scholarly journals Autonomous Flying With Neuromorphic Sensing

2021 ◽  
Vol 15 ◽  
Author(s):  
Patricia P. Parlevliet ◽  
Andrey Kanaev ◽  
Chou P. Hung ◽  
Andreas Schweiger ◽  
Frederick D. Gregory ◽  
...  
Keyword(s):  
Flight Control ◽  
Information Acquisition ◽  
Research Effort ◽  
Autonomous Systems ◽  
Flight Behavior ◽  
Bird Flight ◽  
Autonomous Flight ◽  
Operational Capabilities ◽  
Systems Learning ◽  
Complex Models

Autonomous flight for large aircraft appears to be within our reach. However, launching autonomous systems for everyday missions still requires an immense interdisciplinary research effort supported by pointed policies and funding. We believe that concerted endeavors in the fields of neuroscience, mathematics, sensor physics, robotics, and computer science are needed to address remaining crucial scientific challenges. In this paper, we argue for a bio-inspired approach to solve autonomous flying challenges, outline the frontier of sensing, data processing, and flight control within a neuromorphic paradigm, and chart directions of research needed to achieve operational capabilities comparable to those we observe in nature. One central problem of neuromorphic computing is learning. In biological systems, learning is achieved by adaptive and relativistic information acquisition characterized by near-continuous information retrieval with variable rates and sparsity. This results in both energy and computational resource savings being an inspiration for autonomous systems. We consider pertinent features of insect, bat and bird flight behavior as examples to address various vital aspects of autonomous flight. Insects exhibit sophisticated flight dynamics with comparatively reduced complexity of the brain. They represent excellent objects for the study of navigation and flight control. Bats and birds enable more complex models of attention and point to the importance of active sensing for conducting more complex missions. The implementation of neuromorphic paradigms for autonomous flight will require fundamental changes in both traditional hardware and software. We provide recommendations for sensor hardware and processing algorithm development to enable energy efficient and computationally effective flight control.

Simplified GPS model for UAV fault tolerant control laws design

2015 ◽  
Vol 3 (1) ◽  
pp. 39-60 ◽  
Author(s):  
Ghassan Al-Sinbol ◽  
Mario G Perhinschi ◽  
Brenton K Wilburn
Keyword(s):  
Flight Control ◽  
Fault Tolerant ◽  
Research Effort ◽  
Fault Tolerant Control ◽  
West Virginia University ◽  
Control Laws ◽  
Content Type ◽  
Autonomous Flight ◽  
Operational Conditions ◽  
Simulation Tools

Purpose – A simplified global positioning system (GPS) error model including models for a variety of abnormal operational conditions and failures is developed to provide simulation tools for the design, testing, and evaluation of autonomous flight fault tolerant control laws. The paper aims to discuss these issues. Design/methodology/approach – Analysis and experimental data are used to build simplified models for GPS position and velocity errors on all three channels. The GPS model is interfaced with West Virginia University unmanned aerial vehicles (UAV) simulation environment and its utility demonstrated through simulation for several autonomous flight scenarios including GPS abnormal operation. Findings – The proposed simplified GPS model achieves desirable levels of accuracy and realism for all components for the purpose of general UAV dynamic simulation and development of fault tolerant autonomous flight control laws. Research limitations/implications – The simplified GPS model allows investigating GPS malfunction effects on the performance of autonomous UAVs and designing trajectory tracking algorithms with advanced fault tolerant capabilities. Practical implications – The simplified GPS model has proved to be a flexible and useful tool for UAV simulation and design of autonomous flight control laws at normal and abnormal conditions. Originality/value – The outcomes of this research effort achieve a level of detail never attempted before in modeling GPS operation at normal and abnormal conditions for UAV simulation and autonomous flight control laws design using a simplified framework.

Flight Control System Design and Autonomous Flight Control of Small-Scale Unmanned Helicopter Based on Nanosensors

2021 ◽  
Vol 16 (4) ◽  
pp. 675-688
Author(s):  
Xinfan Yin ◽  
Xianmin Peng ◽  
Guichuan Zhang ◽  
Binghui Che ◽  
Chang Wang
Keyword(s):  
Control System ◽  
Flight Control ◽  
Control Algorithm ◽  
Installation Space ◽  
Small Scale ◽  
Unmanned Helicopter ◽  
Flight Control System ◽  
Autonomous Flight ◽  
Position Controller ◽  
Attitude Controller

Due to the limitation of the size and power, micro unmanned aerial vehicle (MUAV) usually has a small load capacity. Aiming at the problems of limited installation space and easy being interfered in flight attitude measurement of the small-scale unmanned helicopter (SUH), a low-cost and lightweight flight control system of the SUH based on ARM Cortex-M4 core microcontroller and Micro-Electro-Mechanical Systems (MEMS) sensors is developed in this paper. On this basis, in order to realize the autonomous flight control of SUH, firstly, the mathematical model of the SUH is given by using the Newton-Euler formulation. Secondly, a cascade flight controller consisting of the attitude controller and the position controller is developed based on linear active disturbance rejection control (LADRC) and proportional-integral-derivative (PID) control. Furthermore, simulations are conducted to validate the performance of the attitude controller and the position controller in MATLAB/SIMULINK simulation environment. Finally, based on the Align T-REX 470L SUH experimental platform, the hovering experiment and the route flight experiment are also carried out to validate the performance of the designed flight control system hardware and the proposed control algorithm. The results show that the flight control system designed in this paper has high reliability and strong anti-interference ability, and the control algorithm can effectively and reliably realize the attitude stabilization control and route control of the SUH, with high control accuracy and small error.

scholarly journals Neural evidence supports a dual sensory-motor role for insect wings

2017 ◽  
Vol 284 (1862) ◽  
pp. 20170969 ◽  
Author(s):  
Brandon Pratt ◽  
Tanvi Deora ◽  
Thomas Mohren ◽  
Thomas Daniel
Keyword(s):  
Flight Control ◽  
Information Acquisition ◽  
Receptive Fields ◽  
Mechanical Stimulus ◽  
Sensory Function ◽  
Computational Techniques ◽  
Campaniform Sensilla ◽  
Specific Stimulus ◽  
Processing Times ◽  
Stimulus Features

Flying insects use feedback from various sensory modalities including vision and mechanosensation to navigate through their environment. The rapid speed of mechanosensory information acquisition and processing compensates for the slower processing times associated with vision, particularly under low light conditions. While halteres in dipteran species are well known to provide such information for flight control, less is understood about the mechanosensory roles of their evolutionary antecedent, wings. The features that wing mechanosensory neurons (campaniform sensilla) encode remains relatively unexplored. We hypothesized that the wing campaniform sensilla of the hawkmoth, Manduca sexta, rapidly and selectively extract mechanical stimulus features in a manner similar to halteres. We used electrophysiological and computational techniques to characterize the encoding properties of wing campaniform sensilla. To accomplish this, we developed a novel technique for localizing receptive fields using a focused IR laser that elicits changes in the neural activity of mechanoreceptors. We found that (i) most wing mechanosensors encoded mechanical stimulus features rapidly and precisely, (ii) they are selective for specific stimulus features, and (iii) there is diversity in the encoding properties of wing campaniform sensilla. We found that the encoding properties of wing campaniform sensilla are similar to those for haltere neurons. Therefore, it appears that the neural architecture that underlies the haltere sensory function is present in wings, which lends credence to the notion that wings themselves may serve a similar sensory function. Thus, wings may not only function as the primary actuator of the organism but also as sensors of the inertial dynamics of the animal.

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