Trajectory optimization and data-driven modeling for robotic bat flapping flight

biologically inspired robots;aerial vehicles;underactuated robots;flapping flight;trajectory planning;aerodynamics;dynamics;data-driven modeling;parameter estimation;optimization

Hoff, Jonathan E.

关键词: biologically inspired robots;    aerial vehicles;    underactuated robots;    flapping flight;    trajectory planning;    aerodynamics;    dynamics;    data-driven modeling;    parameter estimation;    optimization;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/107903/HOFF-DISSERTATION-2020.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Planning flight trajectories is essential for practical application of flying systems. This topic has been well studied for fixed and rotary winged aerial vehicles, but far fewer works have explored it for flapping systems. Trajectory planning requires a model that is both accurate and computationally inexpensive, and this is difficult for flapping systems with complex aerodynamic properties. There have been significant efforts in creating both analytical and data-driven models for many of these types of vehicles including ornithopters and small aerial vehicles mimicking insects. However, very few works have explored modeling for aerial vehicles with a skeletal structure throughout the wings and a single flexible membrane that covers the wings and tail such as is found in robots with bat morphology. In this dissertation, we build upon previous efforts to model a robotic bat and present a methodology for using a combination of first-principles and data-driven tools. We record a series of load cell tests and free flight experiments, and we optimize the model parameters to improve long-term flight prediction. We introduce several extra terms in the model including a term explaining the coupling between wings and tail in order to maximize the effectiveness of collected flight data. The result is a model that performs well in prediction for a range of different tail actuator configurations as demonstrated by our flight results using a bat robot. We present a generalized approach that uses this model with direct collocation methods to plan dynamically feasible flight maneuvers. We demonstrate a range of different maneuvers including a launch to an altitude, a banked turn, and a launch, dive, and recover maneuver. The launch to an altitude and launch, dive, and recover maneuvers are validated with free-flight experiments.