vice versa. At the same time, it is important to conserve the battery that is used for both work execution and mobility. 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. 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. 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%. Furthermore, the accuracy of the SNN is 94.0% in experiments where sensor data are continuously obtained while the robotic lawn mower is operating. 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.
With the acceleration of urbanization, the amount of solid waste in rural areas continues to increase, bringing serious pollution problems to the environment. Therefore, the resource utilization of rural solid waste has become one of the important tasks of current environmental protection work. This article analyzes the current situation of rural solid waste resource utilization and proposes a path to achieve it, including government guidance, technological innovation, and social participation. At the same time, this article also discusses the difficulties and strategies for achieving the resource utilization of rural solid waste, providing a reference for the realization of rural solid waste resource utilization.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.