Human-drone Interaction Research Articles

The Role of Drone’s Digital Facial Emotions and Gaze in Shaping Individuals’ Social Proxemics and Interpretation

The potential for drones to engage with people and find applications in social contexts is often constrained by their mechanical design, leading to concerns and negative associations that significantly affect people’s acceptance of drones in their personal space. In this study, we explore how social cues can impact the spatial dynamic of Human–Drone Interaction (HDI). Within a fully immersive virtual environment, 25 participants engaged with and navigated around a drone that communicated several facial emotions on a digital display such as Joy, Sadness and Anger, and enacted distinct ”gaze” behaviors (i.e., following participants or averting its gaze). Our results indicate that participants responded to the drone’s gaze in a manner akin to what is reported during human interaction: maintaining a greater distance when the drone established eye contact and instinctively getting closer when it averted its gaze. A more granular analysis revealed that participants who lacked prior familiarity with drones or possessed neutral to positive attitudes toward them demonstrated a higher sensitivity to the drone’s digital facial emotions. This finding highlights the potential for leveraging social cues to facilitate the integration of drones into various human-centric environments and tailor their design and behavior to suit specific individuals and situations.

Preferred Distance in Human-Drone Interaction.

In two augmented-reality experiments, we transferred the paradigm of interpersonal distance regulation to human-drone interaction. In the first experiment, we used a simple spherical drone model and explored how both hovering height and approach angle affect the preferred distance. Drone height above the ground had a strong effect. The preferred distance to the drone was larger than that typically found toward human actors, in particular, when the drone trajectory was very high. In the second experiment, we sought to gain a deeper understanding of the factors that may influence this effect. In addition to the simple spherical drone model used in the first experiment, we also varied its appearance and attachment to the ground. Surprisingly, anthropomorphic features increased preferred distances. We, therefore, discuss the extent to which social aspects and subjectively perceived danger influence the preferred distance for interaction with drones, which thus need to be considered in the design of human-drone interaction.

Exploring the effect of human-drone communication modality on safety and balance control in virtual construction environments

This study examines the impact of Human-Drone Interaction (HDI) modalities on construction workers’ safety and balance control within virtual environments. Utilising virtual reality (VR) simulations, the study explored how gesture and speech-based communications influence workers’ physical postures and balance, contrasting these modalities with a non-interactive control group. One hundred participants were recruited, and their movements and balance control were tracked using motion sensors while they interacted with virtual drones through either gesture, speech, or without communication. Results showed that interactive modalities significantly improved balance control and reduced the risk of falls, suggesting that advanced HDI can enhance safety on construction sites. However, speech-based interaction increased cognitive workload, highlighting a trade-off between physical safety and mental strain. These findings underscore the potential of integrating intuitive communication methods into construction operations, although further research is needed to optimise these interactions for long-term use and in diverse noise environments.

Humanzentrierte Implementierung von (teil-)autonomen Drohnen

Abstract The industrial use of drones is constantly increasing due to the transition from Industry 4.0 to Industry 5.0. A prerequisite for the concrete implementation is the legal and organizational risk assessment of flight robotics. The core of the article is a systematic overview of relevant human-centered risk factors for the adaptation of drones in organizations. Based on the proposed risk taxonomy, design options for human-drone interaction and an overview of key questions for risk assessment are presented.

Drones in Wonderland – Disentangling Collocated Interaction Using Radical Form

We propose the use of radical drone form to modify collocated Human-Drone Interaction (HDI) experiences. Our work suggests that due to the lack of collocated interactive experiences with flying entities people are more likely to follow HDI metaphors that are not necessarily realistic, and instead relate to the realms of myth, magic, and fantasy. We motivate this interaction design opportunity and contribute a set of collocated HDI design variables that help utilize it. To further investigate our approach, we present the results of two user studies, one using static stimuli (N=97) and one immersive experience (N=20), around various drone forms. Our findings suggest that form strongly impacts the ways people perceive drones and their roles. We conclude by reflecting on people's interactive experiences with radical drone forms and a set of derived implications for HDI design.

