Bumblebees Exhibit Adaptive Flapping Responses to Air Disturbances
Insects excel in trajectory and attitude handling during flight, yet the specific kinematic behaviours they use for maintaining stability in air disturbances are not fully understood. This study investigates the adaptive strategies of bumblebees when exposed to gust disturbances directed from three different angles within a plane cross-sectional to their flight path. By analyzing characteristic wing motions during gust traversal, we aim to uncover the mechanisms that enable bumblebees to maintain control in unsteady environments. We utilised high-speed cameras to capture detailed flight paths, allowing us to extract dynamic information. Our results reveal that bees make differential bilateral kinematic adjustments based on gust direction: sideward gusts elicit posterior shifts in the wing closest to the gust, while upward gusts trigger coordinated posterior shifts in both wings. Downward gusts prompted broader flapping and increased flapping frequencies, along with variations in flap timing and sweep angle. Stroke sweep angle was a primary factor influencing recovery responses, coupled with motion around the flap axis. The adaptive behaviours strategically position the wings to optimize gust reception and enhance wing-generated forces. These strategies can be distilled into specific behavioural patterns for analytical modelling to inform the design of robotic flyers. We observed a characteristic posterior shift of wings when particular counteractive manoeuvres were required. This adjustment reduced the portion of the stroke during which the wing receiving gust forces was positioned in front of the centre of gravity, potentially enhancing manoeuvrability and enabling more effective recovery manoeuvres. These findings deepen our understanding of insect flight dynamics and offer promising strategies for enhancing the stability and manoeuvrability of MAVs in turbulent environments.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Model-Reference Adaptive Flight Control of a 95-mg Insect-Scale Flapping-Wing Aerial Robot
Due to the system's scale and complex fabrication, the model describing the dynamics of a flapping-wing insect-scale aerial robot is subject to parameter uncertainty; for example, in the inertia matrix and the actuator m…
A Simulation Platform for Flapping-Wing Vehicles
Flapping-wing aerial vehicles (FWAVs) demonstrate remarkable agility but face substantial autonomy challenges due to their high sensitivity to aerodynamic disturbances and limited sensor payload capacity. Current simulat…
Point CloudsPeriodic Proprioceptive Stimuli Learning and Internal Model Development for Avian-inspired Flapping-wing Flight State Estimation
This paper presents a novel learning-based approach for online state estimation in flapping wing aerial vehicles (FWAVs). Leveraging low-cost Magnetic, Angular Rate, and Gravity (MARG) sensors, the proposed method effect…
State EstimationPerch like a bird: bio-inspired optimal maneuvers and nonlinear control for Flapping-Wing Unmanned Aerial Vehicles
This research endeavors to design the perching maneuver and control in ornithopter robots. By analyzing the dynamic interplay between the robot's flight dynamics, feedback loops, and the environmental constraints, we aim…
Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning Study
A formal autism diagnosis can be an inefficient and lengthy process. Families may wait months or longer before receiving a diagnosis for their child despite evidence that earlier intervention leads to better treatment ou…
Action DetectionActivity DetectionActivity RecognitionAutism detection+1