Structured Light Obstacle Detection for Low-Profile Blind Spots
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Solution Overview
Problem
Existing autonomous mobile devices, such as floor sweeping robots, struggle to detect low obstacles due to limitations in their sensors, leading to collisions and entanglements during operation.
Innovation Solution
Equipping the devices with a structured light module comprising line laser sensors and a camera module to collect environmental information, and implementing omission remediation actions to supplement detection in blind areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensors (laser radars or cameras) are used for obstacle detection, then the device can detect obstacles in general, but low obstacles cannot be detected accurately leading to collisions and entanglements
Solution Approach 1:
The patent combines multiple sensing modalities (laser radar, camera, and structured light module) into an integrated obstacle detection system. The structured light module projects light patterns and captures reflected light to detect low obstacles that traditional sensors miss, while the processor fuses data from all sensors to achieve comprehensive and accurate obstacle detection, resolving the contradiction between general detection capability and low obstacle detection accuracy.
2Loss of information
If the structured light module is used to collect environmental information, then the richness and accuracy of information collection is improved, but blind area ranges exist where obstacles cannot be detected
Solution Approach 1:
The patent implements dynamic detection by controlling the autonomous mobile device to execute omission remediation actions when obstacles are detected in blind areas. The device adjusts its movement and detection strategy in real-time, performing supplementary detection maneuvers to cover blind spots, thereby transforming the static detection limitation into a dynamic solution that adapts to detected obstacles and eliminates information loss in blind areas.
3Area of stationary object
If omission remediation actions are executed to detect blind area obstacles, then detection coverage is improved, but the complexity of the detection process increases
Solution Approach 1:
The patent employs feedback control where the processor continuously monitors obstacle detection results and automatically triggers omission remediation actions when obstacles are detected in blind areas. The system receives feedback from the structured light module about detection status, processes this information, and executes appropriate remediation maneuvers, creating a closed-loop control system that expands detection coverage through automated feedback-driven actions rather than manual intervention.
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
Enhances the richness and accuracy of environmental information collection, enabling effective obstacle detection and avoidance, and facilitating comprehensive environmental map construction.
Implementation Method 1
collecting obstacle information within a front area using a structured light module
Implementation Method 2
structured light module comprising line laser sensors and a camera module
Data Source
AI summary
Disclosed are an information collection method, a device and a storage medium. An autonomous mobile device may collect environmental information by means of a structured light module, and may collect, in a supplementary manner, obstacle information within a blind area range of the structured light module by executing an omission remediation action, such that the autonomous mobile device detects richer and more accurate environmental information during a task execution process, thereby avoiding omitting information of lower obstacles. The obstacles can be avoided and an environmental map can be constructed according to the detected obstacle information, thereby providing a foundation for subsequent working task execution and obstacle avoidance.