Proximity Human-Robot Interaction Using Pointing Gestures and a Wrist-mounted IMU

Gromov, Boris; Abbate, Gabriele; Gambardella, Luca Maria; Giusti, Alessandro

doi:10.1109/icra.2019.8794399

Cited by 33 publications

(11 citation statements)

References 31 publications

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“…Human pointing devices, such as the computer mouse, are ubiquitous in modern computers. Pointing motion has been leveraged in human-robot interaction (HRI) as a natural and effective way to communicate with robots [1][2][3][4][5][6] .…”

Section: Introductionmentioning

confidence: 99%

Human Pointing Motion during Interaction with an Autonomous Blimp

Hou

Tao

Zhang

2022

Preprint

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We investigate the interaction between a human and a miniature autonomous blimp using a wand as pointing device. The wand movement generated by the human is followed by the blimp through a tracking controller.The Vector Integration to Endpoint (VITE) model, previously applied to human-computer interface (HCI), has been applied to model the human generated wand movement when interacting with the blimp. We show that the closed-loop human-blimp dynamics are exponentially stable. Similar to HCI using computer mouse, overshoot motion of the blimp has been observed. The VITE model can be viewed as a special reset controller used by the human to generate wand movements that effectively reduce the overshoot of blimp motion. Moreover, we have observed undershoot motion of the blimp due to its inertia, which does not appear in HCI using computer mouse. The asymptotic stability of the human-blimp dynamics is beneficial towards tolerating the undershoot motion of the blimp.

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Section: Introductionmentioning

confidence: 99%

Human Pointing Motion during Interaction with an Autonomous Blimp

Hou

Tao

Zhang

2022

Preprint

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“…IMU suits were proposed as a solution to the problem of unobstructed motion capture as early as 2004 in the context of humanoid robot control [ 19 ], and motion capture of specific body parts for specific applications has improved significantly since then. IMU systems for tracking head [ 20 ], leg [ 21 ], hand [ 22 ], and wrist [ 23 ] motion have been used in various industrial robotic applications as well as for rehabilitation tasks [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Wearable IMU System for Flexible Teleoperation of a Collaborative Industrial Robot

Škulj

Vrabič

Podržaj

2021

Sensors

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Increasing the accessibility of collaborative robotics requires interfaces that support intuitive teleoperation. One possibility for an intuitive interface is offered by wearable systems that measure the operator’s movement and use the information for robot control. Such wearable systems should preserve the operator’s movement capabilities and, thus, their ability to flexibly operate in the workspace. This paper presents a novel wireless wearable system that uses only inertial measurement units (IMUs) to determine the orientation of the operator’s upper body parts. An algorithm was developed to transform the measured orientations to movement commands for an industrial collaborative robot. The algorithm includes a calibration procedure, which aligns the coordinate systems of all IMUs, the operator, and the robot, and the transformation of the operator’s relative hand motions to the movement of the robot’s end effector, which takes into account the operator’s orientation relative to the robot. The developed system is demonstrated with an example of an industrial application in which a workpiece needs to be inserted into a fixture. The robot’s motion is compared between the developed system and a standard robot controller. The results confirm that the developed system is intuitive, allows for flexible control, and is robust enough for use in industrial collaborative robotic applications.

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“…Currently, there is no standard method to define such mappings for motion-based BoMIs. Heuristic methods are based on gestures commonly adopted in human communication, such as pointing [17][18][19][20] , or leverage kinematic correspondences between human and robot morphologies, for example by letting an operator control an anthropomorphic robotic arm using their arm motion 21,22 . Data-driven methods rely on data from human spontaneous motor behavior when interacting with the robot, and have been used to control aerial robots 10 , ground robots 23 and humanoid manipulators 24 .…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Personalization of Body-Machine Interfaces to Control Diverse Robot Types

Macchini

Schiano

Floreano

2021

Preprint

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Body-Machine Interfaces (BoMIs) for robotic teleoperation can improve a user’s experience and performance. However, the implementation of such systems needs to be optimized on each robot independently, as a general approach has not been proposed to date. Here, we present a novel machine learning method to generate personalized BoMIs from an operator’s spontaneous body movements. The method captures individual motor synergies that can be used for the teleoperation of robots. The proposed algorithm applies to people with diverse behavioral patterns to control robots with diverse morphologies and degrees of freedom, such as a fixed-wing drone, a quadrotor, and a robotic manipulator.

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Proximity Human-Robot Interaction Using Pointing Gestures and a Wrist-mounted IMU

Cited by 33 publications

References 31 publications

Human Pointing Motion during Interaction with an Autonomous Blimp

Human Pointing Motion during Interaction with an Autonomous Blimp

A Wearable IMU System for Flexible Teleoperation of a Collaborative Industrial Robot

Data-Driven Personalization of Body-Machine Interfaces to Control Diverse Robot Types

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