Optical Reflection Pattern Sensing for Reflective Obstacle Misjudgment
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Solution Overview
Problem
Autonomous robots face misjudgments due to strong reflective signals from highly-reflective surfaces, leading to false alarms and navigation errors, as conventional light sensors struggle to differentiate between reflective lights and actual obstacles.
Innovation Solution
An optical sensing system employing multiple light sources and a processor to capture and analyze reflection patterns from horizontal and vertical linear lights, allowing for the differentiation between actual objects and misjudgments caused by reflective surfaces, thereby adjusting navigation accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conventional light sensor is used to detect obstacles, then the robot can navigate around the house, but misjudgments occur when receiving lights reflected by highly-reflective walls or floors
Solution Approach 1:
The detection area is divided into multiple segments including a first detection area for horizontal scanning and a second detection area for vertical scanning. The processor separately processes reflection patterns from each area, analyzing horizontal and vertical reflection characteristics independently to distinguish obstacles from highly-reflective surfaces
Solution Approach 2:
The system transitions from single-plane detection to multi-dimensional detection by introducing vertical scanning in addition to horizontal scanning. The light source emits light at different angles (horizontal and vertical directions) to create multi-dimensional reflection patterns that help differentiate obstacles from reflective surfaces
2Measurement precision
If the light sensor receives strong reflective signals from highly-reflective surfaces, then the sensor can detect the surfaces, but false alarms and misjudgments occur
Solution Approach 1:
The processor analyzes reflection patterns by comparing intensity distributions from different detection areas and iteratively determines whether detected objects are actual obstacles or false alarms caused by highly-reflective surfaces, adjusting navigation decisions based on this feedback analysis
Solution Approach 2:
The system detects variations in reflection intensity patterns (analogous to color changes in optical properties) by comparing the intensity distribution of reflected light from different directions and angles, identifying characteristic patterns that distinguish obstacles from highly-reflective surfaces
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
Effectively reduces false alarms and improves navigation accuracy by distinguishing between reflective signals and actual obstacles, enhancing the autonomous robot's ability to avoid collisions with highly-reflective surfaces.
Implementation Method 1
a light sensor... to capture a first frame by receiving first reflective lights from the detection area
Data Source
AI summary
An optical sensing system and a method for eliminating misjudgment of a reflective light are provided. The optical sensing system includes a first light source, a second light source, a light sensor, and a processor. The processor is configured to: control the first light source to scan a horizontal detection area; control the light sensor to capture a first frame by receiving first reflective lights from the horizontal detection area; obtain a first reflection pattern, and analyze the first reflection pattern to determine whether an object is within the first portion; if so, control the second light source to scan a first vertical detection area; control the light sensor to capture a second frame from the first vertical detection area; process the second frame to obtain a second reflection pattern, and analyze the second reflection pattern to determine whether the object is detected by a misjudgment.