Multimodal control for human-robot cooperation

Cherubini, Andrea; Passama, Robin; Meline, Arnaud; Crosnier, André; Fraisse, Philippe

doi:10.1109/iros.2013.6696664

Cited by 31 publications

(27 citation statements)

References 14 publications

(18 reference statements)

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“…To this end, many pHRI works rely on the physical contact (touch) between robot and operator [31]. More recently, to guarantee interaction even in the absence of direct contact, researchers have proposed the use of pointing gestures [32], as well as the integration of vision with force [33,34,35]. Also, in our work, interaction includes both vision and force.…”

Section: Research On Physical Human-robot Collaborationmentioning

confidence: 99%

Collaborative manufacturing with physical human–robot interaction

Cherubini¹,

Passama²,

Crosnier³

et al. 2016

Robotics and Computer-Integrated Manufacturing

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469

188

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Although the concept of industrial cobots dates back to 1999, most present day hybrid human-machine assembly systems are merely weight compensators. Here, we present results on the development of a collaborative human-robot manufacturing cell for homokinetic joint assembly. The robot alternates active and passive behaviours during assembly, to lighten the burden on the operator in the first case, and to comply to his/her needs in the latter. Our approach can successfully manage direct physical contact between robot and human, and between robot and environment. Furthermore, it can be applied to standard position (and not torque) controlled robots, common in the industry. The approach is validated in a series of assembly experiments. The human workload is reduced, diminishing the risk of strain injuries. Besides, a complete risk analysis indicates that the proposed setup is compatible with the safety standards, and could be certified.

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Section: Research On Physical Human-robot Collaborationmentioning

confidence: 99%

Collaborative manufacturing with physical human–robot interaction

Cherubini¹,

Passama²,

Crosnier³

et al. 2016

Robotics and Computer-Integrated Manufacturing

Self Cite

469

188

View full text Add to dashboard Cite

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“…In (27), Λ * is the diagonal gain matrix applied when s is close to s * , and α ≥ 0 and β ∈ ]0, 1] are two scalar parameters such that, as the task error norm ||s * − s|| increases, Λ exponentially decreases (with slope dependent on α) to βΛ * , for very large task error. This exponential trend compensates that of the error signal, thus generating a less variable control inputq, as will be shown by the experiments.…”

Section: Control Frameworkmentioning

confidence: 99%

“…In our previous work [27], we have started the design of a multimodal framework for human-robot cooperation. The approach is marker-less, and has been validated in a mock-up industrial scenario.…”

Section: Introductionmentioning

confidence: 99%

“…Other, minor improvements, with regards to [27], include the introduction of force-based control, guaranteeing safety of HRI, and better accuracy, velocity, and control smoothness. Moreover, our task-oriented approach, in contrast with similar ones, such as the stack-of-tasks [29] and constraint-based programming [30,31], is directly usable in real HRI scenarios (to our knowledge, the method presented in [31] has been used, for now, only for human collision avoidance).…”

Section: Introductionmentioning

confidence: 99%

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A unified multimodal control framework for human–robot interaction

Cherubini

Passama

Fraisse

et al. 2015

Robotics and Autonomous Systems

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In human-robot interaction, the robot controller must reactively adapt to sudden changes in the environment (due to unpredictable human behaviour). This often requires operating different modes, and managing sudden signal changes from heterogeneous sensor data. In this paper, we present a multimodal sensor-based controller, enabling a robot to adapt to changes in the sensor signals (here, changes in the human collaborator behaviour). Our controller is based on a unified task formalism, and in contrast with classical hybrid visionforce-position control, it enables smooth transitions and weighted combinations of the sensor tasks. The approach is validated in a mock-up industrial scenario, where pose, vision (from both traditional camera and Kinect), and force tasks must be realized either exclusively or simultaneously, for human-robot collaboration.

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“…Finally, Cherubini and al. [15] use also the Kinect and one another camera rigidly linked to the robot's end effector for human intention recognition and human-robot collaboration in industry environment. The system is based on a multimodal control approach and a state machine.…”

Section: A Camera-based Motion Trakingmentioning

confidence: 99%

Safer hybrid workspace using human-robot interaction while sharing production activities

Meziane

Otis

et al. 2014

2014 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Proceedings

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Abstract-In a near future, human and industrial manipulator will work together sharing a common workspace and production activities leading to a potential increase of accident. The research project concerns the adaptation of industrial robot already installed in a flexible manufacturing system in order to make it more interactive with human. The aim concerns the reduction of potential risk of injuries while working with an industrial robot. This paper presents a new inexpensive, non-intrusive, non-invasive, and non-vision-based system, for human detection and collision avoidance. One method investigated for improving safety concerns planning of safe path. This system recognizes human activities and locates operator's position in real time through an instrumented safety helmet. This safety helmet includes an IMU (Inertial Measurement Unit) and an indoor localization system such as RSSI (Received Signal Strength Indication) using industrial wireless equipment. A hybrid workspace including a flexible manufacturing system has been designed in order to practice experiments in an industrial-like environment.

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Multimodal control for human-robot cooperation

Cited by 31 publications

References 14 publications

Collaborative manufacturing with physical human–robot interaction

Collaborative manufacturing with physical human–robot interaction

A unified multimodal control framework for human–robot interaction

Safer hybrid workspace using human-robot interaction while sharing production activities

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