Optimal Elastic Wing for Flapping-Wing Robots Through Passive Morphing

Abstract

Flapping wing robots show promise as platforms for safe and efficient flight in near-human operations, thanks to their ability to agile maneuver or perch at a low Reynolds number. The growing trend in the automatization of these robots has to go hand in hand with an increase in the payload capacity. This work provides a new passive morphing wing prototype to increase the payload of this type of UAV. The prototype is based on a biased elastic joint and the holistic research also includes the modelling, simulation and optimization scheme, thus allowing to adapt the prototype for any flapping wing robot. This model has been validated through flight experiments on the available platform, and it has also been demonstrated that the morphing prototype can increase the lift of the robot under study by up to 16% in real flight and 10% of estimated consumption reduction.

A Review of Flapping Mechanisms for Avian-Inspired Flapping-Wing Air Vehicles

“…Passive * Rigid mechanism [24,58,68,69] Compliant mechanism [71] Active ** Rigid mechanism [59,84] Compliant mechanism [60,[72][73][74]…”

A Bibliometric Analysis of Flapping Wing Instrumentation

“…This can include laser displacement sensors to measure the wing position [69,70] or the position of fixed points in the system such as a slider or the actuator itself [42,44,71], which can also be measured using infra-red [64]. Alternatively, particle image velocimetry can be used to measure the airflow over the wing [73] or a motion capture system to record the motion of aspects of the system during flight [74].…”

Small target detection algorithm for flapping wing UAV based on improved YOLOv8

“…As shown in Figure 1, it is a Common flapping wing UAV. Currently, the mainstream UAV small target detection algorithms are deployed on rotor UAVs, with few detection algorithms deployed on flapping wing UAVs [2] . This highlights the effectiveness of the approach proposed in this paper, which involves increasing the small target detection layer and introducing the MHSA mechanism to improve the accuracy (mAP) of the target detection model on the testset.…”