Reach-to-Grasp Planning for a Synergy-Controlled Robotic Hand Based on Grasp Quality Prediction

2018 ◽  
Author(s):  
Zenghui Liu ◽  
Zhonghao Wu ◽  
Tianlai Dong ◽  
Xiangyang Zhu ◽  
Kai Xu
Keyword(s):  
Robotic Hand ◽  
Quality Prediction ◽  
Reach To Grasp ◽  
Grasp Planning ◽  
Grasp Quality

Reach to Grasp Planning for a Synergy-Controlled Robotic Hand based on Pesudo-Distance Formulation

2020 ◽  
Vol 17 (05) ◽  
pp. 2050015
Author(s):  
Zenghui Liu ◽  
Yuyang Chen ◽  
Xiangyang Zhu ◽  
Kai Xu
Keyword(s):  
Planning Process ◽  
Quality Measurement ◽  
Robotic Hand ◽  
Reach To Grasp ◽  
Grasp Planning ◽  
Object A ◽  
Position And Orientation ◽  
Low Dimensional ◽  
Power Grasp ◽  
Grasp Quality

In the past several years, grasp analysis of multi-fingered robotic hands has been actively studied through the use of posture synergies. In these grasping planning algorithms, a formulated optimization is usually performed in the hand’s low-dimensional representation together with the hand’s position and orientation. The optimization terminates at a stable grasp, often after repeated trials with different initial guesses. Furthermore, there is no guarantee that the generated grasp leads to a smooth reach-to-grasp trajectory since the grasping planning process mostly concerns hand poses with the fingers proximal to the object. A unified theoretical framework of a gradient-based iterative algorithm is hence proposed in this paper to plan a reach-to-grasp task, predicting the grasp quality and adjusting the hand’s posture synergies, position and orientation during the approaching phase to achieve a stable grasp. The grasp quality measurement is adopted from a highly efficient pseudo-distance formulation. Stable power grasp and precision pinch can be consistently and intentionally planned with different contact conditions specified in the formulation, which means that an intention for planning a power grasp would not generate a pinch result. Several numerical simulation case studies are presented to demonstrate the effectiveness of the proposed algorithm.

Grasp Configurations Optimization of Dexterous Robotic Hand Based on Haptic Exploration Information

2017 ◽  
Vol 14 (04) ◽  
pp. 1750013 ◽  
Author(s):  
Haiwei Gu ◽  
Yuanfei Zhang ◽  
Shaowei Fan ◽  
Minghe Jin ◽  
Hong Liu
Keyword(s):  
Planning Process ◽  
Optimization Method ◽  
Robot Kinematics ◽  
Robotic Hand ◽  
Grasp Planning ◽  
Research Areas ◽  
Haptic Exploration ◽  
Robot Configurations ◽  
Grasp Quality

Haptic exploration and grasp planning by dexterous robot hand are usually two independent research areas. In this paper, the determination of optimal grasp configurations after haptic exploration of unknown objects is discussed. The haptic exploration information is used to select initial grasp points and the corresponding robot configurations, which greatly improve the efficiency of grasp planning process. The feasible searching regions on the object are obtained under the constrains of manipulability and robot kinematics. Then, the optimization method based on KNN search is applied to find the optimal grasp positions in feasible searching regions. The selected optimal grasp points set can achieve high grasp quality under the constrains of robot kinematics and manipulability. The optimization method combines multiple grasp quality metrics, which is fast and feasible in optimal grasp points searching. Experiments validate the feasibility and effectivity of the proposed method.

