The Impact of Virtual Reality and Viewpoints in Body Motion Based Drone Teleoperation

The operation of telerobotic systems can be a challenging task, requiring intuitive and efficient interfaces to enable inexperienced users to attain a high level of proficiency. Body-Machine Interfaces (BoMI) represent a promising alternative to standard control devices, such as joysticks, because they leverage intuitive body motion and gestures. It has been shown that the use of Virtual Reality (VR) and first-person view perspectives can increase the user's sense of presence in avatars. However, it is unclear if these beneficial effects occur also in the teleoperation of non-anthropomorphic robots that display motion patterns different from those of humans. Here we describe experimental results on teleoperation of a non-anthropomorphic drone showing that VR correlates with a higher sense of spatial presence, whereas viewpoints moving coherently with the robot are associated with a higher sense of embodiment. Furthermore, the experimental results show that spontaneous body motion patterns are affected by VR and viewpoint conditions in terms of variability, amplitude, and robot correlates, suggesting that the design of BoMIs for drone teleoperation must take into account the use of Virtual Reality and the choice of the viewpoint.

Personalized Body-Machine Interfaces for Advanced Human-Robot Interaction

Matteo Macchini

Robotic systems are becoming more and more pervasive in modern industrial, scientific and personal activities through recent years and will play a fundamental role in the future society. Despite their increasing level of automation, teleoperation is still needed in many robotic applications. Human-Robot Interfaces (HRIs) are a central element in the diffusion of robots and are a crucial component to make them available to the use of most people. However, standard interfaces such as remote controllers still need time and extensive training to be proficiently mastered by users. Body-Machine Interfaces (BoMIs) represent a promising alternative to such devices as they leverage the innate ability of humans to finely control their movements. This thesis aims to provide new insights about humans' natural motor behaviors when interfacing with robots and to propose novel approaches for the implementation of HRIs based on body motion. The core contribution of this work is the design, realization and qualification of machine learning-based methods to translate the motion of a person into control inputs for a robot. The methods described here allowed naive users to effectively control real and simulated robots based on the processing of their body movements with a faster learning rate compared to classic interfaces. We extended our core algorithm to leverage knowledge about the natural organization of human motion resulting a more general method that was successfully applied for the control of a set of morphologically different machines. Through the implementation of an online adaptive functionality, we enabled users to modify their motor behaviour during teleoperation resulting in a lower perceived physical and cognitive workload and augmented performance. Human factors such as the sense of presence in virtual or distant environments, or the motor characteristics of distinct body areas are a key factor for advanced telerobotic applications. We studied the impact of the use of Virtual Reality for the control of non-anthropomorphic machines, and the effects of designing BoMIs based on different limbs opening to new knowledge about the effects of such factors in human-robot interaction. The design of a wearable haptic interface for drone operation showed the potential of augmenting their senses to perceive a distal environment through the sense of touch. Thanks to this novel design, nonexpert users were enabled to navigate drones through narrow passages with limited visibility, a task almost impossible to perform with standard interfaces.We believe that this thesis contributes to pave the way towards a deeper understanding of humans in human-robot interaction, as well as providing a new set of tools for the design of simple, intuitive interfaces with the goal of democratizing robotic teleoperation for the masses.

EPFL2021

Does spontaneous motion lead to intuitive Body-Machine Interfaces? A fitness study of different body segments for wearable telerobotics

Dario Floreano, Matteo Macchini, Fabrizio Schiano

Human-Robot Interfaces (HRIs) represent a crucial component in telerobotic systems. Body-Machine Interfaces (BoMIs) based on body motion can feel more intuitive than standard HRIs for naive users as they leverage humans’ natural control capability over their movements. Among the different methods used to map human gestures into robot commands, data-driven approaches select a set of body segments and transform their motion into commands for the robot based on the users’ spontaneous motion patterns. Despite being a versatile and generic method, there is no scientific evidence that implementing an interface based on spontaneous motion maximizes its effectiveness. In this study, we compare a set of BoMIs based on different body segments to investigate this aspect. We evaluate the interfaces in a teleoperation task of a fixed-wing drone and observe users’ performance and feedback. To this aim, we use a framework that allows a user to control the drone with a single Inertial Measurement Unit (IMU) and without prior instructions. All the interfaces are entirely datadriven and depend on the user’s spontaneous motion. We show through a user study that selecting the body segment for a BoMI based on spontaneous motion can lead to sub-optimal performance. Based on our findings, we suggest additional metrics based on biomechanical and behavioral factors that might improve data-driven methods for the design of HRIs.

2021

HRI usability evaluation of interaction modes for a teleoperated agricultural robotic sprayer

Thanasis Hadzilacos

Teleoperation of an agricultural robotic system requires effective and efficient human-robot interaction. This paper investigates the usability of different interaction modes for agricultural robot teleoperation. Specifically, we examined the overall influence of two types of output devices (PC screen, head mounted display), two types of peripheral vision support mechanisms (single view, multiple views), and two types of control input devices (PC keyboard, PS3 gamepad) on observed and perceived usability of a teleoperated agricultural sprayer. A modular user interface for teleoperating an agricultural robot sprayer was constructed and field-tested. Evaluation included eight interaction modes: the different combinations of the 3 factors. Thirty representative participants used each interaction mode to navigate the robot along a vineyard and spray grape clusters based on a 2 x 2 x 2 repeated measures experimental design. Objective metrics of the effectiveness and efficiency of the human-robot collaboration were collected. Participants also completed questionnaires related to their user experience with the system in each interaction mode. Results show that the most important factor for human-robot interface usability is the number and placement of views. The type of robot control input device was also a significant factor in certain dependents, whereas the effect of the screen output type was only significant on the participants' perceived workload index. Specific recommendations for mobile field robot teleoperation to improve HRI awareness for the agricultural spraying task are presented. (C) 2017 Elsevier Ltd. All rights reserved.

Elsevier Sci Ltd2017

Personalized Body-Machine Interfaces for Advanced Human-Robot Interaction

Matteo Macchini

EPFL2021