AI Mowing Time Window Control with User Feedback
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Solution Overview
Problem
Existing methods for planning and controlling the operation of garden devices, such as mowing robots, lack sufficient accuracy, adaptability to local conditions, and consideration of user preferences, leading to inefficient and suboptimal mowing schedules.
Innovation Solution
The implementation of an AI system trained on user-evaluated time windows for garden device operation, which uses simulative generated time windows as initial training data, allowing for improved scheduling based on user feedback and environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulative methods with grass growth simulation are used to determine operation time windows, then a basic scheduling framework is established, but accuracy and adaptability to user needs are insufficient
Solution Approach 1:
The system implements feedback by collecting user evaluations of suggested operation time windows and using this feedback to train an AI system. The AI system learns from user preferences and local conditions, continuously improving the accuracy and adaptability of operation time window determinations through iterative training cycles.
Solution Approach 2:
The system transitions from static simulative methods to a dynamic AI-based approach that adapts parameters based on learned patterns. The AI system modifies operation time window parameters by learning from historical user evaluations and local condition data, enabling adaptive scheduling that evolves with user needs.
2Productivity
If manual user requests or predetermined regular intervals are used for mowing, then operation simplicity is maintained, but efficiency and optimization of mowing schedules are reduced
Solution Approach 1:
The system enables self-service by allowing the garden device to autonomously determine optimal operation time windows based on grass growth simulations and learned patterns. The AI system automatically schedules operations without requiring manual user input, improving efficiency while managing complexity through automated decision-making.
Solution Approach 2:
The system performs preliminary action by predicting optimal operation time windows in advance using grass growth simulations and AI predictions. This allows the system to proactively schedule mowing operations before they are needed, optimizing productivity while maintaining manageable system complexity through advance planning.
3Manufacturing precision
If chaotically navigating mowing robots are used to mow the lawn in random paths, then device simplicity is maintained, but mowing quality and evenness are compromised
Solution Approach 1:
The system applies dynamics by adapting the mowing path from chaotic random navigation to a dynamic, optimized trajectory. The AI system learns optimal navigation patterns that balance mowing quality and time efficiency, enabling the robot to navigate more effectively while maintaining reasonable system complexity through adaptive learning.
Data Source
AI summary
The invention relates to a computer-implemented method for determining a time window for operation (TWO) of a garden device (100), in particular a mowing robot (101), a garden tractor (102) or a mower (103). The time window for operation (TWO) is determined according to input data (ID), e.g. weather data, lawn characteristics data and user profile data, by means of a grass growth simulation (201) and/or by means of a trained AI system (202) and proposed to the user for evaluation. Training data (TD) can be generated based on the user evaluation data (UED) in order to train the AI system (202). Improved garden device deployment plans can be generated by training the AI system (202) with the generated training data (TD).