Experimental Evaluation of Safe Trajectory Planning for an Omnidirectional UAV

Abstract

Autonomous aerial vehicles play a critical role in search and rescue operations, where navigation through cluttered and confined environments is essential. To this end, this paper presents a novel trajectory planning framework for omnidirectional drones that dynamically adjusts tracking velocity based on the platform’s proximity to obstacles, ensuring a balance between safety and efficiency in cluttered and challenging environments. The proposed approach generates a geometric path to the target location. At each waypoint, the minimum distance between the drone’s convex hull and surrounding obstacles is determined, allowing the computation of the velocity constraints. By slowing down near obstacles and accelerating in open spaces, the method enhances both safety and maneuverability. The framework is validated through real-world experiments using the OmniOcta UAV, demonstrating its ability to navigate through constrained spaces. Furthermore, we present an experimental study to investigate key sources of tracking deviations, including propeller dynamics and aerodynamic interactions near obstacles.