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AlphaPilot: autonomous drone racing
Philipp Foehn1, Dario Brescianini1, Elia Kaufmann1
1Dep. of Informatics, Dep. of Neuroinformatics, University of Zurich and ETH, Zurich, Switzerland.
Autonomous Robots
|February 28, 2022
Summary
This study introduces a new autonomous drone racing system using vision, advanced filtering, and path planning. It achieved high speeds and a second-place finish in the 2019 AlphaPilot Challenge.
Area of Science:
- Robotics
- Computer Vision
- Artificial Intelligence
Background:
- Traditional drone racing systems rely on detecting only the next gate.
- This limits navigation capabilities and real-time path optimization.
Purpose of the Study:
- To develop an autonomous, vision-based drone racing system.
- To enable navigation using multiple gate detections and global mapping.
- To achieve time-optimal trajectory planning for enhanced performance.
Main Methods:
- Learned data abstraction for processing visual information.
- Nonlinear filtering for state estimation and drift compensation.
- Time-optimal trajectory planning based on approximate drone dynamics.
Main Results:
- The system successfully navigated complex race courses at high speeds.
- It utilized multiple, simultaneous gate detections for robust state estimation.
- The system placed second in the 2019 AlphaPilot Challenge.
Conclusions:
- The novel system demonstrates effective autonomous drone racing capabilities.
- Global gate mapping and drift-compensated state estimation are key innovations.
- The approach enables real-time, near time-optimal path planning.
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