Surface type detection for robotic cleaning device
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Solution Overview
Problem
Robotic cleaning devices lack effective methods to determine and adapt to various surface types in environments, leading to inefficient cleaning routes and potential damage to surfaces.
Innovation Solution
The use of polarized light detection systems, including a light emitter and polaroid filters, to identify surface types by calculating the percentage difference in horizontal and vertical polarization of reflected light, allowing the robotic cleaning device to plan and execute optimized cleaning routes based on surface types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robotic cleaning devices use basic sensors for obstacle detection, then navigation capability is provided, but surface type detection precision is insufficient
Solution Approach 1:
The patent replaces mechanical surface detection methods with optical polarization detection. A light emitter projects polarized light onto the surface, and light detectors measure the polarization state of reflected light to determine surface type. This optical system provides precise surface type detection without mechanical contact, resolving the contradiction between detection precision and device complexity.
Solution Approach 2:
The patent utilizes changes in light polarization parameters (horizontal vs vertical polarization intensity ratios) to detect surface type. By measuring how different surfaces alter polarization states of reflected light, the system achieves high-precision surface identification. This parameter-based detection method enables accurate surface type classification while maintaining a relatively simple detection system architecture.
2Productivity
If robotic cleaning device traverses all detected surfaces, then cleaning coverage is maximized, but surface damage risk increases
Solution Approach 1:
The patent implements preliminary surface type detection using polarized light analysis before the robotic cleaning device commits to a cleaning route. By identifying delicate surfaces (such as rugs, polished floors, or sensitive materials) in advance, the system can pre-plan avoidance strategies or adjust cleaning parameters. This preliminary detection enables high cleaning coverage while preventing damage to vulnerable surfaces.
3Productivity
If robotic cleaning device uses simple route planning, then navigation speed is maintained, but cleaning efficiency on varied surfaces deteriorates
Solution Approach 1:
The patent applies local quality optimization to route planning by detecting surface types at different locations and adjusting cleaning parameters locally. The polarized light detection system identifies surface characteristics (carpet, hardwood, tile, etc.) and the controller modifies cleaning head pressure, speed, or tool selection based on local surface properties. This localized adaptation improves cleaning efficiency on varied surfaces without requiring complete re-planning of the entire route, thus minimizing time loss.
4Measurement precision
If robotic cleaning device uses optical polarization detection, then surface type detection precision is improved, but device complexity increases
Solution Approach 1:
The patent integrates the polarized light detection system into the existing robotic cleaning device architecture, allowing the optical system to serve multiple functions: surface type detection, surface condition assessment, and navigation assistance. By making the detection system universal rather than dedicated to a single function, the patent achieves high surface type detection precision while minimizing the increase in overall device complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the robotic cleaning device to accurately detect surface types, avoid damaging surfaces, and optimize cleaning operations by modifying its route and cleaning configurations accordingly, improving cleaning efficiency and surface protection.
Implementation Method 1
a type of a surface of the environment can be detected by the robotic cleaning device based on optical polarization of light
Implementation Method 2
a light emitter installed on the robotic cleaning device can produce light to be reflected from the surface at a particular angle
Implementation Method 3
a filter can be a polaroid filter configured to block a plane of vibration of an electromagnetic wave
Implementation Method 4
a first polaroid filter can be configured to block a first plane and a second polaroid filter can be configured to block a second plane
Data Source
AI summary
The present disclosure describes techniques for a robotic cleaning device to determine a plan to clean an environment based on types of surfaces in the environment. The plan can include a route to take in the environment and/or one or more configurations to apply to the robotic cleaning device during the route. Determining the plan can include inserting detected surface types into an environmental map of the environment. The environmental map can then be used on future routes such that the robotic cleaning device can know a surface type of a surface before the robotic cleaning device reaches the surface. In some examples, a type of a surface of the environment can be detected by the robotic cleaning device based on optical polarization of light.