Line Following, Obstacle Avoidance, and Flight Stability in Autonomous Quadrotor Drones
School Name
South Carolina Governor's School for Science & Mathematics
Grade Level
12th Grade
Presentation Topic
Computer Science
Presentation Type
Mentored
Abstract
The purpose behind this research is to advance the way that drones detect and move around objects, follow lines, and remain stable in their flight. The underlying reason for this is to make autonomous drone deliveries and pickups for online shopping companies such as eBay and Amazon more reliable and safer. This way, thirty minute and one-hour deliveries will become a possibility as they become safer and legal. The methodology for this is to write algorithms and python programs to accomplish each of these individual tasks. Then these algorithms are tested in a secure flight space for stability. As problems in these algorithms are detected, they are recorded and corrected through plentiful fine-tuning. This is done through flight logs recorded in Queue Ground Control, an Ubuntu program. The results of this were inconsistent, sometimes with the drone crashing into ceiling of the flight space, and sometimes with the drone completing the whole course in under a minute. The main issues are in altitude control when avoiding obstacles. However, things such as AR Tag detection have proven to be consistently successful. Line detection is only an issue at sharp turns of 90 degrees or more. The flight stability is very consistent, and the drone maintains a stable altitude as it hovers in a horizontal plane.
Recommended Citation
Bain, William, "Line Following, Obstacle Avoidance, and Flight Stability in Autonomous Quadrotor Drones" (2019). South Carolina Junior Academy of Science. 124.
https://scholarexchange.furman.edu/scjas/2019/all/124
Location
Founders Hall 140 A
Start Date
3-30-2019 8:30 AM
Presentation Format
Oral Only
Group Project
No
Line Following, Obstacle Avoidance, and Flight Stability in Autonomous Quadrotor Drones
Founders Hall 140 A
The purpose behind this research is to advance the way that drones detect and move around objects, follow lines, and remain stable in their flight. The underlying reason for this is to make autonomous drone deliveries and pickups for online shopping companies such as eBay and Amazon more reliable and safer. This way, thirty minute and one-hour deliveries will become a possibility as they become safer and legal. The methodology for this is to write algorithms and python programs to accomplish each of these individual tasks. Then these algorithms are tested in a secure flight space for stability. As problems in these algorithms are detected, they are recorded and corrected through plentiful fine-tuning. This is done through flight logs recorded in Queue Ground Control, an Ubuntu program. The results of this were inconsistent, sometimes with the drone crashing into ceiling of the flight space, and sometimes with the drone completing the whole course in under a minute. The main issues are in altitude control when avoiding obstacles. However, things such as AR Tag detection have proven to be consistently successful. Line detection is only an issue at sharp turns of 90 degrees or more. The flight stability is very consistent, and the drone maintains a stable altitude as it hovers in a horizontal plane.
