Aircraft Maintenance Robot Positioning With 3D Point Cloud Matching
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Solution Overview
Problem
Industrial robots face challenges in automating aircraft maintenance tasks, such as distinguishing aircraft from other objects and determining their position without prior knowledge, due to dynamic environments and varying aircraft configurations.
Innovation Solution
A robot equipped with odometry systems and 3D vision sensors, utilizing neural networks to generate and process point cloud data for identifying target aircraft by comparing geometric features with reference models, allowing it to navigate and perform tasks like fastener removal or installation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object detection methods are used, then the robot can detect objects in the enclosure, but the robot cannot accurately distinguish the target aircraft from other objects without prior knowledge
Solution Approach 1:
The system performs preliminary mapping of the enclosure using LiDAR and camera sensors to create a point cloud representation before the aircraft arrives. This preliminary action allows the robot to have advance knowledge of the environment layout without knowing the specific aircraft location, enabling it to quickly identify and distinguish the target aircraft when it appears in the enclosure.
Solution Approach 2:
The system transitions from 2D image data to 3D point cloud data by combining LiDAR range information with camera images. This dimensional enhancement provides depth and spatial context that allows the robot to accurately distinguish the aircraft's three-dimensional shape and features from other objects in the enclosure, significantly improving identification accuracy without requiring prior knowledge of aircraft position.
2Adaptability or versatility
If the robot is deployed without prior knowledge of the aircraft's position, then the robot can be flexibly deployed to any enclosure, but the robot cannot determine its position relative to the aircraft
Solution Approach 1:
The system continuously captures real-time point cloud data from the enclosure and compares it against the stored reference model of the target aircraft. This feedback loop allows the robot to dynamically update its understanding of the environment and accurately determine its position relative to the aircraft, even when deployed without prior knowledge of the aircraft's location. The system adjusts its navigation and positioning based on this continuous feedback.
Solution Approach 2:
The system creates a digital copy (point cloud representation) of the physical enclosure and aircraft. By comparing the real-time sensed point cloud against this digital copy and reference models, the robot can accurately determine its position and orientation relative to the target aircraft without requiring pre-programmed location information, thus maintaining deployment flexibility while achieving precise positioning.
3Measurement precision
If the robot uses detailed feature extraction to identify the target aircraft, then the identification accuracy improves, but the processing time increases
Solution Approach 1:
The system performs preliminary extraction and storage of key geometric features from reference models of target aircraft before deployment. During operation, instead of processing complete high-resolution point clouds in real-time, the system compares pre-extracted feature sets (such as shape descriptors, geometric moments, and key landmark positions) against the current sensor data. This preliminary preparation significantly reduces processing time while maintaining high identification accuracy.
Solution Approach 2:
The system extracts only the most discriminative geometric features from the complete point cloud data, such as shape descriptors, curvature characteristics, and key structural landmarks, rather than processing all raw data points. This selective extraction of essential features maintains high identification accuracy by focusing on the most informative characteristics while dramatically reducing the computational burden and processing time required for aircraft identification.
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 the robot to accurately identify and position itself relative to aircraft in dynamic environments, improving efficiency and safety in aircraft maintenance operations.
Implementation Method 1
a light detection and ranging (LiDAR) sensor configured to collect data representing a point cloud of the enclosure
Implementation Method 2
The point cloud data can be preprocessed to generate inputs for a first neural network. The first neural network can extract features from the point cloud data that can be used to distinguish the target aircraft from other objects in the hangar.
Implementation Method 3
The robot can use the collected data to identify a target aircraft and navigate to the target aircraft
Data Source
AI summary
Techniques for auto-locating and autonomous operation data storage are disclosed. An example method can include storing a representation of an aircraft in a data storage of the computing system. The representation and instructions for performing a first operation on the aircraft can be transmitted to a first robot. The first robot can be configured to identify the aircraft based on a first feature of the representation. The method can further include receiving sensor data. The method can further include transmitting the sensor data to an analysis service to identify a state of a part of the aircraft. The method can further include storing the analysis in the data storage. The method can further include generating second instructions for performing a second operation on the aircraft based on the analysis and the representation stored in the data storage.