Fast orient: lightweight computer vision for wrist control in assistive robotic grasping

Maymo, Mireia Ruiz; Shafti, Ali; Amir, Faisal

doi:10.1109/biorob.2018.8487622

Cited by 5 publications

(5 citation statements)

References 22 publications

(31 reference statements)

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“…For example, a computer vision has been used to enhance the wrist control by using the robotised exoskeleton hands in achieving an assistive robotic grasp. 245 Although an improvement has been made in terms of control, natural reaching and grasping Picking up a wide range of objects [241] Table III. Continued.…”

Section: Assistive and Warehouse Robotsmentioning

confidence: 99%

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Mohammed¹,

Chua²,

Kwek³

2021

Robotica

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SUMMARY Interaction between a robot and its environment requires perception about the environment, which helps the robot in making a clear decision about the object type and its location. After that, the end effector will be brought to the object’s location for grasping. There are many research studies on the reaching and grasping of objects using different techniques and mechanisms for increasing accuracy and robustness during grasping and reaching tasks. Thus, this paper presents an extensive review of research directions and topics of different approaches such as sensing, learning and gripping, which have been implemented within the current five years.

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Section: Assistive and Warehouse Robotsmentioning

confidence: 99%

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Mohammed¹,

Chua²,

Kwek³

2021

Robotica

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“…In addition, Maymo et al in [ 84 ] proposed the FastOrient algorithm for the UR10 Universal Robot to be able to determine the orientation of the target object to be picked up. First, the image is acquired and transformed to grayscale.…”

Section: Algorithms Used For Objectsmentioning

confidence: 99%

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

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Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

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“…Several studies focus on controlling the stability and robustness of hand motion during grasping. For example, computer vision has been used to enhance wrist control in robotized exoskeleton hands to achieve assistive robotic grasping [298]. Although an improvement has been realized in terms of enhancing the controlling part, the challenge of achieving a natural reaching and grasping motion remains.…”

Section: A Humanoid Robotsmentioning

confidence: 99%

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

2020

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The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation. Various studies are examined through a survey of existing literature and investigation of various aspects, namely, the intended applications, techniques applied, challenges faced by researchers and recommendations for minimizing obstacles. This review refers to all relevant articles on deep reinforcement learning-based object manipulation and solutions. The object grasping issue is a major manipulation challenge. Object grasping requires detection systems, methods and tools to facilitate efficient and fast agent training. Several studies have proposed that object grasping and its subtypes are the main elements in dealing with the environment and agent. Unlike other review articles, this review article provides different observations on deep reinforcement learning-based manipulation. The results of this comprehensive review of deep reinforcement learning in the manipulation field may be valuable for researchers and practitioners because they can expedite the establishment of important guidelines.

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Fast orient: lightweight computer vision for wrist control in assistive robotic grasping

Cited by 5 publications

References 22 publications

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

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