Ready—Aim—Fly! Hands-Free Face-Based HRI for 3D Trajectory Control of UAVs

Bruce, Jake; Perron, Jacob

doi:10.1109/crv.2017.39

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…C 3 : -'up/down', raises or lowers the altitude of the drone [32], [33], [34], [12], [24], [14], [28], [22], [36], [37]. C 4 : -'fly higher/fly lower', the drone raises/decreases altitude but moves forward/back [13], [15], [16], [38], [39], [14]. C 5 : -'turn right/turn left', drone spins around its axis to right or left [8], [40], [22], [37].…”

Section: B Resultsmentioning

confidence: 99%

A Set of Smart Ring Gestures for Drone Control

Şiean¹

2022

Proceedings of the 12th International Conference on “Electronics, Communications and Computing"

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We present in this paper the results of a frequency analysis of gesture commands frequently employed for human- drone interaction in the scientific literature, and we propose a set of gestures for controlling drones that can be performed with smart rings. Our method consists in the analysis of thirty-seven articles, which we examined closely to extract commands for human-drone interaction, including voice, gesture, and multi- modal input. Based on our meta-analysis, we present a set of six groups of commands for human-drone interaction together with a set of smart ring gestures to interact with and control drones. Our results can be used to inform the design of new interactive applications for controlling smart-ring drones.

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Section: B Resultsmentioning

confidence: 99%

A Set of Smart Ring Gestures for Drone Control

Şiean¹

2022

Proceedings of the 12th International Conference on “Electronics, Communications and Computing"

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“…The crossing problem for mobile robots has been especially dealt with by [6,7]. Both publications deal with the so-called rendezvous problem whereby the key point is the trajectory planning under time constraints, taking into account the dynamics of the con-tributing robots.…”

Section: Related Workmentioning

confidence: 99%

“…A humanfriendly interaction between robots and humans can be obtained by human-like sensor systems [5]. A prominent role in robot navigation is the trajectory-crossing problem of robots and humans [6,7] and corresponding fuzzy solutions [8]. Motivations for a fuzzy solution of the intersection problem are manifold.…”

Section: Introductionmentioning

confidence: 99%

Crossing-Point Estimation in Human–Robot Navigation—Statistical Linearization versus Sigma-Point Transformation

Palm,

Lilienthal

2024

Sensors

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Interactions between mobile robots and human operators in common areas require a high level of safety, especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection, the crossings of planned trajectories and their uncertainty based on model fluctuations, system noise and sensor noise play an outstanding role. This paper discusses the calculation of the expected areas of interactions during human–robot navigation with respect to fuzzy and noisy information. The expected crossing points of the possible trajectories are nonlinearly associated with the positions and orientations of the robots and humans. The nonlinear transformation of a noisy system input, such as the directions of the motion of humans and robots, to a system output, the expected area of intersection of their trajectories, is performed by two methods: statistical linearization and the sigma-point transformation. For both approaches, fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from the given output distribution parameters.

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“…Some output of the gesture recognition module is reported in Figure 3 . The face has been used to control the robot in [ 111 ]. The user triggers the robot to fly by making a distinct facial expression, and the direction and intensity of flight are given by the head position and size; three different trajectories have been implemented and tested.…”

Section: Eye Level Viewmentioning

confidence: 99%

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

et al. 2020

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The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed.

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Ready—Aim—Fly! Hands-Free Face-Based HRI for 3D Trajectory Control of UAVs

Cited by 12 publications

References 15 publications

A Set of Smart Ring Gestures for Drone Control

A Set of Smart Ring Gestures for Drone Control

Crossing-Point Estimation in Human–Robot Navigation—Statistical Linearization versus Sigma-Point Transformation

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

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