Wheelchair-mounted robotic arms: analysis, evaluation and development

Alqasemi, Redwan; McCaffrey, E.J.; Edwards, K.; Dubey, Rajiv

doi:10.1109/aim.2005.1511167

Cited by 7 publications

(1 citation statement)

References 1 publication

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“…Cameras appear to be the most used technology since they provided the majority of the information available. Furthermore, the most popular schemes of this stream are the Manus, Raptor [8], FRIEND IT [9] and the VICTORIA project [10]. The most advanced assistive robots belong to the category of mobile platforms, where the robotic arm is mounted onto an autonomous vehicle.…”

Section: Introductionmentioning

confidence: 99%

Guiding a robotic gripper by visual feedback for object manipulation tasks

Kouskouridas

Amanatiadis

Γαστεράτος

2011

2011 IEEE International Conference on Mechatronics

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This paper presents a novel object manipulation technique that could be adopted by any advanced mechatronic platform in order to perform demanding pick and place tasks.The ultimate goal of a robotics researcher is to provide an appli cable manipulation solution that minimizes user's involvement.It has been shown that the best solution to this problem is provided by the introduction of sensors that allow an automatic or, at least, semi-automatic grasping of the targets. The proposed method relies on a vision-based framework that is responsible for several vital tasks that affect directly the manipulation process. The contribution of the paper incorporates a shape retrieval technique accompanied with classification and clustering algorithms that are utilized during the objects' pose estimation process. The experimental results obtained confirm the validity of the presented approach.

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Section: Introductionmentioning

confidence: 99%

Guiding a robotic gripper by visual feedback for object manipulation tasks

Kouskouridas

Amanatiadis

Γαστεράτος

2011

2011 IEEE International Conference on Mechatronics

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Design and Evaluation of a Hair Combing System Using a General-Purpose Robotic Arm

Shin¹,

Matarić

Nikolaidis

2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Adaptive training programs are crucial for recovery post stroke. However, developing programs that automatically adapt depends on quantifying how difficult a task is for a specific individual at a particular stage of their recovery. In this work, we propose a method that automatically generates regions of different task difficulty levels based on an individual's performance. We show that this technique explains the variance in user performance for a reaching task better than previous approaches to estimating task difficulty.

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