An Efficient Multimodal Nature-Inspired Unmanned Aerial Vehicle Capable of Agile Maneuvers

Herein, the development of a multimodal, nature-inspired unmanned aerial vehicle (UAV) that operates in three different flight regimes (rotor wing, tailsitter, cruise), unlike typical UAVs which only consist of two, is discussed. The platform uses a nature-inspired method to achieve efficient hover, yet possesses the flexibility to enter a more agile state via additional tailsitter and cruise modes. Both the mechanical configuration and software/control architecture used to achieve three-mode capability are documented in detail. A sigmoid blending control is implemented as the transition control strategy, consisting of transition coordinators that adjust the weight of each individual controller on the actuator outputs. To improve the transition sequence based on performance goals such as reduced altitude variation and throttle usage, an optimization routine is performed to obtain the optimized blending parameters. The optimized parameters are then experimentally verified on a physical prototype in lab conditions and shown to exhibit at least a 20% improvement for altitude variation and at least 5% less throttle usage across 4 consecutive transitions compared with baseline. Three-mode capability outdoors in real-world conditions are also demonstrated in multiple flights. Power draw in rotor-wing mode is found to be 30% less than in tailsitter mode.

Automated summary

This study discusses the development of a multimodal, nature-inspired unmanned aerial vehicle (UAV) that can operate in three different flight modes. The UAV achieves efficient hover through a nature-inspired method and has the flexibility to enter more agile states using additional modes. The study documents the mechanical configuration and software/control architecture used to enable the three-mode capability. A sigmoid blending control is implemented for transition control, and an optimization routine is performed to improve the transition sequence based on performance goals. The optimized parameters are experimentally verified and shown to improve altitude variation and throttle usage compared to baseline.