Onboard Sensing, Flight Control, and Navigation of a Dual-Motor Hummingbird-Scale Flapping Wing Robot

Zhan Tu, Purdue University

Abstract

Insects and hummingbirds not only can perform long-term stationary hovering but also are capable of acrobatic maneuvers. At their body scale, such extraordinary flight performance remains unmatched by state-of-the-art conventional man-made aerial vehicles with fixed or rotary wings. Insects’ and hummingbirds’ near maximal performance come from their highly intricate and powerful wing-thorax actuation systems, sophisticated sensory system, and precise neuromotor control. Flapping Wing Micro Air Vehicles (FWMAVs) with bio-inspired flapping flight mechanisms hold great promise in matching the performance gap of their natural counterparts. Developing such autonomous flapping-wing vehicles to achieve animal-like flight, however, is challenging. The difficulties are mainly from the high power density requirements under the stringent constraints of scale, weight, and power, severe system oscillations induced by high-frequency wing motion, high nonlinearity of the system, and lack of miniature navigation sensors, which impede actuation system design, onboard sensing, flight control, and autonomous navigation. To address these open issues, in this thesis, we first introduce systematic modeling of a dual-motor hummingbird-scale flapping wing robot. Based upon it, we then present studies of the onboard sensor fusion, flight control, and navigation method. By taking the key inspiration from its natural counterparts, the proposed hummingbird robot has a pair of independently controlled wings. Each wing is directly actuated by a dc motor. Motors undergo reciprocating motion. Such a design is a severely underactuated system, namely, it relies on only two actuators (one per wing) to control full six degrees of freedom body motion. As a bio-inspired design, it also requires the vehicle close to its natural counterparts’ size and weight meanwhile provide sufficient lift and control effort for autonomy. Due to stringent payload limitation from severe underactuation and power efficiency challenges caused by motor reciprocating motion, the design and integration of such a system is a challenging task. In this thesis, we present the detailed modeling, optimization, and system integration of onboard power, actuation, sensing, and flight control to address these unique challenges. As a result, we successfully prototyped such dual-motor powered hummingbird robot, either with power tethers or fully untethered. The tethered platform is used for designing onboard sensing, control, and navigation algorithms. Untethered design tackles system optimization and integration challenges. Both tethered/untethered versions demonstrate sustained stable flight. For onboard attitude sensing, a real-time sensor fusion algorithm is proposed with model-based adaptive compensation for both sensor reading drift and wing motion induced severe system vibration. With accurate and robust sensing results, a nonlinear robust control law is designed to stabilize the system during flight. Stable hovering and waypoint tracking flight were experimentally conducted to demonstrate the control performance. In order to achieve natural flyers’ acrobatic maneuverability, we propose a hybrid control scheme by combining a model-based robust controller with a model-free reinforcement learning maneuver policy to perform aggressive maneuvers. The model-based control is responsible for stabilizing the robot in nominal flight scenarios. The reinforcement learning policy pushes the flight envelope to pilot fierce maneuvers. To demonstrate the effectiveness of the proposed control method, we experimentally show animal-like tight flip maneuver on the proposed hummingbird robot, which is actuated by only two DC motors. These successful results show the promise of such a hybrid control design on severely underactuated systems to achieve high-performance flight.

Degree

Ph.D.

Advisors

Deng, Purdue University.

Subject Area

Design|Robotics|Transportation

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