Aircraft Auto-Locating Robot Using 3D Point Cloud Navigation
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Solution Overview
Problem
Industrial robots face challenges in distinguishing aircraft from other objects in a hangar and navigating towards the aircraft without causing damage, especially when the aircraft's location is unknown or changing.
Innovation Solution
A robot equipped with odometry systems and three-dimensional vision sensors, such as LiDAR, collects data to generate a point cloud of the hangar. This data is processed using neural networks to identify the target aircraft and navigate towards it, verifying the target through continuous comparison with a reference model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object detection methods are used, then the robot can detect objects in the hangar, but the robot cannot distinguish the target aircraft from other objects and cannot determine its position relative to the aircraft
Solution Approach 1:
The patent transitions from conventional 2D image detection to 3D point cloud representation using LiDAR technology. This dimensional enhancement allows the robot to capture spatial depth information and geometric features of objects, enabling accurate distinction between the target aircraft and other hangar objects through three-dimensional shape analysis rather than relying solely on two-dimensional visual patterns.
Solution Approach 2:
The system changes the detection parameters by using multiple laser beams with different angles to scan objects, creating a comprehensive point cloud representation. By varying the scanning angles and using multiple measurement parameters (distance, angle, reflectivity), the system achieves precise object identification and positioning without requiring overly complex detection hardware.
2Extent of automation
If the robot navigates autonomously without prior knowledge of aircraft location, then the robot can operate independently, but the robot may collide with objects or fail to locate the target aircraft efficiently
Solution Approach 1:
The robot employs continuous feedback mechanisms where the LiDAR system constantly scans the environment, compares detected objects with the target aircraft model, and adjusts the navigation path in real-time. This closed-loop control allows the robot to verify target identification, avoid obstacles, and maintain safe navigation without human intervention, significantly improving both autonomous operation and navigation safety.
Solution Approach 2:
The system performs preliminary scanning and mapping of the hangar environment before initiating target acquisition. By pre-processing the spatial data and identifying potential obstacles or safe pathways in advance, the robot prepares navigation routes that minimize collision risks while maintaining autonomous operation, balancing automation extent with navigation reliability.
3Measurement precision
If the robot uses multiple sensors and complex processing, then the robot can accurately identify and navigate to the aircraft, but the system complexity and computational requirements increase
Solution Approach 1:
The patent divides the complex identification task into segmented processing stages: first, the LiDAR system collects raw point cloud data; second, the system extracts geometric features from the point cloud; third, the system compares extracted features with the target aircraft model; and finally, the system confirms target identification. This segmentation allows accurate aircraft identification while managing system complexity through modular processing architecture.
4Measurement precision
If the robot continuously scans and verifies the target, then the robot can ensure accurate positioning, but the time required to complete the task increases
Solution Approach 1:
The system performs partial verification by scanning only critical geometric features of the target aircraft that are sufficient for accurate identification and positioning, rather than continuously scanning the entire aircraft. This selective verification approach maintains positioning accuracy while reducing the time required for target confirmation, optimizing the balance between precision and efficiency.
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 robot effectively identifies and navigates to the target aircraft, ensuring accurate positioning for maintenance tasks without causing damage to the aircraft or other objects in the hangar.
Implementation Method 1
A robot equipped with odometry systems and three-dimensional vision sensors, such as LiDAR, collects data to generate a point cloud of the hangar
Data Source
AI summary
Techniques for auto-locating and autonomous operation data storage are disclosed. An example method can include storing a multi-dimensional representation of an aircraft in a data storage. The method can further include causing transmission of the multi-dimensional representation of the aircraft and first control instructions to a robot for performing a first autonomous operation on the aircraft, the robot configured to identify the aircraft based on the multi-dimensional representation, the aircraft located at a premises. The method can further include processing sensor data generated at the premises. The method can further include generating second control instructions for performing a second autonomous operation on the aircraft based on the sensor data. The method can further include causing transmission of the second control instructions to the robot for performing the second autonomous operation on the aircraft.