A Low-cost Autonomous Lawn Mower with AI-Based Obstacle Avoidance and GPS Guidance System

Thanapon Kosri; Tossawat Seekhamharn; Phasawut Phoonsrichaiyasit; Poowadon Khungpo; Phaophak Sirisuk; Theerayod Wiangtong

doi:10.55003/ETH.420305

Authors

Thanapon Kosri School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand
Tossawat Seekhamharn School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand
Phasawut Phoonsrichaiyasit School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand
Poowadon Khungpo School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand
Phaophak Sirisuk School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand
Theerayod Wiangtong School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand

DOI:

https://doi.org/10.55003/ETH.420305

Keywords:

Autonomous, Lawn mower, Image processing, YOLO, GPS navigation, Low-cost

Abstract

This paper presents a cost-effective robotic system capable of manual control via RF remote and autonomous navigation using GPS-based information. The system employs artificial intelligence to dynamically classify and avoid non-grass obstacles, ensuring safe operation in real environments. The prototype integrates affordable hardware including Arduino board, sensors, actuators and Raspberry Pi with lightweight algorithms to balance performance and cost. Experimental validation confirms its ability to follow predefined paths with ±1.5 meters deviation in open area and 90% obstacle avoidance success rate. With a total hardware cost under $200, this prototype highlights feasibility for larger-scale implementation.

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