Intelligent robotic gripper with adaptive grasping force

Huang, Shiuh‐Jer; Chang, Wei-Han; Su, Jui-Yiao

doi:10.1007/s12555-016-0249-6

Cited by 28 publications

(13 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Functional near-infrared spectroscopy has so far shown great potential for use as a portable device for BCI applications ( Park et al, 2016 ; Yin et al, 2016 ; Chaudhary et al, 2017 ; Huang et al, 2017 ; Sereshken et al, 2017 ). However, the main issue associated using fNIRS signals for BCI applications is the inherent 2 s time delay from the neuronal activation ( Jasdzewski et al, 2003 ; Hong and Nguyen, 2014 ).…”

Section: Role Of the Initial Dip In Bcimentioning

confidence: 99%

Existence of Initial Dip for BCI: An Illusion or Reality

Hong

Zafar

2018

Front. Neurorobot.

View full text Add to dashboard Cite

A tight coupling between the neuronal activity and the cerebral blood flow (CBF) is the motivation of many hemodynamic response (HR)-based neuroimaging modalities. The increase in neuronal activity causes the increase in CBF that is indirectly measured by HR modalities. Upon functional stimulation, the HR is mainly categorized in three durations: (i) initial dip, (ii) conventional HR (i.e., positive increase in HR caused by an increase in the CBF), and (iii) undershoot. The initial dip is a change in oxygenation prior to any subsequent increase in CBF and spatially more specific to the site of neuronal activity. Despite additional evidence from various HR modalities on the presence of initial dip in human and animal species (i.e., cat, rat, and monkey); the existence/occurrence of an initial dip in HR is still under debate. This article reviews the existence and elusive nature of the initial dip duration of HR in intrinsic signal optical imaging (ISOI), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The advent of initial dip and its elusiveness factors in ISOI and fMRI studies are briefly discussed. Furthermore, the detection of initial dip and its role in brain-computer interface using fNIRS is examined in detail. The best possible application for the initial dip utilization and its future implications using fNIRS are provided.

show abstract

Section: Role Of the Initial Dip In Bcimentioning

confidence: 99%

Existence of Initial Dip for BCI: An Illusion or Reality

Hong

Zafar

2018

Front. Neurorobot.

View full text Add to dashboard Cite

show abstract

“…The increasing degree of freedom of a robotic hand by possessing a lot of gears, links, and intelligent algorithm, a robotic hand can achieve dexterous manipulation similar to human—for example, optimization of its orientation, surface shape, and size to a target object for grasping—although more complex control algorithms and force analysis are necessary [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. There are also many choices of actuators such as mechanical, electric motor, pneumatic, hydraulic, and magnetic [ 36 , 37 , 38 , 39 ]. Actuators must be chosen carefully by considering factors such as controllability, size, required grasping force, and so on [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

Grasping Force Control for a Robotic Hand by Slip Detection Using Developed Micro Laser Doppler Velocimeter

Morita

Nogami

Higurashi

et al. 2018

Sensors

View full text Add to dashboard Cite

The purpose of this paper is to show the feasibility of grasping force control by feeding back signals of the developed micro-laser Doppler velocimeter (μ-LDV) and by discriminating whether a grasped object is slipping or not. LDV is well known as a high response surface velocity sensor which can measure various surfaces—such as metal, paper, film, and so on—thus suggesting the potential application of LDV as a slip sensor for grasping various objects. However, the use of LDV as a slip sensor has not yet been reported because the size of LDVs is too large to be installed on a robotic fingertip. We have solved the size problem and enabled the performance of a feasibility test with a few-millimeter-scale LDV referred to as micro-LDV (μ-LDV) by modifying the design which was adopted from MEMS (microelectromechanical systems) fabrication process. In this paper, by applying our developed μ-LDV as a slip sensor, we have successfully demonstrated grasping force control with three target objects—aluminum block, wood block, and white acrylic block—considering that various objects made of these materials can be found in homes and factories, without grasping force feedback. We provide proofs that LDV is a new promising candidate slip sensor for grasping force control to execute target grasping.

show abstract

“…To be truly considered dexterous, robotic systems should have the capacity to do complicated tasks. A small step in the right direction is the ability to prevent objects from slipping and falling [68]. The adjustment of already held objects, such as by using the individual fingers [183], is a required stepping stone to advanced object handling.…”

Section: Open-ended Grasp Learningmentioning

confidence: 99%

“…First, there is a proposed method of adapting to an object which may be slipping from the grip. This is addressed in two studies by Huang et al [68] and Hang et al [57] where the first proposes varying the pressure placed on the object and the latter rather focuses on adding a step for re-positioning fingers on a dexterous gripper in order to attain a better grasp on the object. A separate, more recent study shows the use of reinforcement learning to re-position or re-grip an object in a single dexterous gripper [147].…”

Section: Open-ended Grasp Learningmentioning

confidence: 99%

The State of Lifelong Learning in Service Robots:

Kasaei

Melsen

Beers

et al. 2021

J Intell Robot Syst

View full text Add to dashboard Cite

Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming. In this paper, we review a set of previously published works and discuss advances in service robots from object perception to complex object manipulation and shed light on the current challenges and bottlenecks.

show abstract

Intelligent robotic gripper with adaptive grasping force

Cited by 28 publications

References 14 publications

Existence of Initial Dip for BCI: An Illusion or Reality

Existence of Initial Dip for BCI: An Illusion or Reality

Grasping Force Control for a Robotic Hand by Slip Detection Using Developed Micro Laser Doppler Velocimeter

The State of Lifelong Learning in Service Robots:

Contact Info

Product

Resources

About