Assisting Dependent People at Home Through Autonomous Unmanned Aerial Vehicles

Belmonte, Lidia M.; Morales, Rafael; García, Arturo S.; Segura, Eva; Nováis, Paulo; Fernández-Caballero, Antonio

doi:10.1007/978-3-030-24097-4_26

Cited by 7 publications

(7 citation statements)

References 19 publications

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“…σ models the variance of I INV and it depends on the thermal voltage and transistor's ideality factor n. which is equivalent to the harmonic mean of currents from the constituent inverters. Under a multivariate control of inverter's current, that is, when more than one input voltages vary simultaneously, the characteristics of I INV are, therefore, more closely represented by an HMG function in (10) where each constituent 1-D function is controlled by input V X , V Y , and V Z . The column current in the design follows a harmonic mean of Gaussians since current through each stacked inverter is proportional to a Gaussian-like function.…”

Section: A Mixture Of Hmg Functionsmentioning

confidence: 99%

“…S PATIALLY intelligent devices can autonomously traverse and explore their application domain, thereby opening intriguing prospects for sensors and embedded systems. For example, in an agriculture field, flying cameras can locate infected plants to prevent disease spread [3], [4]; flying gas sensors can identify gas leaks in an industrial plant [5], [6]; during earthquakes, autonomous cameras can assist in search and rescue missions by navigating through building debris [7], [8]; and inside a home, a flying camera can actively monitor fire breakouts and intruders [9], [10]. Thus, most embedded systems, enlivened by spatial intelligence, can have dramatically elevated use cases.…”

Section: Introductionmentioning

confidence: 99%

“…Fig. 4(b) shows the correlation between the inverter current model in(10) and HSPICE-simulated inverter's short-circuit current in linear and log domains. Fig.4(c) shows the modulation of μ by programming V TH,n and V TH, p in the inverter design.Fig.…”

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confidence: 99%

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Ultralow-Power Localization of Insect-Scale Drones: Interplay of Probabilistic Filtering and Compute-in-Memory

Shukla

Muralidhar

Iliev

et al. 2022

IEEE Trans. VLSI Syst.

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We propose a novel compute-in-memory (CIM)-based ultralow-power framework for probabilistic localization of insect-scale drones. Localization is a critical subroutine for path planning and rotor control in drones, where a drone is required to continuously estimate its pose (position and orientation) in flying space. The conventional probabilistic localization approaches rely on the 3-D Gaussian mixture model (GMM)-based representation of a 3-D map. A GMM model with hundreds of mixture functions is typically needed to adequately learn and represent the intricacies of the map. Meanwhile, localization using complex GMM map models is computationally intensive. Since insect-scale drones operate under extremely limited area/power budget, continuous localization using GMM models entails much higher operating energy, thereby limiting flying duration and/or size of the drone due to a larger battery.Addressing the computational challenges of localization in an insect-scale drone using a CIM approach, we propose a novel framework of 3-D map representation using a harmonic mean of the "Gaussian-like" mixture (HMGM) model. We show that short-circuit current of a multiinput floating-gate CMOS-based inverter follows the harmonic mean of a Gaussian-like function. Therefore, the likelihood function useful for drone localization can be efficiently implemented by connecting many multiinput inverters in parallel, each programmed with the parameters of the 3-D map model represented as HMGM. When the depth measurements are projected to the input of the implementation, the summed current of the inverters emulates the likelihood of the measurement. We have characterized our approach on an RGB-D scenes dataset. The proposed localization framework is ∼25× energy-efficient than the traditional, 8-bit digital GMM-based processor paving the way for tiny autonomous drones.

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Section: A Mixture Of Hmg Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ultralow-Power Localization of Insect-Scale Drones: Interplay of Probabilistic Filtering and Compute-in-Memory

Shukla

Muralidhar

Iliev

et al. 2022

IEEE Trans. VLSI Syst.

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“…Apart from this, to provide a solution that is accepted by the dependent person and can thus be of real use to them, it is also necessary to approach the design of the assistant UAV from the point of view of the assisted person [9,10]. Therefore, we have decided to design a virtual reality (VR) simulation platform that provides a safe and realistic environment to develop and test the different engineering solutions [11,12], and also allows us to conduct studies with potential end-users and people who know the problem of care for dependent people in order to adapt the design to their preferences.…”

Section: Introductionmentioning

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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“…Our current research interest is framed in this field. One of our major objectives is to design an autonomous aerial vehicle to assist dependent people [6,7]. In this sense, it is necessary that a vision system captures images of the dependent person, which are analyzed in order to determine the assistance required at each moment.…”

Section: Introductionmentioning

confidence: 99%

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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Assisting Dependent People at Home Through Autonomous Unmanned Aerial Vehicles

Cited by 7 publications

References 19 publications

Ultralow-Power Localization of Insect-Scale Drones: Interplay of Probabilistic Filtering and Compute-in-Memory

Ultralow-Power Localization of Insect-Scale Drones: Interplay of Probabilistic Filtering and Compute-in-Memory

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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