Autonomous navigation system for flapping wing aerial vehicle based on event-trigger planner

Flapping Wing Aerial Vehicles (FWAVs) hold immense potential for applications such as search-and-rescue missions in complex terrains, environmental monitoring in hazardous areas, and exploration in confined spaces. However, their adoption is hindered by the challenges of autonomous navigation in unknown environments, exacerbated by their limited onboard computational resources and demanding flight dynamics. This work addresses these challenges by presenting a lightweight, vision-based autonomous navigation system weighing 26.0 g, enabling FWAVs to achieve obstacle-avoidance flight at a speed of 9.0 m/s. Central to this system is a novel end-to-end Bi-level Cooperative Policy (BCP) that significantly improves flight efficiency and safety. BCP employs lightweight neural networks for real-time performance and leverages Hierarchical Reinforcement Learning (HRL) for robust and efficient training. Quantitative evaluations show that BCP achieves up to 6.5% shorter path lengths, 11.2% faster task completion time, and improved explainability compared to state-of-the-art reinforcement learning algorithms. Additionally, BCP demonstrates 35.7% more efficient and stable training, reducing computational overhead while maintaining high performance. The system design incorporates optimized lightweight components, including a 4.0 g customized stereo camera, a 6.0 g 3D-printed camera mount, and a 16.0 g onboard computer, all tailored to FWAV applications. Real-flight experiments validate the sim-to-real transferability of the proposed navigation system, demonstrating its readiness for real-world deployment in challenging scenarios. This research advances the practicality of FWAVs, paving the way for their broader adoption in critical missions where compact, agile aerial robots are indispensable.