Garden Tool Timing Control Using AI and User Feedback
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Solution Overview
Problem
Existing methods for planning the operation of gardening equipment, such as robotic lawnmowers, fail to accurately adapt to changing environmental conditions and user preferences, leading to inefficient and suboptimal mowing operations.
Innovation Solution
An AI system is trained using user-generated training data to determine optimal working time windows for gardening equipment, incorporating input data such as weather, lawn conditions, and user feedback to improve scheduling accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulation methods with grass growth simulation are used to determine working time windows, then a basic scheduling framework is provided, but accuracy and adaptability to user needs are insufficient
Solution Approach 1:
The system implements feedback by collecting user ratings on suggested working time windows and using this feedback to continuously train and improve the AI model. User preferences and actual outcomes are fed back into the system to refine future scheduling predictions, thereby increasing both accuracy and adaptability to individual user needs.
Solution Approach 2:
The AI model performs self-improvement by automatically training on collected user feedback data without requiring manual reconfiguration. The system serves itself by using its own operational data and user interactions to enhance its scheduling capabilities over time, improving accuracy while adapting to user preferences autonomously.
2Productivity
If robotic lawnmowers navigate chaotically in random paths, then coverage of the lawn is achieved, but sufficient time is required to mow completely and evenly
Solution Approach 1:
The system dynamically adjusts the mowing duration parameter based on real-time factors including grass height measurements, weather conditions, and lawn area characteristics. This dynamic adjustment allows the robotic lawnmower to optimize its operating duration for each specific mowing task, improving efficiency while ensuring complete and even coverage without requiring excessive time.
3Reliability
If the lawn is mowed daily at predetermined intervals, then regular maintenance is ensured, but resource utilization is inefficient and does not adapt to changing environmental conditions
Solution Approach 1:
The system changes the mowing schedule parameter from fixed predetermined intervals to dynamically determined working time windows based on grass growth simulation, weather conditions, and user preferences. This parameter change maintains reliable maintenance by ensuring lawns are mowed when needed while significantly improving resource utilization by avoiding unnecessary mowing operations during periods when grass growth is minimal or environmental conditions are unfavorable.
Data Source
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AI summary
The disclosure relates to a computer-implemented method for determining a working time window (TWO) for a garden tool (100), in particular a robotic lawnmower (101), a garden tractor (102), or a lawnmower (103). The working time window (TWO) is determined based on input data (ID), e.g., weather data, lawn property data, and user profile data, using a grass growth simulation (201) and/or a trained AI system (202), and is suggested to the user for evaluation. Based on the user evaluation data (UED), training data (TD) can be generated to train the AI system (202). By training the AI system (202) with the generated training data (TD), improved deployment plans for the garden tools can be created.