Biomimetic joint/task space hybrid adaptive control for bimanual robotic manipulation

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“…The object has been modeled as a massspring-damper system. The dynamics model of the object are formulated according to (7) [23]:…”

Section: B Dual Arm Manipulation Using Image-based Visual Controlmentioning

confidence: 99%

Autonomous vision-guided bi-manual grasping and manipulation

Rastegarpanah

Marturi

2017

2017 IEEE Workshop on Advanced Robotics and Its Social Impacts (ARSO)

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“…Here, we analyze the characteristics and limitations of controllers defined in two commonly used formulations: joint and endpoint space, exploring especially the implementation of AAN controllers in these two spaces. A comparative analysis of these two approaches has been reported for industrial manipulators (Smith et al, 2014(Smith et al, , 2015 but, to the best of our knowledge, such comparison has not been extensively examined within the context of rehabilitation robotics and even less in lower limb applications.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive and Hybrid End-Point/Joint Impedance Controller for Lower Limb Exoskeletons

2018

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Assist-as-needed (AAN) algorithms for the control of lower extremity rehabilitation robots can promote active participation of patients during training while adapting to their individual performances and impairments. The implementation of such controllers requires the adaptation of a control parameter (often the robot impedance) based on a performance (or error) metric. The choice of how an adaptive impedance controller is formulated implies different challenges and possibilities for controlling the patient's leg movement. In this paper, we analyze the characteristics and limitations of controllers defined in two commonly used formulations: joint and end-point space, exploring especially the implementation of an AAN algorithm. We propose then, as a proof-of-concept, an AAN impedance controller that combines the strengths of working in both spaces: a hybrid joint/end-point impedance controller. This approach gives the possibility to adapt the end-point stiffness in magnitude and direction in order to provide a support that targets the kinematic deviations of the end-point with the appropriate force vector. This controller was implemented on a two-link rehabilitation robot for gait training—the Lokomat®Pro V5 (Hocoma AG, Switzerland) and tested on 5 able-bodied subjects and 1 subject with Spinal Cord Injury. Our experiments show that the hybrid controller is a feasible approach for exoskeleton devices and that it could exploit the benefits of the end-point controller in shaping a desired end-point stiffness and those of the joint controller to promote the correct angular changes in the trajectories of the joints. The adaptation algorithm is able to adapt the end-point stiffness based on the subject's performance in different gait phases, i.e., the robot can render a higher stiffness selectively in the direction and gait phases where the subjects perform with larger kinematic errors. The proposed approach can potentially be generalized to other robotic applications for rehabilitation or assistive purposes.

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Intelligent Control of Robot Manipulator

Yang

2016

Advanced Technologies in Modern Robotic Applications

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Biomimetic joint/task space hybrid adaptive control for bimanual robotic manipulation

Cited by 8 publications

References 15 publications

Autonomous vision-guided bi-manual grasping and manipulation

Autonomous vision-guided bi-manual grasping and manipulation

An Adaptive and Hybrid End-Point/Joint Impedance Controller for Lower Limb Exoskeletons

Intelligent Control of Robot Manipulator

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