SUMMARYMoving animals orchestrate myriad motor systems in response to multimodal sensory inputs. Coordinating movement is particularly challenging in flight control, where animals deal with potential instability and multiple degrees of freedom of movement. Prior studies have focused on wings as the primary flight control structures, for which changes in angle of attack or shape are used to modulate lift and drag forces. However, other actuators that may impact flight performance are reflexively activated during flight. We investigated the visual-abdominal reflex displayed by the hawkmoth Manduca sexta to determine its role in flight control. We measured the open-loop stimulus-response characteristics (measured as a transfer function) between the visual stimulus and abdominal response in tethered moths. The transfer function reveals a 41ms delay and a high-pass filter behavior with a pass band starting at ~0.5Hz. We also developed a simplified mathematical model of hovering flight wherein articulation of the thoracic-abdominal joint redirects an average lift force provided by the wings. We show that control of the joint, subject to a high-pass filter, is sufficient to maintain stable hovering, but with a slim stability margin. Our experiments and models suggest a novel mechanism by which articulation of the body or ʻairframeʼ of an animal can be used to redirect lift forces for effective flight control. Furthermore, the small stability margin may increase flight agility by easing the transition from stable flight to a more maneuverable, unstable regime.
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This paper considers the design of motion control algorithms f o r robot fish. W e present modeling, control design, and experimental trajectory tracking results for an experimental planar robotic fish system that is propelled using carangiform-like locomotion. Our model for the fish's propulsion is based o n quasi-steady fluid flow. Using this model, we propose gaits for forward and turning trajectories and analyze system response under such control strategies. Our models and predictions are verified by experiment.
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