Collision-Aware Navigation for Flapping-Wing Aerial Robots in Dense Environments
Rui Chen, Qian Chen, Tiefeng Li · 2024
Executing flight missions in complex and unknown environments poses a significant challenge for visually limited Flapping Wing Micro Air Vehicles (FWMAVs). The difficulties are primarily manifested in two ways: Firstly, the dynamic constraints of the FWMAV impose curvature constraints on its flight trajectory. Secondly, perceiving obstacles and achieving obstacle avoidance under visually limited conditions is highly challenging. In this study, we initially introduce an enhanced version of the Rapidly-exploring Random Tree (RRT) algorithm. This refined approach to path planning significantly mitigates the jitter experienced during the trajectory tracking of FWMAVs. Subsequently, we present a novel re-planning strategy, which empowers the aircraft to autonomously navigate through complex and unknown environments, relying exclusively on touch sensors affixed to its wings, which has been verified through simulation. This strategy offers a viable alternative approach in scenarios where visual perception capabilities are constrained or limited.