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.

scholarly journals Grasping Assisting Algorithm in Tele-Operated Robotic Gripper

2021 ◽  
Vol 11 (6) ◽  
pp. 2640
Author(s):  
Tomer Fine ◽  
Guy Zaidner ◽  
Amir Shapiro
Keyword(s):  
State Of The Art ◽  
Human Subjects ◽  
Quality Measures ◽  
Human Operator ◽  
Physical Presence ◽  
Complete State ◽  
The Future ◽  
Power Grasp ◽  
Grasp Quality

The involvement of Robots and automated machines in different industries has increased drastically in recent years. Part of this revolution is accomplishing tasks previously performed by humans with advanced robots, which would replace the entire human workforce in the future. In some industries the workers are required to complete different operations in hazardous or difficult environments. Operations like these could be replaced with the use of tele-operated systems that have the capability of grasping objects in their surroundings, thus abandoning the need for the physical presence of the human operator at the area while still allowing control. In this research our goal is to create an assisting system that would improve the grasping of a human operator using a tele-operated robotic gripper and arm, while advising the operator but not forcing a solution. For a given set of objects we computed the optimal grasp to be achieved by the gripper, based on two grasp quality measures of our choosing (namely power grasp and precision grasp). We then tested the performance of different human subjects who tried to grasp the different objects with the tele-operated system, while comparing their success to unassisted and assisted grasping. Our goal is to create an assisting algorithm that would compute optimal grasps and might be integrated into a complete, state-of-the-art tele-operated system.

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.

scholarly journals Neural coordination during reach-to-grasp

2015 ◽  
Vol 114 (3) ◽  
pp. 1827-1836 ◽  
Author(s):  
Mukta Vaidya ◽  
Konrad Kording ◽  
Maryam Saleh ◽  
Kazutaka Takahashi ◽  
Nicholas G. Hatsopoulos
Keyword(s):  
Cortical Neurons ◽  
Coordination Dynamics ◽  
Reach To Grasp ◽  
Neuronal Ensembles ◽  
Reach And Grasp ◽  
Dimensional State Space ◽  
Catch Up ◽  
Low Dimensional ◽  
Bidirectional Interactions ◽  
New Framework

When reaching to grasp, we coordinate how we preshape the hand with how we move it. To ask how motor cortical neurons participate in this coordination, we examined the interactions between reach- and grasp-related neuronal ensembles while monkeys reached to grasp a variety of different objects in different locations. By describing the dynamics of these two ensembles as trajectories in a low-dimensional state space, we examined their coupling in time. We found evidence for temporal compensation across many different reach-to-grasp conditions such that if one neural trajectory led in time the other tended to catch up, reducing the asynchrony between the trajectories. Granger causality revealed bidirectional interactions between reach and grasp neural trajectories beyond that which could be attributed to the joint kinematics that were consistently stronger in the grasp-to-reach direction. Characterizing cortical coordination dynamics provides a new framework for understanding the functional interactions between neural populations.

Botulinum Toxin to Treat Upper-Limb Spasticity in Hemiparetic Patients: Grasp Strategies and Kinematics of Reach-to-Grasp Movements

2009 ◽  
Vol 24 (2) ◽  
pp. 141-151 ◽  
Author(s):  
Djamel Bensmail ◽  
Johanna Robertson ◽  
Christophe Fermanian ◽  
Agnès Roby-Brami
Keyword(s):  
Botulinum Toxin ◽  
Upper Limb ◽  
Motor Command ◽  
Sensory Loss ◽  
Functional Improvement ◽  
Hand Position ◽  
Reach To Grasp ◽  
Upper Limb Function ◽  
Position And Orientation ◽  
Post Hoc

