An adaptive spiking neural controller for flapping insect-scale robots

Clawson, Taylor S.; Stewart, Terrence C.; Eliasmith, Chris; Ferrari, Silvia

doi:10.1109/ssci.2017.8285173

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Equations (6-12) together constitute the equations of motion of the robot that are subject to the actuator dynamic constraints [Eqs. (14) and ( 15)]. They can be cast in standard form [Eq.…”

Section: B Standard Forms Of Fwmav Flight Dynamicsmentioning

confidence: 99%

“…The dominant mode analysis in Sec. VII provides a foundation for understanding stable and unstable flight conditions and, eventually, how unstable conditions may be stabilized by a feedback controller [14,83].…”

Section: Hovering Flapping Flightmentioning

confidence: 99%

“…Although many flapping flight controllers have been proposed in the literature [13][14][15][16][17][18][19][20][21][22][23][24], to date control designs have been demonstrated primarily for hovering regimes and symmetric longitudinal flight [24,25]. The reason is that modeling and analyzing the robot dynamics away from these regimes is made difficult by the nonlinear, periodic, and time-varying nature of asymmetric flapping flight [26].…”

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confidence: 99%

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Full Flight Envelope and Trim Map of Flapping-Wing Micro Aerial Vehicles

Clawson¹,

Ferrari²,

Helbling³

et al. 2020

Journal of Guidance, Control, and Dynamics

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Controlling agile and complex air-vehicle maneuvers requires knowledge of the full flight envelope and dominant modes of motion. This paper presents a comprehensive approach for determining the full flight envelope and trim map of minimally actuated flapping-wing micro aerial vehicles that are capable of a broad range of coupled longitudinal-lateral-directional aerobatic maneuvers. By this approach, a representative set of realizable set points and trim conditions can be determined from the flight dynamic model, including asymmetric and unstable maneuvers. Data-driven dynamic mode decomposition is used to identify and analyze the dominant modes of motion both in simulations and physical experiments involving the RoboBee. The dominant eigenplanes and stability characteristics of this flapping-wing robot are successfully validated experimentally for both stable and unstable asymmetric full-envelope maneuvers, including rapidly uncontrolled tumbling.

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Section: B Standard Forms Of Fwmav Flight Dynamicsmentioning

confidence: 99%

Section: Hovering Flapping Flightmentioning

confidence: 99%

mentioning

confidence: 99%

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Full Flight Envelope and Trim Map of Flapping-Wing Micro Aerial Vehicles

Clawson¹,

Ferrari²,

Helbling³

et al. 2020

Journal of Guidance, Control, and Dynamics

Self Cite

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A Relative Spike-Timing Approach to Kernel-Based Decoding Demonstrated for Insect Flight Experiments

Yang

Putney

Sikandar

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

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Gridbot

Tang

Michmizos

2018

Proceedings of the International Conference on Neuromorphic Systems

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It is true that the "best" neural network is not necessarily the one with the most "brain-like" behavior. Understanding biological intelligence, however, is a fundamental goal for several distinct disciplines. Translating our understanding of intelligence to machines is a fundamental problem in robotics. Propelled by new advancements in Neuroscience, we developed a spiking neural network (SNN) that draws from mounting experimental evidence that a number of individual neurons is associated with spatial navigation. By following the brain's structure, our model assumes no initial all-to-all connectivity, which could inhibit its translation to a neuromorphic hardware, and learns an uncharted territory by mapping its identified components into a limited number of neural representations, through spike-timing dependent plasticity (STDP). In our ongoing effort to employ a bioinspired SNN-controlled robot to real-world spatial mapping applications, we demonstrate here how an SNN may robustly control an autonomous robot in mapping and exploring an unknown environment, while compensating for its own intrinsic hardware imperfections, such as partial or total loss of visual input.

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An adaptive spiking neural controller for flapping insect-scale robots

Cited by 4 publications

References 23 publications

Full Flight Envelope and Trim Map of Flapping-Wing Micro Aerial Vehicles

Full Flight Envelope and Trim Map of Flapping-Wing Micro Aerial Vehicles

A Relative Spike-Timing Approach to Kernel-Based Decoding Demonstrated for Insect Flight Experiments

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