Visual Grasp Planning for Unknown Objects Using a Multifingered Robotic Hand

2013 ◽  
Vol 18 (3) ◽  
pp. 1050-1059 ◽  
Author(s):  
Vincenzo Lippiello ◽  
Fabio Ruggiero ◽  
Bruno Siciliano ◽  
Luigi Villani
Keyword(s):  
Robotic Hand ◽  
Grasp Planning ◽  
Unknown Objects

scholarly journals Corticospinal excitability underlying digit force planning for grasping in humans

2014 ◽  
Vol 111 (12) ◽  
pp. 2560-2569 ◽  
Author(s):  
Pranav Parikh ◽  
Marco Davare ◽  
Patrick McGurrin ◽  
Marco Santello
Keyword(s):  
Primary Motor Cortex ◽  
Single Pulse ◽  
Intracortical Inhibition ◽  
Corticospinal Excitability ◽  
Reach To Grasp ◽  
Grasp Planning ◽  
Sensorimotor Memory ◽  
Force Planning ◽  
Intracortical Inhibition And Facilitation

Control of digit forces for grasping relies on sensorimotor memory gained from prior experience with the same or similar objects and on online sensory feedback. However, little is known about neural mechanisms underlying digit force planning. We addressed this question by quantifying the temporal evolution of corticospinal excitability (CSE) using single-pulse transcranial magnetic stimulation (TMS) during two reach-to-grasp tasks. These tasks differed in terms of the magnitude of force exerted on the same points on the object to isolate digit force planning from reach and grasp planning. We also addressed the role of intracortical circuitry within primary motor cortex (M1) by quantifying the balance between short intracortical inhibition and facilitation using paired-pulse TMS on the same tasks. Eighteen right-handed subjects were visually cued to plan digit placement at predetermined locations on the object and subsequently to exert either negligible force (“low-force” task, LF) or 10% of their maximum pinch force (“high-force” task, HF) on the object. We found that the HF task elicited significantly smaller CSE than the LF task, but only when the TMS pulse coincided with the signal to initiate the reach. This force planning-related CSE modulation was specific to the muscles involved in the performance of both tasks. Interestingly, digit force planning did not result in modulation of M1 intracortical inhibitory and facilitatory circuitry. Our findings suggest that planning of digit forces reflected by CSE modulation starts well before object contact and appears to be driven by inputs from frontoparietal areas other than M1.

Automated Design of Robotic Hands for In-Hand Manipulation Tasks

2020 ◽  
Vol 17 (01) ◽  
pp. 1950029
Author(s):  
Christopher Hazard ◽  
Nancy Pollard ◽  
Stelian Coros
Keyword(s):  
Kinematic Model ◽  
Automated Design ◽  
Robotic Hand ◽  
Motion Synthesis ◽  
Robotic Hands ◽  
Dexterous Manipulation ◽  
Grasp Planning ◽  
User Input ◽  
High Level

Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this paper, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high-level objectives as input. Our pipeline is capable of building custom hand designs around specific manipulation tasks based on high-level user input. Our framework comprises of a sequence of trajectory optimizations chained together to translate a sequence of objective poses into an optimized hand mechanism along with a physically feasible motion plan involving both the constructed hand and the object. We demonstrate the feasibility of this approach by synthesizing a series of hand designs optimized to perform specified in-hand manipulation tasks of varying difficulty. We extend our original pipeline 32 to accommodate the construction of hands suitable for multiple distinct manipulation tasks as well as provide an in depth discussion of the effects of each non-trivial optimization term.

Optimal grasp force for robotic grasping and in-hand manipulation with impedance control

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoqing Li ◽  
Ziyu Chen ◽  
Chao Ma
Keyword(s):  
Degrees Of Freedom ◽  
Impedance Control ◽  
Difficult Problem ◽  
Contact Forces ◽  
Design Parameters ◽  
Robotic Hand ◽  
Content Type ◽  
Cone Constraint ◽  
Grasp Force ◽  
Grasp Quality

