Autonomous Drone Flight Path Generation for Remote Vehicle Inspection
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Solution Overview
Problem
Existing remote vehicle viewing solutions using drones are hindered by network delays, user skill limitations, and potential property damage due to uncontrollable drone movements, especially in environments with wind and obstacles.
Innovation Solution
A system that generates and transmits a predetermined flight path to an aerial drone based on vehicle coordinates, orientation, and wind conditions, allowing the drone to capture specific views of a vehicle while avoiding obstacles and other drones, ensuring safe and controlled operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users remotely pilot a drone to view vehicles, then users can choose specific viewing locations and angles, but network delays and lack of user skill make this solution technically insufficient
Solution Approach 1:
The system introduces an autonomous flight controller as an intermediary between the user and the drone. The controller receives viewing location requests from users, automatically plans safe flight paths that avoid obstacles and other drones, and executes the drone navigation without requiring direct user piloting. This mediator resolves the contradiction by enabling user choice of viewing locations while eliminating the technical insufficiencies of direct remote piloting.
Solution Approach 2:
The drone equips itself with autonomous navigation capabilities including obstacle detection sensors, wind condition sensors, and an integrated flight control system. The drone independently adjusts its flight path in real-time to avoid obstacles and compensate for wind effects, without requiring continuous user intervention. This self-service capability enables the drone to autonomously achieve user-selected viewing locations while maintaining safety and reliability.
2Adaptability or versatility
If users directly control drones, then users can select viewing angles, but possible property damage from wind (e.g., the drone being blown into the vehicle or a nearby vehicle) occurs
Solution Approach 1:
The system performs preliminary wind condition assessment before drone deployment using wind sensors and environmental data. The autonomous flight controller pre-calculates safe flight paths that account for predicted wind effects, positioning the drone at safe distances from vehicles and obstacles before wind conditions become problematic. This preliminary action prevents property damage by proactively compensating for wind effects rather than reacting to them after damage risk arises.
Solution Approach 2:
The drone incorporates real-time wind sensing and obstacle detection capabilities that continuously feed information back to the autonomous flight controller. The controller dynamically adjusts the drone's flight path in response to changing wind conditions and detected obstacles, maintaining safe distances from vehicles and preventing property damage. This closed-loop feedback system enables the drone to adapt to environmental conditions while protecting against harmful effects.
3Productivity
If a drone is deployed to view a vehicle, then specific views can be captured, but interference from other drones and obstacles must be avoided
Solution Approach 1:
The system merges the navigation and obstacle avoidance functions of multiple drones into a centralized autonomous flight control system. The controller receives data from all drones in the environment, calculates coordinated flight paths that prevent interference between drones, and distributes optimized navigation commands to each drone. This merging of control functions enables efficient multi-drone operation while reducing the complexity burden on individual drones.
Solution Approach 2:
The autonomous flight controller dynamically changes flight parameters such as altitude, speed, and position based on real-time environmental conditions and the presence of other drones. When obstacles or other drones are detected, the controller automatically adjusts the drone's flight path parameters to maintain safe distances while still achieving the desired viewing objective. This parameter adaptation enables the drone to navigate complex environments with multiple moving elements.
Data Source
AI summary
Systems, methods, and computer-readable storage media for generating a flight path for aerial drones around parked vehicles, where the flight path navigates an aerial drone to specific locations associated with predetermined views of the vehicle. A remotely located user can select from a list of predetermined views where the drone should travel, and the computer system can direct the drone to a location associated with the selected view, align the camera on the drone to capture the selected view, and relay live video of the selected view of the parked car to the user.