UAV Roof Scanning with 3D Flight Planning and Auto Docking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing unmanned aerial vehicle (UAV) systems struggle with efficient, autonomous roof scanning, particularly in maintaining consistent distance and orientation for robust image capture, requiring significant human intervention and being limited by battery life.
Innovation Solution
An unmanned aerial vehicle system that performs an initial coarse scan, generates facets based on a three-dimensional map, allows user feedback for editing, and executes a scan plan with automated docking and charging, using visual inertial odometry for localization and obstacle detection, enabling consistent image capture and extended operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image capture modes are implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-planning the entire scan mission, including generating a 3D map of the roof, identifying facets, calculating optimal flight paths, and sequencing image capture positions before the actual scanning begins. This pre-computation enables automated execution without real-time complex decision-making, resolving the contradiction between automation and complexity.
Solution Approach 2:
The roof scanning task is segmented into discrete facets, with each facet assigned specific capture positions and flight path segments. This segmentation transforms a complex continuous scanning problem into manageable discrete units, enabling automated control while reducing overall system complexity through modular processing.
2Manufacturing precision
If manual control is used for precise positioning, then manufacturing precision is improved, but productivity deteriorates
Solution Approach 1:
The system replaces manual mechanical control with automated visual inertial odometry and pre-calculated flight paths. The UAV autonomously navigates to precise positions using computer vision and inertial sensing, eliminating the need for manual positioning while maintaining high precision and enabling faster scanning throughput.
Solution Approach 2:
All positioning calculations, flight paths, and capture positions are pre-computed based on the 3D roof map and facet analysis. This preliminary action enables the UAV to automatically execute precise positioning without real-time manual intervention, achieving both high precision and improved productivity.
3Duration of action of moving object
If battery capacity is increased to extend operation, then duration of action is improved, but weight of moving object increases
Solution Approach 1:
The scanning mission is segmented into multiple phases corresponding to different battery charge levels. The system monitors battery status and automatically plans intermediate docking points where the UAV can land, recharge, and resume scanning. This segmentation enables extended operational duration without requiring excessive battery capacity, avoiding unnecessary weight increase.
Solution Approach 2:
The docking station serves as an intermediary that provides battery recharging capability. Instead of relying on a single large battery, the system uses the docking station as an external energy reservoir, allowing the UAV to perform multiple scanning cycles with a smaller onboard battery, thus reducing UAV weight while extending total operational duration.
Data Source
AI summary
Described herein are systems for roof scan using an unmanned aerial vehicle. For example, some methods include capturing, using an unmanned aerial vehicle, an overview image of a roof of a building from above the roof; presenting a suggested bounding polygon overlaid on the overview image to a user; determining a bounding polygon based on the suggested bounding polygon and user edits; based on the bounding polygon, determining a flight path including a sequence of poses of the unmanned aerial vehicle with respective fields of view at a fixed height that collectively cover the bounding polygon; fly the unmanned aerial vehicle to a sequence of scan poses with horizontal positions matching respective poses of the flight path and vertical positions determined to maintain a consistent distance above the roof; and scanning the roof from the sequence of scan poses to generate a three-dimensional map of the roof.