Autonomous Drone Navigation for Wind Turbine Inspection
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Solution Overview
Problem
Current drone navigation systems for inspecting wind turbines and similar structures face challenges in obtaining high-resolution images due to reliance on absolute positioning methods, which are inaccurate and require human intervention, especially in extreme environments like wind sectors.
Innovation Solution
A method and device using a combination of LiDAR scanners, image processing, and inertial sensor fusion for autonomous navigation, allowing drones to maintain a predetermined relative distance and centering on structures despite wind disturbances and orientation changes, eliminating the need for GPS and human piloting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If absolute positioning measurements such as GPS are used for drone navigation, then the navigation system is simple to implement, but the positioning accuracy deteriorates and high-resolution image capture becomes impossible
Solution Approach 1:
The patent introduces visual references (fiducial markers) as intermediary objects placed on the target structure. These markers serve as mediators between the drone and the structure, enabling precise relative positioning through computer vision tracking of the markers' position and orientation, thereby achieving high positioning accuracy without complex absolute positioning systems
Solution Approach 2:
The patent replaces GPS-based mechanical/electromagnetic positioning with an optical-based computer vision system. By using cameras to detect visual references and calculate relative position through image processing, the system achieves superior accuracy while maintaining manageable complexity through software-based solutions
2Manufacturing precision
If the drone flies at a close predetermined distance from the target structure, then high-resolution images can be captured, but the drone becomes highly susceptible to wind disturbances and trajectory deviations
Solution Approach 1:
The patent implements continuous feedback control by constantly tracking the position and orientation of visual references on the target structure. The system uses this real-time feedback to calculate required trajectory adjustments and automatically corrects deviations caused by wind disturbances, maintaining both close proximity for high-resolution imaging and stable positioning
Solution Approach 2:
The patent employs dynamic trajectory adjustment where the drone's flight path is continuously adapted based on real-time visual feedback. The navigation system dynamically modifies speed, position, and orientation to compensate for environmental disturbances while maintaining the optimal close distance for image capture
3Productivity
If manual remote control is used to adjust the drone trajectory, then the navigation system remains simple, but human intervention is required and inspection efficiency decreases
Solution Approach 1:
The patent enables the drone to perform self-navigation by automatically detecting visual references, calculating its own position and orientation relative to the target structure, and autonomously adjusting its trajectory without human intervention. This self-service capability significantly improves inspection efficiency while maintaining manageable system complexity through software-based autonomous control
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
This solution provides accurate, repeatable, and consistent high-resolution image capture of wind turbine components, reducing inspection time and eliminating human interaction, while being robust against wind conditions and geometric variations.
Implementation Method 1
LiDAR is a surveying method that measures distance to a target by illuminating that target with a pulsed laser light, and measuring the reflected pulses with a sensor
Implementation Method 2
Keep the target (e.g., the windmill structure) centered in the image recorded by the camera of the drone
Implementation Method 3
a combination of measurements obtained by a LiDAR laserscanner, which can either be two-dimensional (2D) or three-dimensional (3D), image processing and inertial sensor fusion
Data Source
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AI summary
A drone (1) and method of autonomous navigation for tracking objects, wherein computer vision and LiDAR sensors of the drone (1) are used and comprising: - detecting by both calibrated computer vision and LiDAR sensors at least an object to be tracked by the drone (1), - measuring by the LiDAR sensor a set of features of the detected object, - estimating a relative position of the drone (1) and the detected object; - commanding the drone (1) to reach a target waypoint which belongs to a set of waypoints determining a trajectory, the set of waypoints being defined based on the measured features of the detected object and the estimated relative position; - once the target waypoint is reached by the drone (1), adjusting the trajectory by redefining a next target waypoint from the set of waypoints to keep the detected object centered on the computer vision sensor.