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Updated: Oct 2, 2025

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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
Drone racing . Agile flight . Aerial vehicles

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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.