Reactive Trajectory Generation of Unmanned Aerial Vehicle Incorporating Fuzzy C-Means Clustering and Optimization Problem-Based Guidance

Abstract

This study introduces a reactive trajectory generation framework designed for navigating a hexacopter through environments with multiple obstacles. The algorithm uses the obstacle data acquired by a LiDAR sensor mounted on the UAV to dynamically generate trajectories in real-time. As the UAV continuously acquires obstacle data during the flight, spherical bounding boxes are created for identifying the safe navigational spaces, thereby effectively reducing collision risks. Moreover, an interior point algorithm is employed to determine the aiming points on these bounding boxes, which optimizes the trajectory generation. Additionally, the framework incorporates a Fuzzy c-means clustering algorithm, which enables the UAV to dynamically detect and maneuver around multiple obstacles. The effectiveness and robustness of the proposed algorithm are rigorously tested through single-obstacle scenarios and extensive Monte Carlo simulations, which confirmed its viability in environments with multiple obstacles.

Keywords

• Bounding box
• collision avoidance
• fuzzy c-means clustering
• optimization problem
• reactive trajectory generation
• unmanned aerial vehicle