Adaptive Scheduling for Robotic Mowers Using Load Profiling
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Solution Overview
Problem
Current outdoor service robots, such as robotic lawn mowers, operate on manually specified schedules that require frequent user adjustments due to seasonal and environmental changes, leading to inefficiencies and wear and tear, as accurately estimating task duration is challenging.
Innovation Solution
The system employs load profiling using sensors like battery voltage and motor current to estimate grass height and density, and environmental profiling to adjust mowing schedules based on grass growth models and weather conditions, allowing for adaptive task scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manually specified schedules are used for robotic mowers, then the system is simple to operate, but frequent user adjustments are required due to seasonal and environmental changes
Solution Approach 1:
The robotic mower autonomously monitors its own performance metrics (battery voltage, motor current) and environmental conditions, then automatically adjusts its mowing schedule without user intervention. The system profiles its load characteristics and uses this data to determine optimal mowing intervals, enabling self-service schedule adaptation to seasonal and environmental changes
Solution Approach 2:
The system continuously monitors performance metrics including battery voltage and motor current during mowing operations. This feedback is used to profile load characteristics and estimate grass height and density. The feedback loop enables the system to detect when schedule adjustments are needed and automatically implement them, eliminating the need for frequent manual user adjustments
2Measurement precision
If load profiling with multiple sensors is implemented, then grass height and density estimation accuracy improves, but device complexity increases
Solution Approach 1:
The system uses existing sensors (battery voltage monitor, motor current sensor) as intermediaries to indirectly measure grass height and density. Instead of directly measuring grass properties, the sensors monitor electrical load characteristics during mowing, which serve as proxies for grass conditions. This intermediary approach achieves accurate measurement without adding dedicated grass measurement sensors
Solution Approach 2:
The existing electrical sensors in the robotic mower are made multi-functional by using them both for their primary purpose (power management and motor control) and for grass condition measurement. The battery voltage and motor current sensors simultaneously monitor power consumption and estimate grass height and density, eliminating the need for separate measurement systems
3Productivity
If adaptive scheduling is implemented, then productivity increases by optimizing mowing frequency, but device complexity increases
Solution Approach 1:
The mowing schedule transitions from a static, pre-determined timetable to a dynamic system that automatically adapts to changing conditions. The scheduling interval becomes a variable parameter that adjusts based on real-time and historical data about grass growth rates, environmental conditions, and mower performance, enabling optimized productivity without rigid fixed schedules
Solution Approach 2:
The system performs preliminary profiling of load characteristics during initial mowing operations to establish baseline data about grass conditions and mower performance. This preliminary action creates a knowledge base that enables future schedule optimizations, allowing the system to proactively adjust schedules before inefficiencies occur rather than reacting to problems after they arise
Data Source
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AI summary
A method for scheduling mowing tasks by a robotic mower is provided. An estimated height of grass cut by the robotic mower is determined for a predetermined number of past mowing tasks. The estimated height of grass cut is compared with a predicted height of grass in an operating environment for the robotic mower. Then, a mowing schedule for the robotic mower is adjusted by decreasing a time between mowing tasks in response to the estimated height of grass cut being greater than the predicted height of grass. Alternatively, the mowing schedule for the robotic mower is adjusted by increasing the time between mowing tasks in response to the estimated height of grass cut being less than the predicted height of grass.