Robust Linear Longitudinal Feedback Control of a Flapping Wing Micro Air Vehicle

Belmonte, Lidia M.; Morales, Rafael; Fernández-Caballero, Antonio; Somolinos, J.A.

doi:10.1007/978-3-319-18914-7_47

Cited by 2 publications

(2 citation statements)

References 24 publications

(20 reference statements)

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“…• Control Algorithm: It is used to calculate what inputs should be applied to the quadrotor model in order to follow a specific trajectory reference, thereby ensuring that the UAV is correctly positioned and oriented during the monitoring flight. For this component, we designed a generalised proportional integral (GPI) controller based on the flatness theory that demonstrated good results in both stabilisation and tracking tasks, even in the presence of atmospheric disturbances and noise measurements, improving the performance of a traditional PID controller [16][17][18][19][20][21][22][23]. The theoretical details of the GPI control scheme are described in [15].…”

Section: Uav Simulatormentioning

confidence: 99%

Facial Emotion Recognition from an Unmanned Flying Social Robot for Home Care of Dependent People

et al. 2021

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This work is part of an ongoing research project to develop an unmanned flying social robot to monitor dependants at home in order to detect the person’s state and bring the necessary assistance. In this sense, this paper focuses on the description of a virtual reality (VR) simulation platform for the monitoring process of an avatar in a virtual home by a rotatory-wing autonomous unmanned aerial vehicle (UAV). This platform is based on a distributed architecture composed of three modules communicated through the message queue telemetry transport (MQTT) protocol: the UAV Simulator implemented in MATLAB/Simulink, the VR Visualiser developed in Unity, and the new emotion recognition (ER) system developed in Python. Using a face detection algorithm and a convolutional neural network (CNN), the ER System is able to detect the person’s face in the image captured by the UAV’s on-board camera and classify the emotion among seven possible ones (surprise; fear; happiness; sadness; disgust; anger; or neutral expression). The experimental results demonstrate the correct integration of this new computer vision module within the VR platform, as well as the good performance of the designed CNN, with around 85% in the F1-score, a mean of the precision and recall of the model. The developed emotion detection system can be used in the future implementation of the assistance UAV that monitors dependent people in a real environment, since the methodology used is valid for images of real people.

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Section: Uav Simulatormentioning

confidence: 99%

Facial Emotion Recognition from an Unmanned Flying Social Robot for Home Care of Dependent People

et al. 2021

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“…Flapping-Wing: This class of micro-UAV reproduces the flight of birds or insects [47][48][49]. It has an extremely low payload capability and low endurance owing to its reduced size.…”

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Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study

2019

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Personal assistant robots provide novel technological solutions in order to monitor people’s activities, helping them in their daily lives. In this sense, unmanned aerial vehicles (UAVs) can also bring forward a present and future model of assistant robots. To develop aerial assistants, it is necessary to address the issue of autonomous navigation based on visual cues. Indeed, navigating autonomously is still a challenge in which computer vision technologies tend to play an outstanding role. Thus, the design of vision systems and algorithms for autonomous UAV navigation and flight control has become a prominent research field in the last few years. In this paper, a systematic mapping study is carried out in order to obtain a general view of this subject. The study provides an extensive analysis of papers that address computer vision as regards the following autonomous UAV vision-based tasks: (1) navigation, (2) control, (3) tracking or guidance, and (4) sense-and-avoid. The works considered in the mapping study—a total of 144 papers from an initial set of 2081—have been classified under the four categories above. Moreover, type of UAV, features of the vision systems employed and validation procedures are also analyzed. The results obtained make it possible to draw conclusions about the research focuses, which UAV platforms are mostly used in each category, which vision systems are most frequently employed, and which types of tests are usually performed to validate the proposed solutions. The results of this systematic mapping study demonstrate the scientific community’s growing interest in the development of vision-based solutions for autonomous UAVs. Moreover, they will make it possible to study the feasibility and characteristics of future UAVs taking the role of personal assistants.

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Robust Linear Longitudinal Feedback Control of a Flapping Wing Micro Air Vehicle

Cited by 2 publications

References 24 publications

Facial Emotion Recognition from an Unmanned Flying Social Robot for Home Care of Dependent People

Facial Emotion Recognition from an Unmanned Flying Social Robot for Home Care of Dependent People

Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study

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