Leveraging spatial scanning data of autonomous robotic devices
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Solution Overview
Problem
Autonomous robotic devices, such as robotic vacuums, lack the capability to effectively analyze and utilize spatial scanning data to generate actionable recommendations for improving environmental conditions, leading to suboptimal cleaning patterns and potential issues like dirt buildup or allergen presence going unnoticed.
Innovation Solution
A robotic vacuum system that collects and analyzes spatial scanning data over multiple cleaning cycles to identify cleanliness trends, generating recommendations for users based on these trends, including alerts for dirt buildup, allergen presence, or wear patterns, and adjusting sensitivity levels based on user responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the robotic vacuum collects and analyzes cleanliness data over multiple cleaning cycles to identify trends, then the ability to generate actionable recommendations for improving environmental conditions is improved, but the device complexity increases
Solution Approach 1:
The robotic vacuum performs preliminary data collection during routine cleaning cycles, accumulating cleanliness data over time before analysis. The system pre-processes spatial scanning data and stores it for later trend identification, enabling future recommendations without adding complexity to the core cleaning function.
Solution Approach 2:
The patent introduces an intermediary processing layer that separates data collection from analysis. The robotic vacuum collects data during cleaning, stores it temporarily, and then analyzes it to generate recommendations. This intermediary storage and processing mechanism decouples the complexity of analysis from the simplicity of cleaning operations.
2Productivity
If the robotic vacuum generates specific recommendations for users based on cleanliness trends, then the productivity and cleaning efficiency are improved, but the difficulty of detecting and measuring cleanliness trends increases
Solution Approach 1:
The system implements feedback by analyzing cleanliness data from multiple cleaning cycles, identifying trends in dirt accumulation patterns, and generating recommendations based on these trends. The feedback loop continuously monitors cleanliness changes over time and adjusts recommendations accordingly, improving cleaning efficiency through data-driven insights.
Solution Approach 2:
The robotic vacuum applies partial analysis by focusing on specific areas or types of cleanliness issues that show significant trends, rather than analyzing all possible parameters equally. This selective approach to trend detection reduces measurement complexity while maintaining productivity improvements in critical areas.
Data Source
AI summary
Provided is a method, computer program product, and system for leveraging spatial scanning data of an environment collected by a robotic vacuum to generate recommendations for improving environmental conditions. A robotic vacuum may collect cleanliness data relative to an environment. The robotic vacuum may store the cleanliness data over a plurality of cleaning cycles. The robotic vacuum may analyze the cleanliness data over the plurality of cleaning cycles to identify one or more cleanliness trends. The robotic vacuum may generate a recommendation for improving an environmental condition relative to the environment based on the identified one or more cleanliness trends. The robotic vacuum may provide the recommendation to a user.