IoT-Enabled Reinforcement Learning for Autonomous Robotic Lawn Mowers in Residential Gardens

Traditional automated lawn mowers sometimes have difficulties in navigating complex garden designs and changing to changing conditions, which causes ineffective efficiency and limited coverage. This research introduces a new way to improve the efficiency and autonomy of robotic lawn mowers in residential settings by combining Reinforcement Learning (RL) algorithms with Internet of Things (IoT) technologies. To help the robotic mower respond appropriately to environmental signals it presents a system that uses IoT sensors to collect data on topography, weather, and vegetation in real-time. It uses RL methods to make the mower better at mowing and navigating over time. With the autonomous mower attaining more accuracy in grass cutting while reducing energy consumption and operating expenses, our trial findings show considerable increases in coverage and efficiency compared to traditional techniques. Building on previous work in smart gardening technology to the body of understanding on IoT enabled autonomous systems for home use. The research addresses inefficient lawn management through the integration of IoT and RL, resulting in adaptive, accurate, and dependable robotic mowing for enhanced energy efficiency and coverage.