Software Module for Objects Shape Tracing and Recognition before Gripping

Stareţu, Ionel; Itu, Alexandru

doi:10.4028/www.scientific.net/amr.463-464.1165

Cited by 10 publications

(14 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The requirement for flexible gripper is the ability to make safety grasps, detect and recognize objects [9,10]. Their modalities of applications differ highly from conventional grippers since conventional grippers are equipped in a domestic environment and are usually not intended for repetitive tasks that require high precision or strong force.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

Petković

Shamshirband

Anuar

et al. 2015

J Intell Robot Syst

View full text Add to dashboard Cite

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult make decision strategies using conventional techniques. Here, an adaptive neuro fuzzy inference system (ANFIS) for controlling input displacement and object recognition of a new adaptive compliant gripper is presented. The grasping function of the proposed adaptive multi-fingered gripper relies on the physical contact of the finger with an object. This design of the each finger has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Fuzzy based controllers develop a control signal according to grasping object shape which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS strategy, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.

show abstract

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

Petković

Shamshirband

Anuar

et al. 2015

J Intell Robot Syst

View full text Add to dashboard Cite

show abstract

“…The degree of mobility M = M I + M II − f C = 1 + 1 − 1 = 1 results for the whole of a gripping module. 19,20…”

Section: The Main Stages Of the Gripper Designmentioning

confidence: 99%

Innovative self-adaptive gripper design, functional simulation, and testing prototype

Frincu

Scârneciu

Stareţu

2022

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

This article presents the design, functional simulation, and prototype of an innovative adaptive jaw gripper. First, based on the comparative analysis of several types of anthropomorphic finger grippers and adaptive jaw grippers, to avoid their disadvantages, the structural scheme of a gripper module based on a polycontour mechanism, comprising a guided parallelogram contour, was established to obtain a parallel translational movement of the elements of the jaw holders and therefore of the jaws. Then the structural analysis is briefly made to verify the correct operation of the mechanism of the gripping module, and details of the kinematic analysis and of the design of the components in the CATIA software are given. After obtaining the 3D version of the gripping module, its functional simulation and ADAMS analysis is performed. The sensory system used at the level of the jaws is also described and then the gripper assembly is obtained including a base plate and five gripper modules and as a result an adaptive gripper with five jaw holder elements is created. Next is the functional simulation of the adaptive gripper for gripping several types of parts. The prototype made and the test are presented for gripping five types of parts and we show the prospects of continuing this research with practical applicability by mounting on a robot and implementing in a robotic line for gripping and handling a series of parts of various shapes and sizes.

show abstract

“…This research highlighted the fact that each anthropomorphic gripper comprises five types of components [15]. Each component's peculiarities can affect the anthropomorphic gripper performance [16–18]. In terms of functionality, the components are classified as: transmission systems used to transmit power from an actuator at a specific distance; actuator mechanisms that are used for the precise control of angular position, velocity and acceleration of phalanges [19,20], converting actuator movement into mechanical energy, thereby inducing the phalange's movement; joints and connections that are the constituents of each phalanx; sensors that are devices attached to the gripper, endowing it with sense functions (for example, a pressure sensor attached to each phalanx, allowing it to send data about the force applied by each phalanx on an object); and motion control modules used to send and translate information received from the computer to each actuator.…”

Section: The Prototypementioning

confidence: 99%

Leap Motion Device Used to Control a Real Anthropomorphic Gripper

Stareţu

Moldovan

2016

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

This paper presents for the first time the use of the Leap Motion device to control an anthropomorphic gripper with five fingers. First, a description of the Leap Motion device is presented, highlighting its main functional characteristics, followed by testing of its use for capturing the movements of a human hand's fingers in different configurations. Next, the HandCommander soft module and the Interface Controller application are described. The HandCommander is a software module created to facilitate interaction between a human hand and the GraspIT virtual environment, and the Interface Controller application is required to send motion data to the virtual environment and to test the communication protocol. For the test, a prototype of an anthropomorphic gripper with five fingers was made, including a proper hardware system of command and control, which is briefly presented in this paper. Following the creation of the prototype, the command system performance test was conducted under real conditions, evaluating the recognition efficiency of the objects to be gripped and the efficiency of the command and control strategies for the gripping process. The gripping test is exemplified by the gripping of an object, such as a screw spanner. It was found that the command system, both in terms of capturing human hand gestures with the Leap Motion device and effective object gripping, is operational. Suggestive figures are presented as examples.

show abstract

Software Module for Objects Shape Tracing and Recognition before Gripping

Cited by 10 publications

References 2 publications

RETRACTED ARTICLE: Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

RETRACTED ARTICLE: Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

Innovative self-adaptive gripper design, functional simulation, and testing prototype

Leap Motion Device Used to Control a Real Anthropomorphic Gripper

Contact Info

Product

Resources

About