Design and Evaluation of Anthropomorphic Robotic Hand for Object Grasping and Shape Recognition

Devaraja, Rahul Raj; Maskeliūnas, Rytis; Damaševičius, Robertas

doi:10.3390/computers10010001

Cited by 22 publications

(6 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the shape-based retrieval methods [30,31] researchers only concern is for shapes with similar contour features, while the position, size, and rotation information of objects are not important. In order to make model and data shape retrieval consistent, the extracted shape descriptor must have translation, rotation, and scale invariance.…”

Section: Normalization Of the Fourier Transform For The Shapementioning

confidence: 99%

Fast Shape Recognition via the Restraint Reduction of Bone Point Segment

Guo

Meng

2022

Symmetry

View full text Add to dashboard Cite

In computer science, and especially in computer vision, the contour of an object is used to describe its features; thus, the shape descriptor plays an indispensable role in target detection and recognition. Further, Fourier is an important mathematical description method, and the Fourier transform of a shape contour has symmetry. This paper will demonstrate the symmetry of shape contour in the frequency domain. In recent years, increasing numbers of shape descriptors have come to the fore, but many descriptors ignore the details of shape. It is found that the most fundamental reason affecting the performance of shape descriptors is structural restraints, especially feature structure restraint. Therefore, in this paper, the restraint of feature structure that intrinsically deteriorates recognition performance is shown, and a fast shape recognition method via the Bone Point Segment (BPS) restraint reduction is proposed. An approach using the inner distance to find bone shapes and segment the shape contour by these bones is proposed. Then, Fourier transform is performed on each segment to form the shape feature. Finally, the restraints of the shape feature are reduced in order to build a more effective shape feature. What is commendable is that its discriminability and robustness is strong, the process is simple, and matching speed is fast. More importantly, the experiment results show that the shape descriptor has higher recognition accuracy and the matching speed runs up to more than 1000 times faster than the existing description methods like CBW and TCD. More importantly, it is worth noting that the recognition accuracy approaches 100% in the self-build dataset.

show abstract

Section: Normalization Of the Fourier Transform For The Shapementioning

confidence: 99%

Fast Shape Recognition via the Restraint Reduction of Bone Point Segment

Guo

Meng

2022

Symmetry

View full text Add to dashboard Cite

show abstract

“…An accurate grasping force like a human finger cannot be achieved, although plenty of decoding methods were being used [15] and tested to prevent slippage of the grasped object. Even if this decoding method feedback can simulate a real-human hand [16], the time delay of grasping an object would be longer than the nature hand [17]. Although there are many prosthetics available, there is still a significant gap compared to human hands due to the absence of a feedback system [18] in prostheses compared to humans, where it has numerous sensory receptors that as feedback to the central nervous system [19] while the prostheses only operate in open loop system without feedback.…”

Section: Introductionmentioning

confidence: 99%

3D Printed Prosthetic Robot Arm with Grasping Detection System for Children

Babu¹,

Nasır²,

Ravindran³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

Prosthetic robot arms have been an alternative replacement for upper limb-related disability, especially among children as it helps them perform regular activities such as holding tools, eating, and drinking. This research aims to develop a 3D prosthetic robot arm using SolidWorks and 3D printer, using a low-cost and straightforward mechanism for children. This paper proposes a close loop control system with position control for the prosthetic robot arm to achieve an appropriate grasping force focusing on solid-shaped objects using PID control. The PID controller controls the system's response to perform in the most efficient path. Force-sensitive resistors (FSR) are attached to all fingers to measure the grasping force acting on objects with different surfaces, dimensions, and weights. The controller results showed improvement in the overshoot percentage of 0.902%, as overshoot is essential in preventing the grasped object's deformations. The analysis of the experiment shows that the mean grasping force and static coefficient friction of each object are different regardless of the material the object is made of and the object's mass. For example, a cube-shaped object made of wood requires 0.5288 N of grasping force to grasp the object firmly compared to a plastic-made cube that only requires 0.3245 N to hold the cube. On the other hand, the static coefficient friction for the wood cube is 3.1708 and 0.4725 for the plastic cube. Further research can be done by designing the prosthetic robot arm with independent motorized and multi-degree movement of fingers.

show abstract

“…Focusing on tactile sensations, several studies tried to reproduce the properties of human skin endowing the device with tactile sensing technologies that typically requires cumbersome add-on like sensing skin with different kinds of sensors such as piezoresistive (Osborn et al, 2018 ), capacitive (Cannata et al, 2008 ), piezoelectric (Yi and Zhang, 2016 ), and also optical (Zhao et al, 2016 ). The measurements acquired by these tactile sensors are often given as input to machine learning algorithms, which extract useful information that may be conveyed to the prosthesis users, as described by Jamali and Sammut ( 2011 ); Liarokapis et al ( 2015 ); Konstantinova et al ( 2017 ); Devaraja et al ( 2020 ); Huang and Rosendo ( 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach

et al. 2023

View full text Add to dashboard Cite

IntroductionIn recent years, hand prostheses achieved relevant improvements in term of both motor and functional recovery. However, the rate of devices abandonment, also due to their poor embodiment, is still high. The embodiment defines the integration of an external object – in this case a prosthetic device – into the body scheme of an individual. One of the limiting factors causing lack of embodiment is the absence of a direct interaction between user and environment. Many studies focused on the extraction of tactile information via custom electronic skin technologies coupled with dedicated haptic feedback, though increasing the complexity of the prosthetic system. Contrary wise, this paper stems from the authors' preliminary works on multi-body prosthetic hand modeling and the identification of possible intrinsic information to assess object stiffness during interaction.MethodsBased on these initial findings, this work presents the design, implementation and clinical validation of a novel real-time stiffness detection strategy, without ad-hoc sensing, based on a Non-linear Logistic Regression (NLR) classifier. This exploits the minimum grasp information available from an under-sensorized and under-actuated myoelectric prosthetic hand, Hannes. The NLR algorithm takes as input motor-side current, encoder position, and reference position of the hand and provides as output a classification of the grasped object (no-object, rigid object, and soft object). This information is then transmitted to the user via vibratory feedback to close the loop between user control and prosthesis interaction. This implementation was validated through a user study conducted both on able bodied subjects and amputees.ResultsThe classifier achieved excellent performance in terms of F1Score (94.93%). Further, the able-bodied subjects and amputees were able to successfully detect the objects' stiffness with a F1Score of 94.08% and 86.41%, respectively, by using our proposed feedback strategy. This strategy allowed amputees to quickly recognize the objects' stiffness (response time of 2.82 s), indicating high intuitiveness, and it was overall appreciated as demonstrated by the questionnaire. Furthermore, an embodiment improvement was also obtained as highlighted by the proprioceptive drift toward the prosthesis (0.7 cm).

show abstract

Design and Evaluation of Anthropomorphic Robotic Hand for Object Grasping and Shape Recognition

Cited by 22 publications

References 50 publications

Fast Shape Recognition via the Restraint Reduction of Bone Point Segment

Fast Shape Recognition via the Restraint Reduction of Bone Point Segment

3D Printed Prosthetic Robot Arm with Grasping Detection System for Children

Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach

Contact Info

Product

Resources

About