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Reinforcement Learning enabled hummingbird-like extreme maneuvers of a dual-motor at-scale flapping wing robot
thesisposted on 31.01.2022, 15:28 authored by Fan FeiFan Fei
Insects and hummingbirds exhibit extraordinary flight capabilities and can simultaneously master seemingly conflicting goals: stable hovering and aggressive maneuvering, unmatched by small-scale man-made vehicles. Given a sudden looming visual stimulus at hover, a hummingbird initiates a fast backward translation coupled with a 180-degree yaw turn, which is followed by instant posture stabilization in just under 10 wingbeats. Considering the wingbeat frequency of 40Hz, this aggressive maneuver is accomplished in just 0.2 seconds. Flapping Wing Micro Air Vehicles (FWMAVs) hold great promise for closing this performance gap given its agility. However, the design and control of such systems remain challenging due to various constraints.
First, the design, optimization and system integration of a high performance at-scale biologically inspired tail-less hummingbird robot is presented. Designing such an FWMAV is a challenging task under the constraints of size, weight, power, and actuation limitations. It is even more challenging to design such a vehicle with independently controlled wings equipped with a total of only two actuators and be able to achieve animal-like flight performance. The detailed systematic solution for the design is presented, including system modeling and analysis of the wing-actuation system, body dynamics, and control and sensing requirements. Optimization is conducted to search for the optimal system parameters, and a hummingbird robot is built and validated experimentally.
An open-source high fidelity dynamic simulation for FWMAVs is developed to serve as a testbed for the onboard sensing and flight control algorithm, as well as design, and optimization of FWMAVs. For simulation validation, the hummingbird robot was recreated in the simulation. System identification was performed to obtain the dynamics parameters. The force generation, open-loop and closed-loop dynamic response between simulated and experimental flights were compared and validated. The unsteady aerodynamics and the highly nonlinear flight dynamics present challenging control problems for conventional and learning control algorithms such as Reinforcement Learning.
For robust transient and steady-state flight performance, a robust adaptive controller is developed to achieve stable hovering and fast maneuvering. The model-based nonlinear controller can stabilize the system and adapt to system parameter changes such as wear and tear, thermo effect on the actuator or strong disturbance such as ground effect. The controller is tuned in simulation and experimentally verified by hovering, point-to-point fast traversing, and following by rapid figure-of-eight trajectory. The experimental result demonstrates the state-of-the-art performance of the FWMAV in stationary hovering and fast trajectory tracking tasks, with minimum transient and steady-state error.
To achieve animal level maneuvering performance, especially the hummingbirds' near-maximal performance during rapid escape maneuvers, we developed a hybrid flight control strategy for aggressive maneuvers. The proposed hybrid control policy combines model-based nonlinear control with model-free reinforcement learning. The model-based nonlinear control stabilizes the system's closed-loop dynamics under disturbance and parameter variation. With the stabilized system, a model-free reinforcement learning policy trained in simulation can be optimized to achieve the desirable fast movement by temporarily "destabilizing" the system during flight. Two test cases were demonstrated to show the effectiveness of the hybrid control method: 1)a rapid escape maneuver observed in real hummingbird, 2) a drift-free fast 360-degree body flip. Direct simulation-to-real transfers are achieved, demonstrating the hummingbird-like fast evasive maneuvers on the at-scale hummingbird robot.