Development of vision-based drones for obstacle avoidance
Drones, commonly known as unmanned aerial vehicles (UAVs), are a type of aircraft which is operated without the assistance of any human pilot on board. Drones have revolutionized a wide range of industries, from agriculture and logistics to environmental monitoring and emergency response. The saf...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166795 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Drones, commonly known as unmanned aerial vehicles (UAVs), are a type of aircraft which is
operated without the assistance of any human pilot on board. Drones have revolutionized a
wide range of industries, from agriculture and logistics to environmental monitoring and
emergency response. The safe and autonomous navigation of drones is paramount to maximize
their potential and reduce risks associated with collisions and accidents. This project will focus
on the development of a vision-based drone for obstacle avoidance, thereby, addressing this
crucial aspect of drone operation.
The proposed system in this project harnesses the power of computer vision and deep learning
techniques to enable drones to perceive their environment and adapt their flight trajectory
accordingly. The author has made use of computer vision to detect and classify various types
of obstacles from camera feeds in real-time and the algorithm is further enhanced by
incorporating motion estimation and object tracking to avoid potential threats in dynamic
environments.
This vision-based drone system significantly improves the overall safety, reliability, and
autonomy of drone operations, allowing for seamless integration into an ever-growing range
of applications. This advancement is particularly important for operations in complex and
cluttered environments, such as urban settings and disaster-stricken areas, where conventional
ground position systems (GPS) and sensor-based navigation systems may not be sufficient or
reliable.
The algorithm used in this project has displayed positive results in various simulation
environments, thereby showcasing its effectiveness and robustness. |
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