Multi-robot manipulation controlled by a human with haptic feedback

Sieber, Dominik; Musić, Selma; Hirche, Sandra

doi:10.1109/iros.2015.7353708

Cited by 19 publications

(38 citation statements)

References 14 publications

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“…In a similar scenario, the human directly controls the object position (i.e. the mean position of the robots here) and formation control is performed autonomously in [18], [102], [98]. In [81] both subtasks are performed by the human.…”

Section: Global and Local Behaviorsmentioning

confidence: 99%

“…In the active role, the human provides continuous commands, for example motion and/or force commands. This type of commands is typically conveyed through a motion measuring device such as a touch screen [54], vision-based system [102], [2], or a haptic device [64,81].…”

Section: Command Interfacesmentioning

confidence: 99%

“…In [102] the robot team is in charge of coordinating autonomously. Additionally, the robots within the team may perform the local subtask of collision avoidance, see e.g.…”

Section: Control For the Complementary Interactionmentioning

confidence: 99%

“…This is achieved by ensuring that the autonomous task is uncontrollable to the human, see e.g. [102] and [80].…”

Section: Control For the Complementary Interactionmentioning

confidence: 99%

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Control sharing in human-robot team interaction

Musić

Hirche

2017

Annual Reviews in Control

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122

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The interaction between humans and robot teams is highly relevant in many application domains, for example in collaborative manufacturing, search and rescue, and logistics. It is well-known that humans and robots have complementary capabilities: Humans are excellent in reasoning and planning in unstructured environments, while robots are very good in performing tasks repetitively and precisely. In consequence, one of the key research questions is how to combine human and robot team decision making and task execution capabilities in order to exploit their complementary skills. From a controls perspective this question boils down to how control should be shared among them. This article surveys advances in humanrobot team interaction with special attention devoted to control sharing methodologies. Additionally, aspects affecting the control sharing design, such as human behavior modeling, level of autonomy and humanmachine interfaces are identified. Open problems and future research directions towards joint decision making and task execution in human-robot teams are discussed.

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Section: Global and Local Behaviorsmentioning

confidence: 99%

Section: Command Interfacesmentioning

confidence: 99%

“…In [102] the robot team is in charge of coordinating autonomously. Additionally, the robots within the team may perform the local subtask of collision avoidance, see e.g.…”

Section: Control For the Complementary Interactionmentioning

confidence: 99%

“…This is achieved by ensuring that the autonomous task is uncontrollable to the human, see e.g. [102] and [80].…”

Section: Control For the Complementary Interactionmentioning

confidence: 99%

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Control sharing in human-robot team interaction

Musić

Hirche

2017

Annual Reviews in Control

Self Cite

122

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“…The interaction is achieved via a haptic device. Teleoperation of robot teams in which the human operator is not coupled to a haptic device is investigated in [4] and [5] (hand motion tracked with cameras) and in [6] (commands provided via a tablet). However, the feedback provided to the human in this case is visual, which is insufficient when the robots interact with the environment.…”

Section: Introductionmentioning

confidence: 99%

Robot team teleoperation for cooperative manipulation using wearable haptics

Musić

Salvietti

Dohmann

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Robot teams require planning and adaptive capabilities in order to perform cooperative manipulation tasks in dynamic or unstructured environments. Since these capabilities are inherent to humans, it is suitable to consider humanrobot team teleoperation for cooperative manipulation where a single human collaborates with the robot team. In this paper, we present a subtask-based control approach which enables a simultaneous execution of two subtasks by the robot team, interacting with the object: trajectory tracking and formation preservation. Control inputs for both subtasks are provided by the human operator. The commands are projected onto the spaces of subtasks using a command mapping strategy. Analogously, measured interacting forces are projected onto the space of feedback signals, provided to the human via wearable fingertip haptic devices through a feedback mapping strategy. Experimental results validate the proposed approach.

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Task-Space Admittance Controller with Adaptive Inertia Matrix Conditioning

Fonseca

Adorno

Fraisse

2021

J Intell Robot Syst

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Whenrobots physically interact with the environment, compliant behaviors should be imposed to prevent damages to all entities involved in the interaction. Moreover, during physical interactions, appropriate pose controllers are usually based on the robot dynamics, in which the ill-conditioning of the joint-space inertia matrix may lead to poor performance or even instability. When the control is not precise, large interaction forces may appear due to disturbed end-effector poses, resulting in unsafe interactions. To overcome these problems, we propose a task-space admittance controller in which the inertia matrix conditioning is adapted online. To this end, the control architecture consists of an admittance controller in the outer loop, which changes the reference trajectory to the robot end-effector to achieve a desired compliant behavior; and an adaptive inertia matrix conditioning controller in the inner loop to track this trajectory and improve the closed-loop performance. We evaluated the proposed architecture on a KUKA LWR4+ robot and compared it, via rigorous statistical analyses, to an architecture in which the proposed inner motion controller was replaced by two widely used ones. The admittance controller with adaptive inertia conditioning presents better performance than with a controller based on the inverse dynamics with feedback linearization, and similar results when compared to the PID controller with gravity compensation in the inner loop.

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Multi-robot manipulation controlled by a human with haptic feedback

Cited by 19 publications

References 14 publications

Control sharing in human-robot team interaction

Control sharing in human-robot team interaction

Robot team teleoperation for cooperative manipulation using wearable haptics

Task-Space Admittance Controller with Adaptive Inertia Matrix Conditioning

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