Purpose The purpose of this paper is to achieve stable grasping and dexterous in-hand manipulation, the control of the multi-fingered robotic hand is a difficult problem as the hand has many degrees of freedom with various grasp configurations. Design/methodology/approach To achieve this goal, a novel object-level impedance control framework with optimized grasp force and grasp quality is proposed for multi-fingered robotic hand grasping and in-hand manipulation. The minimal grasp force optimization aims to achieve stable grasping satisfying friction cone constraint while keeping appropriate contact forces without damage to the object. With the optimized grasp quality function, optimal grasp quality can be obtained by dynamically sliding on the object from initial grasp configuration to final grasp configuration. By the proposed controller, the in-hand manipulation of the grasped object can be achieved with compliance to the environment force. The control performance of the closed-loop robotic system is guaranteed by appropriately choosing the design parameters as proved by a Lyapunove function. Findings Simulations are conducted to validate the efficiency and performance of the proposed controller with a three-fingered robotic hand. Originality/value This paper presents a method for robotic optimal grasping and in-hand manipulation with a compliant controller. It may inspire other related researchers and has great potential for practical usage in a widespread of robot applications.

scholarly journals Optimal grasp planning for a dexterous robotic hand using the volume of a generalized force ellipsoid during accepted flattening

2017 ◽  
Vol 14 (1) ◽  
pp. 172988141668713 ◽  
Author(s):  
Peng Jia ◽  
Wei li Li ◽  
Gang Wang ◽  
Song Yu Li
Keyword(s):  
Simulation Analysis ◽  
Internal Force ◽  
Planning Method ◽  
Robotic Hand ◽  
Grasp Planning ◽  
Joint Torques ◽  
Contact Points ◽  
Generalized Force ◽  
Internal Forces ◽  
Dexterous Hand

A grasp planning method based on the volume and flattening of a generalized force ellipsoid is proposed to improve the grasping ability of a dexterous robotic hand. First, according to the general solution of joint torques for a dexterous robotic hand, a grasping indicator for the dexterous hand—the maximum volume of a generalized external force ellipsoid and the minimum volume of a generalized contact internal force ellipsoid during accepted flattening—is proposed. Second, an optimal grasp planning method based on a task is established using the grasping indicator as an objective function. Finally, a simulation analysis and grasping experiment are performed. Results show that when the grasping experiment is conducted with the grasping configuration and positions of contact points optimized using the proposed grasping indicator, the root-mean-square values of the joint torques and contact internal forces of the dexterous hand are at a minimum. The effectiveness of the proposed grasping planning method is thus demonstrated.

The Influence of the Abduction Joints of Four Fingers to Grasp: Experimental and Simulated Verification

2021 ◽  
Vol 11 (24) ◽  
pp. 11960
Author(s):  
Yadong Yan ◽  
Chang Cheng ◽  
Mingjun Guan ◽  
Jianan Zhang ◽  
Yu Wang
Keyword(s):  
Success Rate ◽  
Hand Function ◽  
Great Influence ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Robotic Hand ◽  
Metacarpophalangeal Joints ◽  
Flexion Extension ◽  
Grasp Force ◽  
Grasp Quality

The thumb is the most important finger of the human hand and has a great influence on grasp manipulations. However, the extent to which joints other than the thumb joints affect the grasp, and thus, which joints should be included in a prosthetic hand, remains an open issue. In this paper, we focus on the metacarpophalangeal joints of the four fingers, except the thumb, which can generate flexion/extension and abduction/adduction motions. The contribution of these joints to grasping was evaluated in four aspects: grasp size, grasp force, grasp quality and grasp success rate. Six subjects participated in experiments with respect to the maximum grasp size and grasp force. The results show that possessing abduction mobility of the metacarpophalangeal joints can increase the grasp size by 4.67 ± 1.93 mm and the grasp force by 5.27 ± 4.25 N. Then, the grasping quality and success rate were tested in a simulation platform and using a robotic hand, respectively. The results show that grasp quality was promoted by 76.7% in the simulated environment with abduction mobility compared to without abduction mobility, whereas the grasp success rate was promoted by 68.3%. We believe that the results of this work can benefit the understanding of hand function and prosthetic hand design.

Sign in / Sign up

Export Citation Format

Share Document