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A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning.

Thai Binh Nguyen1, Manzur Murshed2, Tanveer Choudhury1

  • 1Institute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, Australia.

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Summary
This summary is machine-generated.

This study introduces a novel depth-based method for generating safe flight corridors, crucial for autonomous navigation. The approach ensures collision-free paths with minimal overlap, enhancing drone safety in complex environments.

Keywords:
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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Autonomous systems require reliable navigation in unknown environments.
  • Local planning often relies on pre-existing maps, limiting real-time adaptability.
  • Generating safe flight corridors is essential for collision avoidance.

Purpose of the Study:

  • To develop a depth-based hybrid method for generating safe flight corridors.
  • To enable memoryless local navigation planners to operate effectively.
  • To ensure collision-free corridors with minimal overlap for path planning.

Main Methods:

  • Utilizing raw depth images as direct input for a learning-based object-detection engine.
  • Employing an object-detection network to predict polyhedral safe corridors.
  • Implementing a verification procedure to guarantee collision-free corridors.
  • Minimizing corridor overlap, achieving an average Intersection over Union (IoU) below 2%.

Main Results:

  • Successful integration into a memoryless planner with a straight-line path-planning algorithm.
  • High success rates demonstrated in both synthetic and real-world obstacle-dense environments.
  • The method effectively produces separate, collision-free safe corridors.

Conclusions:

  • The proposed depth-based hybrid method is highly capable for generating safe corridors for memoryless local planning.
  • The approach eliminates the need for map fusion, enabling real-time navigation.
  • This technique significantly enhances the safety and efficiency of autonomous flight in complex scenarios.