Multi-Sensor Robotic Feature Detection for Real-Time 3D Inspection
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Solution Overview
Problem
Conventional methods for detecting features and defects in enclosed spaces, such as pipelines, rely on human intervention and are prone to errors and inefficiencies. There is a need for an automated detection method with high accuracy that can be applied across multiple industries and domains.
Innovation Solution
A robotic system equipped with multiple sensors, including visual and infrared cameras, LIDAR, IMU, and motor encoders, that uses deep learning algorithms to recognize features and defects within an environment. The system tracks the robot's position, maps features to 3D representations, and aggregates data using a weighting algorithm to create an accurate output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional human intervention methods are used for detecting features and defects, then the process is simple to implement, but the detection accuracy and efficiency deteriorate due to human errors and susceptibility to mistakes
Solution Approach 1:
The system enables automated self-detection of features and defects through multiple sensors and deep learning algorithms, eliminating the need for human intervention in the detection process. The robot autonomously navigates, collects sensor data, processes images, and generates detection reports,实现ing self-service detection that improves accuracy while managing complexity through automation.
Solution Approach 2:
The patent replaces manual human inspection with an automated robotic system equipped with multiple sensors and deep learning algorithms. The mechanical and cognitive tasks previously performed by humans (visual inspection, defect identification) are substituted by sensor-based data collection and AI-powered image processing, thereby eliminating human errors while achieving high detection accuracy.
2Productivity
If multiple sensors and deep learning algorithms are deployed for automated detection, then detection accuracy and efficiency improve, but the device complexity increases
Solution Approach 1:
The patent combines multiple sensors (visual cameras, infrared cameras, LIDAR, IMU, motor encoders) into a single integrated robotic inspection system. These diverse sensing modalities are merged to collect complementary data about features and defects, enabling comprehensive detection that improves productivity while managing complexity through unified system architecture and coordinated sensor operation.
Solution Approach 2:
The robotic inspection system is designed with multi-functionality, capable of performing various detection tasks using different sensor types and deep learning algorithms. The system can detect visual features, thermal anomalies, dimensional variations, and structural defects, making it a universal inspection platform that improves productivity across multiple application scenarios while consolidating complexity into a single versatile system.
3Measurement precision
If multiple sensors are used to collect disjointed data sets, then comprehensive feature detection is achieved, but the difficulty of correlating and processing the data increases
Solution Approach 1:
The patent introduces an intermediary processing layer that receives data from multiple sensors and integrates them into a unified detection framework. The visual camera, infrared camera, LIDAR, and other sensors capture different aspects of features and defects, and the intermediary deep learning algorithms correlate this disjointed data by fusing it temporally and spatially, thereby achieving accurate feature detection while reducing the complexity of data correlation through systematic integration.
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 system enables efficient and accurate detection of features and defects in real-time or near real-time, reducing human error and improving inspection efficiency across various industries and domains.
Implementation Method 1
one or more spatial distance sensors such as LIDAR
Implementation Method 2
one or more IR cameras
Implementation Method 3
one or more IMU sensors
Data Source
AI summary
Robotic systems and associated methods are described herein. The robotic system may collect measurements from various sensors corresponding to motion of the robotic system, the surrounding environment of the robotic system, or both. The robotic system may generate measurement data based on the collected measurements. Measurements from a particular sensor may be processed in conjunction with different sensors of the robotic system, which may facilitate more accurate or more useful measurement data. The systems and methods of the present disclosure enable the detection, labeling, and locating of features in real time or near real time using the robotic system with little or no reliance on human interaction to detect and map the features. The disclosure provides enhanced accuracy and efficiency as it enhances the functionality and reduces the reliance on human detection of features.