Robotic Lawnmower Vegetation Sensing for Adaptive Mowing Schedules

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Solution Overview

Problem

Current robotic lawnmowers lack the ability to autonomously adapt their mowing schedules and operations 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 adjustments to the mowing schedule to optimize lawn care.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If robotic lawnmowers operate autonomously without real-time vegetation sensing, then device complexity is reduced, but adaptability to lawn conditions deteriorates

Engineering Contradiction:

Improveadaptability to lawn conditionsVSAvoiddevice complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The robotic lawnmower incorporates vegetation characteristic sensors that continuously detect lawn conditions and feed this information back to the control system, enabling real-time adjustments to mowing operations based on actual vegetation state

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system autonomously monitors its own operating conditions and automatically adjusts mowing parameters without human intervention, allowing the lawnmower to self-optimize its performance based on real-time vegetation feedback

Inventive Principle:

Principle #25Self-service

2Productivity

If robotic lawnmowers use fixed preprogrammed schedules, then ease of operation is improved, but productivity deteriorates due to inefficient lawn maintenance

Engineering Contradiction:

Improvemowing efficiencyVSAvoidease of operation

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The mowing schedule transitions from a static preprogrammed timetable to a dynamic schedule that automatically adjusts based on real-time vegetation characteristics detected by sensors, optimizing mowing frequency and timing according to actual lawn conditions

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system modifies mowing parameters such as frequency, timing, and cutter operation based on detected vegetation characteristics like grass height and density, allowing adaptive optimization of productivity without requiring complex user programming

Inventive Principle:

Principle #35Parameter changes

3Reliability

If robotic lawnmowers lack real-time vegetation detection, then device complexity is reduced, but harmful effects on the lawn increase due to potential damage

Engineering Contradiction:

Improvelawn health protectionVSAvoiddevice complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The vegetation sensors detect lawn conditions in advance and predict potential damage risks, allowing the control system to take preventive actions such as adjusting cutter operation or pausing mowing before damage occurs

Inventive Principle:

Principle #9Preliminary anti-action

Solution Approach 2:

Real-time feedback from vegetation sensors enables continuous monitoring of lawn health indicators, allowing the system to immediately respond to adverse conditions and adjust operations to prevent damage

Inventive Principle:

Principle #23Feedback

4Adaptability or versatility

If robotic lawnmowers collect and analyze position-referenced vegetation data, then adaptability to lawn conditions is improved, but loss of information increases due to data management requirements

Engineering Contradiction:

Improvelawn condition analysis capabilityVSAvoiddata management complexity

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The system continuously collects position-referenced vegetation data and feeds it back to the control system for real-time analysis and decision-making, creating a closed-loop information system that adapts to lawn conditions

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system creates digital representations (maps) of the lawn with position-referenced vegetation characteristics, allowing virtual analysis and planning without physically altering the lawn, and enabling efficient data storage and processing

Inventive Principle:

Principle #26Copying

Data Source

PatentUS12114595B2Controlling robotic lawnmowers

Publication Date: 2024.10.15 IROBOT CORP

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.