Autonomous Electric Lawn Mower with Modular Arm
Modular; Robots; Autonomous
About
The problem it solves SERGAP is designed to solve several real-world problems related to lawn and green space maintenance: 🌱 Manual labor dependency: Conventional mowing requires physical effort, which can be risky for elderly or disabled workers. SERGAP automates this task using AI navigation. 🔊 Noise pollution: Traditional gas-powered mowers generate >85dB of noise. SERGAP operates silently (<50dB), making it ideal for public spaces like parks or campuses. 🌍 Carbon emissions: Powered by solar-charged batteries, SERGAP cuts emissions by up to 75% compared to gasoline mowers. 💰 Operational costs: By reducing fuel and labor costs, organizations can save up to 60% in maintenance expenses. 🧠 Lack of adaptability: With its modular system (mower, rice planter, blower, etc.), SERGAP adapts to different use cases and seasons. In short, SERGAP makes lawn care safer, greener, more efficient, and future-ready through autonomous technology and modular versatility.
How it's made
Challenges we ran into During development, we faced several technical and design challenges: ⚙️ Sensor fusion calibration: Integrating LiDAR, GPS-RTK, and 360° cameras into one synchronized system was tricky. Small timing offsets caused navigation errors. Solution: We implemented a timestamp-based synchronization buffer using ROS (Robot Operating System), which aligned all sensor inputs in real-time. 🔋 Power management: Balancing power supply between motors, sensors, and compute units while ensuring battery efficiency was complex. Solution: We optimized the motor PWM control and used solar panels as supplementary charging during idle periods. 🌧️ Environmental safety: The robot needed to detect rain and slippery conditions autonomously to avoid malfunction. Solution: We added humidity and rain sensors with an auto-return-to-base protocol if adverse weather is detected. These hurdles taught us the importance of iterative prototyping, system-level debugging, and designing with real-world unpredictability in mind.