Emotion Appropriateness in Human–Drone Interaction

Emotion Appropriateness in Human–Drone Interaction

Force-torque (F-T) based multi-drone cooperative transport using fuzzy logic and low-cost and imprecise F-T sensor

This paper presents a novel approach to perform the task of cooperative transportation (CT) by using multiple quadcopter drones. A leader-follower approach is utilized. Considering that the leader drone can be controlled using different means, such as Proportional, Integral, and Derivative (PID) control or remote control by an operator, this paper focuses on designing the control scheme for the follower drone. This paper specifically considers outdoor application of such systems that requires usage of Global Positioning System (GPS) to receive the positional information of the drones. However, GPS has inherent errors of order of magnitude that can destabilize the system. In order to address this major limitation, a Force-Torque Feedback Controller (FT-FC) is proposed to control the follower drone. The FT-FC provides control based on the interaction forces and torques acting at the point of contact between the follower drone and the payload. Using such passive control schemes, drones are thus not required to communicate with each other. Fuzzy Logic (FL) is used to implement the FT-FC. Fuzzy Logic provides effective force-torque coordination between drones, emulates human behavior during CT, and allows use of noisy inexpensive force-torque sensors. This paper presents results from numerical simulations showing the effectiveness of the proposed controller for way-point navigation and complex trajectory tracking. Additionally, the effectiveness of the fuzzy-based FT-FC in terms of handling disturbances is also demonstrated. The proposed FL FT-FC is also experimentally validated for two applications viz. three-dimensional physical Human Drone Interaction (HDI) and CT.

Inspection of the Most Suitable Approach and Information Projection Method for Interactions in the Night Flight of a Projector-Mounted Drone

Security is considered heavy labor work owing to night shifts and long working hours. In recent years, the number of security guards has been increasing, and the labor shortage for security guards has become a problem in Japan. To overcome these problems, robots have been used for security purposes. However, most are unable to interact with users or guards at night. In this study, a drone called aerial ubiquitous display (AUD) is proposed to improve the problems of existing security methods using robots and to provide night security. The AUD enables human-drone interaction and night security. Moreover, when an AUD interacts with humans, the drone must come close to the human and project information on the ground. Therefore, this study investigated the optimal parameters for a projector-equipped drone to approach a human at night. In addition, by comparing these results with those of the day approach, we verified whether there would be a change in perception between daytime and nighttime. Furthermore, an experiment was conducted to investigate the types of projections that are most likely to capture a user’s attention.

Generative Adversarial Networks and Data Clustering for Likable Drone Design.

Novel applications for human-drone interaction demand new design approaches, such as social drones that need to be perceived as likable by users. However, given the complexity of the likability perception process, gathering such design information from the interaction context is intricate. This work leverages deep learning-based techniques to generate novel likable drone images. We collected a drone image database () applicable for design research and assessed the drone’s likability ratings in a user study (). We employed two clustering methodologies: 1. likability-based, which resulted in non-likable, neutral, and likable drone clusters; and 2. feature-based (VGG, PCA), which resulted in drone clusters characterized by visual similarity; both clustered using the K-means algorithm. A characterization process identified three drone features: colorfulness, animal-like representation, and emotional expressions through facial features, which affect drone likability, going beyond prior research. We used the likable drone cluster () for generating new images using StyleGAN2-ADA and addressed the dataset size limitation using specific configurations and transfer learning. Our results were mitigated due to the dataset size; thus, we illustrate the feasibility of our approach by generating new images using the original database. Our findings demonstrate the effectiveness of Generative Adversarial Networks (GANs) exploitation for drone design, and to the best of our knowledge, this work is the first to suggest GANs for such application.

Envisioning social drones in education.

Education is one of the major application fields in social Human-Robot Interaction. Several forms of social robots have been explored to engage and assist students in the classroom environment, from full-bodied humanoid robots to tabletop robot companions, but flying robots have been left unexplored in this context. In this paper, we present seven online remote workshops conducted with 20 participants to investigate the application area of Education in the Human-Drone Interaction domain; particularly focusing on what roles a social drone could fulfill in a classroom, how it would interact with students, teachers and its environment, what it could look like, and what would specifically differ from other types of social robots used in education. In the workshops we used online collaboration tools, supported by a sketch artist, to help envision a social drone in a classroom. The results revealed several design implications for the roles and capabilities of a social drone, in addition to promising research directions for the development and design in the novel area of drones in education.

