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Related Experiment Video

Updated: May 31, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

CRGWO-DWA: A structured global-local collaborative planning framework for smooth UAV navigation in dynamic

Yong He¹, Qiang Gao², Qing Huang²

¹School of Artificial Intelligence, Changsha University of Science and Technology, Changsha, 410114, Hunan, China. 003356@csust.edu.cn.

Scientific Reports

|May 29, 2026

Summary

This study introduces CRGWO-DWA, a novel framework for Unmanned Aerial Vehicle (UAV) path planning in dynamic environments. It ensures safer, smoother navigation by enhancing global pathfinding and local obstacle avoidance.

Keywords:

dynamic obstacle avoidance dynamic window approach grey wolf optimizer path planning unmanned aerial vehicle

Related Experiment Videos

Last Updated: May 31, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

Area of Science:

Robotics and Control Systems
Artificial Intelligence and Optimization Algorithms
Aerospace Engineering and Navigation

Background:

Unmanned Aerial Vehicle (UAV) path planning in dynamic environments presents challenges in balancing global path quality, local obstacle avoidance, and motion smoothness.
Existing methods like Grey Wolf Optimizer (GWO) suffer from premature convergence and unsmooth paths, while the Dynamic Window Approach (DWA) exhibits local-optimum tendencies and evaluation imbalance.

Purpose of the Study:

To propose a structured global-local collaborative planning framework, CRGWO-DWA, to address limitations in UAV path planning.
To enhance population diversity, search performance, and path smoothness in the global planning stage.
To improve trajectory discrimination and responsiveness in the local planning stage for dynamic obstacle interference.

Main Methods:

Developed CRGWO (a modified GWO) featuring piecewise linear chaotic initialization, periodic control parameter regeneration, and adaptive leadership weighting for global path planning.
Employed cubic spline interpolation for generating smooth reference paths in the global stage.
Enhanced DWA with min-max normalized evaluation and redesigned weighting for improved local obstacle avoidance and responsiveness.

Main Results:

CRGWO demonstrated superior or tied performance across most benchmark metrics, reducing path length and redundant turns in static environments.
The framework produced more compact and smoother paths in 3D scenarios.
CRGWO-DWA enabled safe, smooth UAV navigation with stable velocity profiles in dynamic multi-obstacle environments.

Conclusions:

The CRGWO-DWA framework offers an effective solution for UAV path planning challenges in dynamic environments.
The proposed enhancements to GWO and DWA significantly improve path quality, safety, and navigation efficiency.
The collaborative global-local approach ensures robust performance under complex and changing environmental conditions.