Online Trajectory Replanning for Avoiding Moving Obstacles Using Fusion Prediction and Gradient-Based Optimization

Autor: Qianyi Fu, Wenjie Zhao, Shiyu Fang, Yiwen Zhu, Jun Li, Qili Chen
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Applied Sciences, Vol 14, Iss 18, p 8339 (2024)
Druh dokumentu: article
ISSN: 2076-3417
DOI: 10.3390/app14188339
Popis: In this study, we introduce a novel method for an online trajectory replanning approach for fixed-wing Unmanned Aerial Vehicles (UAVs). Our method integrates moving obstacle predictions within a gradient-based optimization framework. The trajectory is represented by uniformly discretized waypoints, which serve as the optimization variables within the cost function. This cost function incorporates multiple objectives, including obstacle avoidance, kinematic and dynamic feasibility, similarity to the reference trajectory, and trajectory smoothness. To enhance prediction accuracy, we combine physics-based and pattern-based methods for predicting obstacle movements. These predicted movements are then integrated into the online trajectory replanning framework, significantly enhancing the system’s safety. Our approach provides a robust solution for navigating dynamic environments, ensuring both optimal and secure UAV operation.
Databáze: Directory of Open Access Journals