Abstract
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.
Highlights
Automated driving algorithms and systems for work vehicles such as robotic lawn grass or grass 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
This section focuses on the experiments and evaluations of the random forest (RF) algorithm and shallow neural network (SNN) on real-world sensing data. 5.1 Experiments on RF algorithm 5.1.1 Measurement data
When the heights of lawn grasses and grass cutter are equal, a human operator determines whether the lawn grass is long or short according to the operating sound of the grass cutter
Summary
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. Robotics Software Design and Engineering 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. Precise control handling of work load in these work vehicles is critical for optimizing its energy management
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