Control algorithm of dual arms mobile robot for cooperative works with human

Kosuge, Kazuhiro; Kakuya, Hiromu; Hirata, Yasuhisa

doi:10.1109/icsmc.2001.972015

Cited by 18 publications

(11 citation statements)

References 8 publications

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“…Classically, works in human-robot co-manipulation consider the robot as a passive agent taking care of load compensation and/or stabilization while the human acts as a leader for planning and guiding the cooperative task. A common approach has been to use different types of impedance control with the main goal of improving the safety [1]- [3].…”

Section: Related Workmentioning

confidence: 99%

“…Traditionally, this has been done by utilizing different types of impedance or admittance controllers [1]- [3] that decrease the appearant mass of the object, so that the human can move it freely by applying forces for translation and torques for rotation. However, with large or massive objects, that the human may possibly only be able grasp with one hand, there may be significant limits to the amount of torque that the human can apply directly to the object, in practice only allowing the human to robustly control the applied force.…”

Section: Introductionmentioning

confidence: 99%

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Mapping human intentions to robot motions via physical interaction through a jointly-held object

Karayiannidis

Smith

Kragić

2014

The 23rd IEEE International Symposium on Robot and Human Interactive Communication

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping human intentions to robot motions via physical interaction through a jointly-held object

Karayiannidis

Smith

Kragić

2014

The 23rd IEEE International Symposium on Robot and Human Interactive Communication

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“…However, it only considered the kinematic modelling and control of the system. Kosuge et al studied a control algorithm for a mobile manipulator with dual arms for cooperative work with humans [10]. However, it controlled the mobile base and dual arms separately rather than considering them as one system.…”

Section: Introductionmentioning

confidence: 99%

Design of Robotic Human Assistance Systems Using a Mobile Manipulator

Jia

Liu

et al. 2012

International Journal of Advanced Robotic Systems

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Robotic systems have been widely used in many areas to assist human beings. Mobile manipulators are among the most popular choices. This paper investigates human assistance systems using a mobile manipulator, for example, to guide the blind and to transport objects. Distinct from existing systems, an integrated dynamic model and controller of the mobile manipulator are designed. Singularity, manipulability and safety are all considered in the system design. Furthermore, two human assistance modes -Robot-Human mode and TeleoperatorRobot-Human mode -are designed and analysed. The Teleoperator-Robot-Human mode can integrate human intelligence into the assistance system to further enhance the system efficiency and safety. The experimental results implemented on a mobile manipulator demonstrated the effectiveness of the designed systems.

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“…Thus, the robot requires accurate knowledge of the human's grasp pose. Conventional approaches in physical HRI assume their relative grasp kinematics is known [1], [3], [5]. This is clearly an oversimplification since the human might not grasp the object at a predefined location or the human might regrasp it during the task.…”

Section: Introductionmentioning

confidence: 99%

Grasp pose estimation in human-robot manipulation tasks using wearable motion sensors

Cehajic

Erhart

Hirche

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Knowledge of the human grasp pose is crucial in common control schemes for human-robot object manipulation tasks. Biased estimates of the grasp pose cause undesired interaction wrenches on the human partner, which disturbs the interaction and the recognition of motion intention. A use of wearable motion sensors for tracking the human motion facilitates the grasp pose estimation without a global sensing system. This paper presents an approach for estimating an unknown grasp pose of the human using wearable motion sensors while minimizing undesired interaction wrenches applied to the human. A condition necessary for convergence of the estimator together with appropriate robot motion strategies are provided. Estimation of relative orientation and displacement is performed online and based on minimizing the error in the least-square sense. The estimation process does not rely on a global sensing system and it considers only the measurements of the velocity and acceleration of the cooperating partners in their respective local frames. The approach is experimentally evaluated in a physical human-robot interaction scenario.

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Control algorithm of dual arms mobile robot for cooperative works with human

Cited by 18 publications

References 8 publications

Mapping human intentions to robot motions via physical interaction through a jointly-held object

Mapping human intentions to robot motions via physical interaction through a jointly-held object

Design of Robotic Human Assistance Systems Using a Mobile Manipulator

Grasp pose estimation in human-robot manipulation tasks using wearable motion sensors

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