Wearable Laser Scanner for Real-Time Obstacle Detection
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Solution Overview
Problem
Conventional methods for aiding mobility in visually impaired individuals, such as guide dogs and human companions, are either costly or require one hand, and existing technologies fail to accurately and efficiently detect obstacles in real-time.
Innovation Solution
A wearable system that uses a Light Amplification by Stimulated Emission of Radiation (LASER) scanner to generate beams towards multiple regions in the environment, receiving feedback signals from obstacles, and transforming these signals into obstacle information using Machine Learning algorithms, which activates pressure elements on a wearable device to provide tactile feedback to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods (guide dogs, human companions, sticks) are used to aid mobility, then visually impaired people can navigate their environment, but one hand must be occupied or high maintenance costs are incurred
Solution Approach 1:
The patent replaces mechanical assistance systems (guide dogs, human companions, walking sticks) with an optical sensing system (LASER scanner) that emits laser beams and processes reflected signals to detect obstacles, thereby freeing the user's hands while providing reliable mobility assistance
Solution Approach 2:
The patent introduces a wearable device as an intermediary between the visually impaired user and the environment, containing an obstacle matrix that translates complex environmental information into simplified tactile pressure signals that the user can intuitively understand and respond to
2Measurement precision
If Machine Learning algorithms based on image recognition are used to detect obstacles, then detection accuracy improves, but computational resource requirements increase response time
Solution Approach 1:
The patent extracts only the essential features needed for obstacle detection (distance, height, depth information from laser beam reflections) rather than processing complete images, thereby maintaining detection accuracy while significantly reducing computational resource requirements and enabling real-time response
Solution Approach 2:
The patent performs preliminary processing of laser feedback signals to extract key obstacle parameters before applying ML algorithms, preparing the data in advance in a format that requires minimal computational processing during critical detection moments, thus reducing response time
3Measurement precision
If the environment is divided into multiple regions with corresponding pressure elements, then obstacle location precision improves, but device complexity increases
Solution Approach 1:
The patent divides the scanned environment into multiple discrete regions, with each region mapped to a corresponding pressure element in the obstacle matrix, enabling precise localization of obstacles while maintaining a manageable device structure through systematic segmentation
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 visually impaired individuals to detect obstacles both above and below ground level in real-time, keeping their hands free and reducing the need for costly assistance, while providing an additional safety measure.
Implementation Method 1
receiving, by the wearable LASER scanner, a feedback signal based on reflection of the LASER beam from an obstacle located in at least one of the plurality of regions
Data Source
AI summary
This disclosure relates to method and system for detecting obstacles in an environment of a user in real-time. The method includes generating a LASER beam towards each of a plurality of regions in the environment of the user; receiving a feedback signal based on reflection of the LASER beam from an obstacle located in at least one of the plurality of regions; transforming the feedback signal from the at least one of the plurality of regions into obstacle information through a Machine Learning (ML) algorithm; activating one or more of a plurality of pressure elements of an obstacle matrix based on the obstacle information of the at least one of the plurality of regions; and generating a predefined pressure through each of the one or more of the plurality of pressure elements of the obstacle matrix based on obstacle information of the at least one of the plurality of regions.