An embedded system-based hand-gesture recognition for human-drone interaction

An embedded system-based hand-gesture recognition for human-drone interaction

An embedded system-based hand-gesture recognition for human-drone interaction

An embedded system-based hand-gesture recognition for human-drone interaction

Effect of Illumination on Human Drone Interaction Tasks: An Exploratory Study

With recent changes by the Federal Aviation Administration (FAA) opening the possibility of more areas for drones to be used, such as delivery, there will be increasingly more intera ctions between humans and drones soon. Although current human drone interaction (HDI) investigate what factors are necessary for safe interactions, very few has focused on drone illumination. Therefore, in this study, we explored how illumination affects users’ perception of the drone through a distance perception task. Data analysis did not indicate any significant effects in the normal distance estimation task for illumination or distance conditions. However, most participants underestimated the distance in the normal distance estimation task and indicated that the LED drone was closer when it wa s illuminated during the relative distance estimation task, even though the drones were equidistant. In future studies, factors such as the weather conditions, lighting patterns, and height of the drone will be explored.

CONTROL OF A DRONE WITH BODY GESTURES

AbstractDrones are becoming more popular within military applications and civil aviation by hobbyists and business. Achieving a natural Human-Drone Interaction (HDI) would enable unskilled drone pilots to take part in the flying of these devices and more generally easy the use of drones. The research within this paper focuses on the design and development of a Natural User Interface (NUI) allowing a user to pilot a drone with body gestures. A Microsoft Kinect was used to capture the user’s body information which was processed by a motion recognition algorithm and converted into commands for the drone. The implementation of a Graphical User Interface (GUI) gives feedback to the user. Visual feedback from the drone’s onboard camera is provided on a screen and an interactive menu controlled by body gestures and allowing the choice of functionalities such as photo and video capture or take-off and landing has been implemented. This research resulted in an efficient and functional system, more instinctive, natural, immersive and fun than piloting using a physical controller, including innovative aspects such as the implementation of additional functionalities to the drone's piloting and control of the flight speed.

Toward a roadmap for human-drone interaction

research-article Toward a roadmap for human-drone interaction Share on Authors: Jessica R. Cauchard Ben Gurion University of the Negev Ben Gurion University of the NegevView Profile , Mohamed Khamis University of Glasgow University of GlasgowView Profile , Jérémie Garcia ENAC, Université de Toulouse ENAC, Université de ToulouseView Profile , Matjaž Kljun University of Primorska University of PrimorskaView Profile , Anke M. Brock ENAC, Université de Toulouse ENAC, Université de ToulouseView Profile Authors Info & Affiliations InteractionsVolume 28Issue 2March - April 2021 pp 76–81https://doi.org/10.1145/3447889Published:03 March 2021 6citation741DownloadsMetricsTotal Citations6Total Downloads741Last 12 Months741Last 6 weeks106 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

Web AR Solution for UAV Pilot Training and Usability Testing.

Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real-time images. The great potential of unmanned aerial vehicles (UAVs) for carrying out routine and professional tasks has encouraged their use in the creation of several services, such as package delivery or industrial maintenance. Unfortunately, drone piloting is difficult to learn and requires specific training. Since regular training is performed with virtual simulations, we decided to propose a multiplatform cloud-hosted solution based in Web AR for drone training and usability testing. This solution defines a configurable trajectory through virtual elements represented over barcode markers placed on a real environment. The main goal is to provide an inclusive and accessible training solution which could be used by anyone who wants to learn how to pilot or test research related to UAV control. For this paper, we reviewed drones, AR, and human–drone interaction (HDI) to propose an architecture and implement a prototype, which was built using a Raspberry Pi 3, a camera, and barcode markers. The validation was conducted using several test scenarios. The results show that a real-time AR experience for drone pilot training and usability testing is achievable through web technologies. Some of the advantages of this approach, compared to traditional methods, are its high availability by using the web and other ubiquitous devices; the minimization of technophobia related to crashes; and the development of cost-effective alternatives to train pilots and make the testing phase easier for drone researchers and developers through trendy technologies.

