Development of a Personal Mobility System with Autonomous Driving for Agricultural Work by the Physically Challenged and the Vulnerable

EunByul Ko, KwangHo Han and Chul-Hee Lee
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EunByul Ko: Department of Mechanical Engineering, Inha University, 100 Inharo, Michuholgu, Incheon 22212, Korea
KwangHo Han: Department of Construction Machinery Engineering, Inha University, 100 Inharo, Michuholgu, Incheon 22212, Korea
Chul-Hee Lee: Department of Mechanical Engineering, Inha University, 100 Inharo, Michuholgu, Incheon 22212, Korea

Agriculture, 2022, vol. 12, issue 7, 1-14

Abstract: Many issues have recently arisen as a result of the aging population and dwindling labor force. To solve these problems, including reduced convenience and productivity, research on the mobility of agricultural machinery and logistics involving autonomous driving is being conducted. However, research on improving individual mobility is scarce. Therefore, an autonomous driving personal mobility system is developed in this study for the physically challenged and other vulnerable groups with mobility difficulties. The personal mobility system can help the physically challenged and the vulnerable to overcome their limited mobility and perform agricultural work. An additional feature is a standing function that allows the performance of tasks too difficult to do while seated. In addition, the autonomous driving function guides the personal mobility system along the work route and stops near agricultural crops. In this study, the design and structural stability of the personal mobility system were analyzed through CAD modeling and structural analysis, with LiDAR applied for autonomous driving and machine learning for agricultural work. This personal mobility system is expected to improve mobility for the physically challenged and the vulnerable, allowing them to play a role and participate more fully in society.

Keywords: agricultural work; autonomous driving; LiDAR; machine learning; personal mobility (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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