Smart Lawn Mower Vision Control for Boundary-Free Grass Detection
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Solution Overview
Problem
Current smart lawn mowers face challenges in accurately determining grass areas without pre-defined boundaries, as they rely on default datasets that are inadequate for varying lighting conditions and types of grass, leading to inaccurate grass area detection.
Innovation Solution
A smart lawn mower equipped with an image capturing module, machine learning module, and traveling control module that analyzes surrounding images to determine the grass area by adjusting neural network weights based on real-time image data, allowing it to autonomously cut grass without pre-defined boundaries by integrating user registration and environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the smart lawn mower uses default datasets to determine grass area, then the mower can operate autonomously without pre-defined boundaries, but the accuracy of grass area determination deteriorates under varying lighting conditions and grass types
Solution Approach 1:
The system performs preliminary actions by having users manually annotate grass areas in images before actual operation. These pre-collected and pre-annotated images serve as training data that prepare the machine learning model for accurate grass area determination in various conditions, resolving the contradiction between autonomous operation and determination accuracy.
Solution Approach 2:
The system implements feedback mechanisms where the smart lawn mower continuously captures images during operation, compares detected grass areas with actual conditions, and uses this feedback to refine its determination accuracy. The machine learning model is updated based on real-world performance data, improving accuracy while maintaining autonomous operation.
2Device complexity
If the smart lawn mower relies on fixed default datasets, then the device complexity is reduced, but the adaptability to different lighting environments and grass types deteriorates
Solution Approach 1:
The system transitions from static default datasets to dynamic, adaptive datasets that evolve with machine learning. The machine learning model continuously adapts to different lighting conditions and grass types by learning from new data, providing environmental adaptability while maintaining relatively simple device architecture through software-based solutions.
Solution Approach 2:
The system changes parameters by adjusting machine learning model weights and parameters based on environmental conditions. Instead of changing the physical structure of the device, the system adapts by modifying computational parameters of the machine learning model to suit different lighting environments and grass types.
3Measurement precision
If the smart lawn mower uses pre-defined boundaries, then the grass area determination accuracy is improved, but the ease of operation deteriorates as users must manually set boundaries
Solution Approach 1:
The system implements self-service by enabling the smart lawn mower to automatically determine grass areas using machine learning without requiring users to manually set boundaries. The mower autonomously captures images, processes them through the trained machine learning model, and identifies grass areas independently, improving ease of operation while maintaining determination accuracy.
Data Source
AI summary
A smart lawn mower comprises a traveling control module configured to control the traveling and steering of the mower, an image capturing module configured to capture the surrounding images of the mower, an operation module configured to provide a surrounding-determination information, and a storage module configured to store the surrounding-determination information. The operation module determines a grass area by analyzing the surrounding images captured by the image capturing module. The mower defines a grass area accurately without a predetermined boundary.