Robot Path Planning for External Light Interference
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Solution Overview
Problem
Robots face inefficiencies in driving paths when operating in environments with external light sources, such as sunlight, due to false object detection and inability to distinguish between robot-generated light and external light.
Innovation Solution
A robot system that includes a light emitter, a camera, and processors to obtain and process images and distance information both with and without the light emitter active, allowing for the identification of external light areas and generation of effective driving paths that bypass these areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robot uses the camera to obtain distance information while the light emitter is outputting light, then the robot can obtain distance information to objects, but the robot cannot distinguish between robot-generated light and external light, causing false detection
Solution Approach 1:
The system alternates between emitting light and not emitting light in periodic cycles. During light emission periods, the camera captures images with robot-generated light; during non-emission periods, the camera captures images without robot-generated light. This periodic action enables the system to distinguish between light sources and accurately identify external light areas while maintaining reliable object detection.
Solution Approach 2:
The system compares images captured during light emission with images captured during non-emission periods. By analyzing the difference between these two sets of images, the system generates feedback information that identifies external light areas. This feedback mechanism allows the robot to correct its object detection by excluding false detections caused by external light, thereby maintaining both measurement precision and reliability.
2Productivity
If the robot generates a driving path based on object detection information, then the robot can navigate autonomously, but external light areas are misidentified as objects, causing inefficient driving paths
Solution Approach 1:
The system extracts and identifies external light areas by comparing images captured during light emission with those captured during non-emission periods. By separating the identification of external light areas from general object detection, the system removes the harmful effect of false detections. This extraction process enables the generation of accurate driving paths that avoid misidentifying external light areas as obstacles, thereby improving driving efficiency while maintaining correct environmental perception.
3Adaptability or versatility
If the robot operates in environments with external light sources, then the robot can function in diverse environments, but the robot receives external light and cannot distinguish it from robot-generated light, causing false object recognition
Solution Approach 1:
The system uses periodic light emission and non-emission cycles to create distinguishable imaging conditions. By alternating between these states, the system enables the camera to capture images under different lighting conditions, allowing the robot to adapt to diverse environments with external light sources while maintaining accurate light source discrimination through comparison of the captured images.
Solution Approach 2:
The system performs preliminary imaging during non-emission periods before generating the final driving path. By capturing images without robot-generated light in advance and comparing them with images taken during emission periods, the system preliminarily identifies external light areas. This preliminary action enables accurate light source discrimination in diverse environments before final navigation decisions are made.
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
The solution enhances the accuracy of distance information and improves driving efficiency by distinguishing external light areas from actual objects, thereby preventing collisions and ensuring stable navigation.
Implementation Method 1
a light emitter configured to output light
Implementation Method 2
obtain distance information to an object by receiving light reflected from the object
Data Source
AI summary
A robot includes: a light emitter configured to output light; a camera; and at least one processor configured to: obtain first information about an object using the camera while the light emitter is outputting the light, obtain second information about the object using the camera while the light emitter is not outputting the light, obtain third information about the object based on the first information and the second information, obtain information about an external light area based on at least one from among the first information, the second information, and the third information, and generate a driving path of the robot based on the information about the external light area.