1 / 16

Toward Computing An Optimal Trajectory For An Environment-Oriented Unmanned Aerial Vehicle (UAV)

Toward Computing An Optimal Trajectory For An Environment-Oriented Unmanned Aerial Vehicle (UAV). Rodrigo Romero, Jerald Brady, Octavio Lerma, Vladik Kreinovich, and Craig Tweedie. Goals of This Project (ES Perspective). To develop a mobile Environmental Science data collection platform

kane-dale
Download Presentation

Toward Computing An Optimal Trajectory For An Environment-Oriented Unmanned Aerial Vehicle (UAV)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Toward Computing An Optimal TrajectoryFor An Environment-Oriented UnmannedAerial Vehicle (UAV) Rodrigo Romero, Jerald Brady, Octavio Lerma, Vladik Kreinovich, and Craig Tweedie Joint Annual Meeting NSF Division of Human Resource Development

  2. Goals of This Project (ES Perspective) To develop a mobile Environmental Science data collection platform • Ensure system design is well documented and customizable • Near real time collection of data wirelessly • To optimize data collection by maximizing coverage and minimizing time taken Joint Annual Meeting NSF Division of Human Resource Development

  3. Why an Unmanned Aircraft System? • Provides Access to Remotely Sense Regions • Covers Large Areas Fairly Easily • Dirty Dull and Dangerous flying • Can be Utilized Frequently • Smaller Carbon Footprint when Compared to Other Craft and More Inexpensive Joint Annual Meeting NSF Division of Human Resource Development

  4. Unmanned Aerial Vehicle - Powered Paraglider Joint Annual Meeting NSF Division of Human Resource Development

  5. Unmanned Aircraft System (UAS) • Onboard • Flight Telemetry @ 900 MHz • Environmental Science Data - @ 2.4 GHz • Ground Station • Laptop collects wireless data • Custom developed C# software displays data Joint Annual Meeting NSF Division of Human Resource Development

  6. UAV In Flight – UAV Movie 9625 Joint Annual Meeting NSF Division of Human Resource Development

  7. UAV Landing – UAV Movie 9627 Joint Annual Meeting NSF Division of Human Resource Development

  8. Ground Station Software Screenshot Joint Annual Meeting NSF Division of Human Resource Development

  9. Area Coverage Tool • Ground Station Software Needs • Visualization of data collection while flying • Evaluation of how effective data collection is while flying • Display an “optimal path” for users to take Joint Annual Meeting NSF Division of Human Resource Development

  10. Need for an Optimal Trajectory Complete coverage of a given area with senor readings • UAVs have limited flight time. • All sensors do not record at the same speed or at the same rate • Design a trajectory to account for these issues Joint Annual Meeting NSF Division of Human Resource Development

  11. Corner Coverage Is Not Complete Joint Annual Meeting NSF Division of Human Resource Development

  12. Solution – Fishtail Pathing Joint Annual Meeting NSF Division of Human Resource Development

  13. Sensor Readings and Area Coverage • Evaluation of Our Technique • While mathematically sound the fishtail solution is not practical while flying • Wind and weather conditions for the day can effect flight patterns • Areas inside the flight zone that have high levels of change need to be flown over more Joint Annual Meeting NSF Division of Human Resource Development

  14. Very Heterogeneous Areas Require More Flight Coverage Joint Annual Meeting NSF Division of Human Resource Development

  15. Future Directions • Designing a Smarter Tool • Improving upon existing design by including optimal pathing • Drawing an optimal path based on data already collected • Testing in field to verify improvement in design Joint Annual Meeting NSF Division of Human Resource Development

  16. Further Questions? • Jerald Brady -GraduateStudent- jerald.brady@gmail.com • Vladik Kreinovich –CS Researcher- vladik@utep.edu • Craig Tweedie – ES Researcher - ctweedie@utep.edu • Rodrigo Romero –Presenter- raromero2@utep.edu Joint Annual Meeting NSF Division of Human Resource Development

More Related