Robotic Lawnmower Scheduling Using Vegetation and Weather Sensing
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Solution Overview
Problem
Current robotic lawnmowers lack the ability to autonomously adjust their mowing schedules based on real-time vegetation characteristics and fluctuating weather conditions, leading to inefficient lawn maintenance and potential damage to the lawn.
Innovation Solution
Equipping robotic lawnmowers with vegetation characteristic sensors (such as optical, pressure, and capacitance sensors) and a communication system to gather data on grass height, color, moisture content, and weather conditions, allowing for the generation of position-referenced data and adjustment of mowing schedules to optimize lawn care.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If robotic lawnmowers use fixed preprogrammed mowing schedules, then operation simplicity is maintained, but adaptability to real-time vegetation and weather conditions deteriorates
Solution Approach 1:
The patent implements dynamic mowing schedules that automatically adjust based on real-time sensor data about vegetation characteristics (grass height, moisture content, color) and weather conditions. The system transitions from static preprogrammed schedules to dynamic adaptive scheduling, allowing the robotic lawnmower to respond to changing environmental conditions while maintaining automated operation.
Solution Approach 2:
The system incorporates feedback loops where sensors continuously monitor vegetation and weather conditions, and this information is used to adjust mowing schedules. The robotic lawnmower receives feedback from the environment and modifies its operation accordingly, creating a closed-loop control system that balances automation with adaptability.
2Adaptability or versatility
If robotic lawnmowers equip vegetation sensors and communication systems, then adaptability to lawn conditions improves, but device complexity increases
Solution Approach 1:
The patent integrates multiple functions into the robotic lawnmower system: vegetation sensing (optical, pressure, capacitance sensors), weather monitoring, position tracking, and automated schedule adjustment. These multi-functional components allow the system to adapt to various lawn conditions without requiring separate dedicated devices for each function.
Solution Approach 2:
The system uses communication systems and processing units as intermediaries between the sensors and the control mechanisms. These intermediaries process sensor data and translate it into actionable schedule adjustments, managing the complexity by creating layered information processing stages.
3Manufacturing precision
If robotic lawnmowers collect and process position-referenced vegetation data, then lawn care precision improves, but information processing requirements increase
Solution Approach 1:
The patent divides the mowing area into segments or zones with specific vegetation characteristics. Position-referenced data is collected and processed by geographic location, allowing the system to create targeted mowing schedules for different areas based on their specific conditions, improving precision while managing data processing through spatial organization.
Solution Approach 2:
The system applies different mowing parameters and schedules to different locations based on local vegetation conditions. Each area receives customized treatment based on its specific grass height, moisture content, and other measured characteristics, ensuring optimal care for each locale rather than applying uniform settings across the entire lawn.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables informed decision-making for users by providing detailed lawn health summaries and automatically adapting to changing conditions, promoting healthier lawn growth and reducing maintenance inefficiencies.
Implementation Method 1
The vegetation characteristic sensor can include an optical sensor configured to detect the color
Implementation Method 2
The vegetation characteristic sensor can include a pressure sensor configured to detect the grass height
Implementation Method 3
The vegetation characteristic sensor can include a capacitance sensor configured to detect the moisture content
Data Source
AI summary
A method of mowing with an autonomous robot lawnmower includes traversing a mowable area with the autonomous robot lawnmower carrying a cutter and a vegetation characteristic sensor. The vegetation characteristic sensor is configured to generate sensor data in response to detecting a vegetation characteristic of the mowable area. The vegetation characteristic is selected from the group consisting of a moisture content, a grass height, and a color. The method includes storing position-referenced data representing the vegetation characteristic detected across the mowable area. The position-referenced data is based at least in part on the sensor data and position data. The method includes sending data to a remote device to cause the remote device to display a map including information based on the position-referenced data.