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UAV Aeromagnetic Path Planning in Complex Terrain Based on a Q-Learning-Assisted Multi-Strategy Starfish Optimization

Sihan Yuan¹, Zhipeng Li¹, Junjie Zhang¹

¹School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China.

Biomimetics (Basel, Switzerland)

|May 26, 2026

Summary

This summary is machine-generated.

A new Q-learning-assisted algorithm enhances unmanned aerial vehicle (UAV) path planning for aeromagnetic surveys. This intelligent method improves efficiency and safety in complex terrains, reducing costs and data variability.

Keywords:

Q-learning UAV aeromagnetic survey metaheuristic algorithm path planning

Related Experiment Videos

Area of Science:

Geophysics
Robotics
Artificial Intelligence

Background:

High-quality aeromagnetic data requires low-altitude, terrain-following flights by unmanned aerial vehicles (UAVs).
Efficient and safe path planning for UAVs in complex terrains is a significant challenge.

Purpose of the Study:

To develop an intelligent algorithm for optimizing offline path planning in UAV aeromagnetic surveys.
To enhance path planning efficiency, safety, and data quality in challenging environments.

Main Methods:

Proposed a Q-learning-assisted Multi-Strategy Starfish Optimization Algorithm (QMSFOA).
Integrated Sobol sequence and Refraction Opposition-based Learning for population initialization.
Employed a hybrid adaptive differential mutation mechanism and Q-learning for mode scheduling.
Introduced an adaptive t-distribution elite perturbation strategy for accuracy refinement.

Main Results:

QMSFOA demonstrated superior performance on the CEC-2022 benchmark suite, achieving best convergence accuracy on nine functions.
Simulations showed QMSFOA paths satisfied kinematic and obstacle avoidance constraints in complex 3D terrains.
Path costs were reduced by approximately 25% compared to the standard Starfish Optimization Algorithm (SFOA).
Standard deviation of results was reduced by one to two orders of magnitude.

Conclusions:

The QMSFOA provides an efficient, reliable, and intelligent solution for high-precision UAV geophysical exploration.
The proposed method significantly improves path planning for aeromagnetic surveys in complex environments.
QMSFOA enhances data acquisition quality and operational safety for UAVs.