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A Photosensitivity-Enhanced Plant Growth Algorithm for UAV Path Planning.

Renjie Yang¹, Pan Huang^2,3, Hui Gao¹

¹School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China.

Biomimetics (Basel, Switzerland)

|April 26, 2024

Summary

This summary is machine-generated.

A new photosensitivity-enhanced plant growth algorithm (PEPG) improves UAV low-altitude penetration path planning. This method enhances safety and reduces path length by over 8.2% compared to traditional algorithms.

Keywords:

electronic chart heuristic information path planning plant growth unmanned aerial vehicle

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Area of Science:

Robotics and Autonomous Systems
Artificial Intelligence
Aerospace Engineering

Background:

Unmanned Aerial Vehicles (UAVs) are crucial for national security, necessitating secure low-altitude penetration capabilities.
Existing path planning algorithms struggle with complex marine environments and UAV kinematic constraints.

Purpose of the Study:

To develop an advanced path planning algorithm for UAVs operating at low altitudes in complex marine environments.
To enhance the safety, efficiency, and smoothness of UAV flight paths.

Main Methods:

Constructed a marine combat environment using S-57 electronic chart data.
Developed a photosensitivity-enhanced plant growth algorithm (PEPG) improving upon the plant growth path planning algorithm (PGPP).
Integrated UAV kinematic constraints, including turning angle, into the path planning process.

Main Results:

The PEPG algorithm generated smoother paths with a length reduction of at least 8.2% compared to the original PGPP.
Simulation tests demonstrated PEPG's superiority over A*, RRT, and GA algorithms in path length and quality.
Autonomous tracking flight tests verified the feasibility and safety of the planned paths.

Conclusions:

The PEPG algorithm offers a significant advancement in UAV low-altitude penetration path planning.
PEPG provides a more efficient, safer, and smoother flight path solution for UAVs in complex environments.
The algorithm's effectiveness is validated through simulations and real-world flight tests.