International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 09 | Sep 2025
p-ISSN: 2395-0072
www.irjet.net
AI-Powered Robotic Lawn Mower with Autonomous Navigation and Control Using Raspberry Pi Prakash Kanade1,Divya Kumar2, Shama Ghatwal3, Sunay Kanade4, Tarunesh Sathish5 1Researcher in Robotics, Artificial Intelligence, IOT 2Trainer, LeenaBOT Robotics, Bangalore India 3Student, LeenaBOT Robotics, Goa India
4Student, LeenaBOT Robotics, Bangalore India
5Student, LeenaBOT Robotics, USA ---------------------------------------------------------------------***--------------------------------------------------------------------1. INTRODUCTION Abstract - This study presents the design, development, and
evaluation of an AI-powered GPS-based robotic lawn mower featuring autonomous navigation and control using a Raspberry Pi platform. The system integrates RTK-GNSS for centimeter-level localization, AI-driven path planning (A*, Theta*, DWB/TEB), and multi-sensor obstacle detection using LiDAR, ultrasonic sensors, and computer vision. Field experiments were conducted on outdoor lawns (12–20 m × 8– 15 m) with slopes up to 5°, encompassing open grass, moderate, and dense obstacle conditions. Performance metrics included navigation accuracy, coverage efficiency, overlap percentage, obstacle detection precision and recall, obstacle handling latency, path tracking error, throughput, energy consumption, and blade RPM stability. Results demonstrated superior performance, with navigation accuracy of 0.025 m, coverage efficiency of 98.6%, and overlap of only 6.4%, exceeding target benchmarks. Obstacle detection precision (96.8%) and recall (94.5%) ensured reliable identification of static and dynamic hazards, while handling latency averaged 225 ms, enabling timely collision avoidance. Path tracking error remained at 0.042 m, supporting stable operation, and throughput reached 8.3 m²/min. Energy consumption was low at 3.8 Wh/m², with a battery runtime of 162 minutes, surpassing the 150-minute target. Blade RPM stability of 1.8% maintained consistent cutting performance. Comparative analysis showed that the proposed system outperformed commercial robotic mowers and manual push mowers in accuracy, efficiency, safety, and sustainability. The integration of AI algorithms, RTK-GPS localization, and sensor fusion significantly improved operational adaptability, efficiency, and safety. The mower’s modular architecture, electric operation, and optimized coverage contribute to reduced environmental impact and labor requirements. These findings demonstrate the potential of low-cost, AI-enabled autonomous systems to transform residential and commercial lawn maintenance. Future work may explore scalability, predictive maintenance, and grass health monitoring to expand functionality and market applicability.
The rapid advancement of artificial intelligence (AI), robotics, and embedded systems has revolutionized automation across various sectors, from manufacturing to agriculture. One area that has witnessed growing interest is the automation of routine outdoor tasks, such as lawn maintenance. Traditionally, lawn mowing has been laborintensive, requiring significant time, energy, and physical effort. This limitation has fueled research into robotic lawn mowers, which can perform the task autonomously, reducing human involvement and improving operational efficiency. The integration of AI and Global Positioning System (GPS) technologies with microcontroller-based platforms, such as the Raspberry Pi, presents an opportunity to develop intelligent, adaptable, and cost-effective solutions. This research focuses on an AI-powered GPS-based robotic lawn mower capable of autonomous navigation and control, offering a modern alternative to manual and semi-automated lawn maintenance methods. Evolution of Robotic Lawn Mowers Robotic lawn mowers first emerged in the late 20th century as simple programmable devices that followed predefined paths, often relying on boundary wires to limit their movement. While these early designs reduced physical labor, they lacked intelligence, adaptability, and efficiency. Over time, developments in sensor technologies, computer vision, and real-time data processing have enabled significant improvements in autonomous navigation. Modern commercial robotic mowers often feature obstacle detection sensors, path optimization algorithms, and environmental adaptability. However, many of these solutions are either prohibitively expensive or lack advanced AI capabilities for dynamic decision-making. The integration of low-cost, highperformance platforms like Raspberry Pi, paired with AI algorithms and GPS-based guidance, opens the door to affordable yet intelligent systems that can operate with minimal human supervision.
Key Words: Autonomous navigation, Robotic lawn mower, RTK-GPS, Raspberry Pi, Obstacle detection, and AI path planning.
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