Background. Poor control of grasping in spastic, hemiparetic patients could be because of a combination of poor individuation of joints, weakness, spasticity, and sensory loss. Objective. To investigate the effect of botulinum toxin injections (BTIs) on grasping objects of different shapes and to assess the effect on upper-limb function, reach-to-grasp kinematics, and hand position and orientation at the time of grasp. Methods. We included 15 patients with spastic hemiparesis and 9 healthy controls in this open labeled study, in which the patients were assessed before (M0), 1 month after a first (M1), and 1 month after a second BTI (M4, at 4 months). A motion capture system recorded movements. Kinematic variables were computed as well as hand position and orientation at the time of grasping, and finger configurations were coded from video recordings. Results. In contrast with healthy participants, hemiparetic patients rarely used multipulpar grasps but used specific strategies combined with various directions of approach to the object. BTIs did not alter finger configuration but improved the final direction of the approach and the hand posture during the grasp. No significant changes in kinematic parameters were found using post hoc analysis, although a session effect was found for peak hand velocity. Individual analysis showed that the patients with the best potential for functional improvement are those with good proximal and moderate distal motor command. Conclusions. BTIs can modify hand kinematics as well as the approach and posture of reach-to-grasp movements. Function and grasping strategies are probably more dependent on motor recovery.

Actuator Array Manipulation Using Low Resolution Local Sensing

2014 ◽  
Author(s):  
Deepak Parajuli ◽  
Mark D. Bedillion ◽  
Randy C. Hoover
Keyword(s):  
Degrees Of Freedom ◽  
Accurate Method ◽  
Distributed Sensing ◽  
Object A ◽  
Distributed Manipulation ◽  
Simulink Model ◽  
Manipulation System ◽  
Position And Orientation ◽  
To Come ◽  
Two Degree Of Freedom

An actuator array is a planar distributed manipulation system that uses multiple two degree-of-freedom actuators to manipulate objects with three degrees of freedom (x, y and θ). This paper presents an accurate method of estimating position and orientation of an object using local sensing and communication. In this method, each of the distributed modules contains a number of binary sensors, weight sensors, and two planar actuators. The binary sensors combined together give a binary image and analog sensors in each module combined together form a grayscale image representation of the weight distribution of the object under manipulation. Additive normalization has been used to combine binary and grayscale distributed sensing images together to come up with increased precision estimates of the position and orientation of an object. A distributed sensing simulation has been developed in Simulink and the effectiveness of this method has been verified for rectangular and circular objects using the Simulink model.

scholarly journals Electromyography Classification during Reach-to-Grasp Motion using Manifold Learning

2020 ◽  
Author(s):  
Elnaz Lashgari ◽  
Uri Maoz
Keyword(s):  
Dimensionality Reduction ◽  
Cost Effective ◽  
Human Hand ◽  
Reach To Grasp ◽  
K Nearest Neighbors ◽  
Laplacian Eigenmaps ◽  
Non Invasive ◽  
Reduction Methods ◽  
Machine Interface ◽  
Low Dimensional

AbstractElectromyography (EMG) is a simple, non-invasive, and cost-effective technology for sensing muscle activity. However, EMG is also noisy, complex, and high-dimensional. It has nevertheless been widely used in a host of human-machine-interface applications (electrical wheelchairs, virtual computer mice, prosthesis, robotic fingers, etc.) and in particular to measure reaching and grasping motions of the human hand. Here, we developd a more automated pipeline to predict object weight in a reach-and-grasp task from an open dataset relying only on EMG data. In that we shifted the focus from manual feature-engineering to automated feature-extraction by using raw (filtered) EMG signals and thus letting the algorithms select the features. We further compared intrinsic EMG features, derived from several dimensionality-reduction methods, and then ran some classification algorithms on these low-dimensional representations. We found that the Laplacian Eigenmap algorithm generally outperformed other dimensionality-reduction methods. What is more, optimal classification accuracy was achieved using a combination of Laplacian Eigenmaps (simple-minded) and k-Nearest Neighbors (88% for 3-way classification). Our results, using EMG alone, are comparable to others in the literature that used EMG and EEG together. They also demonstrate the usefulness of dimensionality reduction when classifying movement based on EMG signals and more generally the usefulness of EMG for movement classification.

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