Delivery of Automated External Defibrillators via Drones in Simulated Cardiac Arrest: Users' Experiences and the Human-Drone Interaction

BackgroundSurvival after out-of-hospital cardiac arrest (OHCA) in the United States is approximately 10%. Automatic external defibrillators (AEDs) are effective when applied early, yet public access AEDs are used in <2% of OHCAs. AEDs are often challenging for bystanders to locate and are rarely available in homes, where 70% of OHCAs occur. Drones have the potential to deliver AEDs to bystanders efficiently; however, little is known about the human-drone interface in AED delivery. ObjectivesTo describe user experiences with AED-equipped drones in a feasibility study of simulated OHCA in a community setting. MethodsWe simulated an OHCA in a series of trials with age-group/sex-matched participant pairs, with one participant randomized to search for a public access AED and the other to call a mock 9-1-1 telephone number that initiated the dispatch of an AED-equipped drone. We investigated user experience of 17 of the 35 drone recipient participants via semi-structured qualitative interviews and analyzed audio-recordings for key aspects of user experience. ResultsDrone recipient participants reported largely positive experiences, highlighting that this delivery method enabled them to stay with the victim and continue cardiopulmonary resuscitation. Concerns were few but included drone arrival timing and direction as well as bystander safety. Participants provided suggestions for improvements in the AED-equipped drone design and delivery procedures. ConclusionParticipants reported positive experiences interacting with an AED-equipped drone for a simulated OHCA in a community setting. Early findings suggest a role for drone-delivered AEDs to improve bystander AED use and improve outcomes for OHCA victims.

Drone services: issues in drones for location-based services from human-drone interaction to information processing

ABSTRACT Recently, drones (or unmanned aerial vehicles (UAVs)) are getting increasing attention and taking mobile computing to a new era. Due to the support of highly advanced technologies, soon they might be ubiquitous and networks of drones might be used in providing civilian drone services. In this paper, we provide a survey of drone services and applications, data management for drones, data services using drones, distributed computing trends fuelled by drones and a range of human-drone interaction research which is useful if drones are to regularly serve non-technical users, while highlighting the specific concerns in data management and airborne Internet-of-Things (IoT) computing infrastructure. We present concepts such as drones-as-a-service and fly-in, fly-out computing infrastructure, and note data management and system design issues that arise in these situations. Issues of Big Data arising from such applications, optimising the configuration of airborne and ground infrastructure to provide the best QoS and QoE, situation-awareness, scalability, reliability, scheduling for efficiency, interaction with users and drones using different methods are noted.

The State-of-the-Art of Human–Drone Interaction: A Survey

Drones have expanded from military operations to performing a broad range of civilian applications. As drone usage increases, humans will interact with such systems more often, therefore, it is important to achieve a natural human-drone interaction. Although some knowledge can be derived from the field of human-robot interaction, drones can fly in a 3D space, which essentially changes how humans can interact with them, making human-drone interaction a field of its own. This paper is the first survey on the emerging field of human-drone interaction focusing on multi-rotor systems, providing an overview of existing literature and the current state of the art in the field. This work begins with an analysis and comparison of the drone models that are commonly used by end-users and researchers in the field of human-drone interaction. Following, the current state of the field is discussed, including the roles of humans in HDI, innovative control methods, remaining aspects of interaction, and novelty drone prototypes and applications. This paper concludes by presenting a discussion of current challenges and future work in the field of human-drone interaction.

Drone Near Me

Personal drones are becoming more mainstream and are used for a variety of tasks, such as delivery and photography. The exposed blades in conventional drones raise serious safety concerns. To address this, commercial drones have been moving towards a safe-to-touch design or have increased safety by adding propeller guards. The affordances of safe-to-touch drones enable new types of touch-based human-drone interaction. Various applications have been explored, such as augmented sports and haptic feedback in virtual reality; however, it is unclear if individuals feel comfortable using direct touch and manipulation when interacting with safe-to-touch drones. A previous elicitation study showed how users naturally interact with drones. We replicated this study with an unsafe and a safe-to-touch drone, to find out if participants will instinctively use touch as a means of interacting with the safe-to-touch drone. We found that 58% of the participants used touch, and across all tasks 39% of interactions were touch-based. The proposed touch interactions were in agreement for 67% of the tasks, and users reported that interacting with the safe-to-touch drone was significantly less mentally demanding than the unsafe drone.