Examining the Robustness of Grasping Force Optimization Methods Using Uncertainty Analysis

2018 ◽  
Vol 4 (4) ◽  
Author(s):  
Aimee Cloutier ◽  
James Yang
Keyword(s):  
Center Of Mass ◽  
Linear Method ◽  
Optimization Methods ◽  
Linear Matrix ◽  
Human Hand ◽  
Nonlinear Method ◽  
Object A ◽  
Force Optimization ◽  
Grasping Force ◽  
Robotic Devices

The development of robust and adaptable methods of grasping force optimization (GFO) is an important consideration for robotic devices, especially those which are designed to interact naturally with a variety of objects. Along with considerations for the computational efficiency of such methods, it is also important to ensure that a GFO approach chooses forces which can produce a stable grasp even in the presence of uncertainty. This paper examines the robustness of three methods of GFO in the presence of variability in the contact locations and in the coefficients of friction between the hand and the object. A Monte Carlo simulation is used to determine the resulting probability of failure and sensitivity levels when variability is introduced. Two numerical examples representing two common grasps performed by the human hand are used to demonstrate the performance of the optimization methods. Additionally, the method which yields the best overall performance is also tested to determine its consistency when force is applied to the object's center of mass in different directions. The results show that both the nonlinear and linear matrix inequality (LMIs) methods of GFO produce acceptable results, whereas the linear method produces unacceptably high probabilities of failure. Further, the nonlinear method continues to produce acceptable results even when the direction of the applied force is changed. Based on these results, the nonlinear method of GFO is considered to be robust in the presence of variability in the contact locations and coefficients of friction.

scholarly journals Grasping Force Optimization Approaches for Anthropomorphic Hands

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Aimee Cloutier ◽  
James Yang
Keyword(s):  
Computational Efficiency ◽  
Linear Method ◽  
Contact Forces ◽  
Applied Force ◽  
Contact Friction ◽  
Linear Matrix ◽  
Human Hand ◽  
Contact Points ◽  
Force Optimization ◽  
Grasping Force

An appropriate choice of contact forces for anthropomorphic robotic grasping devices is important for achieving a balanced grasp. Too little applied force may cause an object to slip or be dropped, and too much applied force may cause damage to delicate objects. Prior methods of grasping force optimization (GFO) in the literature can be difficult to compare due to variability in the parameters, such as the type of grasping device, the object grasped, and the contact model, among other factors. Additionally, methods are typically tested on a very small number of scenarios and may not be as robust in other settings. This paper presents a detailed analysis of three optimization approaches based on the literature, comparing them on the basis of accuracy and computational efficiency. Numerical examples are provided for three types of grasp commonly performed by the human hand (cylindrical grasp, tip grasp, and tripod grasp) using both soft finger (SF) contact and hard finger (HF) contact friction models. For each method and grasping example, an external force is applied to the object in eighteen different directions to provide a more complete picture of the methods' performance. Contact points between the hand and the object are predetermined (given). A comparison of the results showed that the nonlinear and linear matrix inequality (LMI) approaches perform best in terms of accuracy, while the computational efficiency of the linear method is stronger unless the number of contact points and segments becomes too large. In this case, the nonlinear method performs more quickly. Future work will extend the problem of GFO to real-time implementation, and a related work (briefly addressed here) examines the sensitivity of optimization methods to variability in the contact locations.

Force Optimization Approaches for Common Anthropomorphic Grasps

2016 ◽  
Author(s):  
Aimee Cloutier ◽  
James Yang
Keyword(s):  
Point Contact ◽  
Contact Forces ◽  
Applied Force ◽  
Linear Matrix ◽  
Human Hand ◽  
Numerical Examples ◽  
Contact Points ◽  
Force Optimization ◽  
Grasping Forces ◽  
Grasping Force

A smart choice of contact forces between robotic grasping devices and objects is important for achieving a balanced grasp. Too little applied force may cause an object to slip or be dropped, and too much applied force may cause damage to delicate objects. Prior methods of grasping force optimization in literature have mostly assumed grasp only at the fingertips but have rarely considered how the whole hand grasps more common to anthropomorphic hands affect the optimization of grasping forces. Further, although numerical examples of grasping force optimization methods are routinely provided, it is often difficult to compare the performance of separate methods when they are evaluated using different parameters, such as the type of grasping device, the object grasped, and the contact model, among other factors. This paper presents three optimization approaches (linear, nonlinear, and nonlinear with linear matrix inequality (LMI) friction constraints) which are compared for an anthropomorphic hand. Numerical examples are provided for three types of grasp commonly performed by the human hand (cylindrical grasp, tip grasp, and tripod grasp) using both soft finger contact and point contact with friction models. Contact points between the hand and the object are predetermined. Results are compared based on their accuracy, computational efficiency, and other various benefits and drawbacks unique to each method. Future work will extend the problem of grasping force optimization to include consideration for variable forces and object manipulation.

scholarly journals Preliminary Results in Testing of a Novel Asymmetric Underactuated Robotic Hand Exoskeleton for Motor Impairment Rehabilitation

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1470 ◽  
Author(s):  
Flaviu Ionuț Birouaș ◽  
Radu Cătălin Țarcă ◽  
Simona Dzitac ◽  
Ioan Dzitac
Keyword(s):  
Mechanical Design ◽  
Experimental Testing ◽  
Motor Impairment ◽  
Human Hand ◽  
Underactuated System ◽  
Robotic Hand ◽  
Advantages And Disadvantages ◽  
Hand Exoskeleton ◽  
Robotic Devices ◽  
And Behavior

Robotic exoskeletons are a trending topic in both robotics and rehabilitation therapy. The research presented in this paper is a summary of robotic exoskeleton development and testing for a human hand, having application in motor rehabilitation treatment. The mechanical design of the robotic hand exoskeleton implements a novel asymmetric underactuated system and takes into consideration a number of advantages and disadvantages that arose in the literature in previous mechanical design, regarding hand exoskeleton design and also aspects related to the symmetric and asymmetric geometry and behavior of the biological hand. The technology used for the manufacturing and prototyping of the mechanical design is 3D printing. A comprehensive study of the exoskeleton has been done with and without the wearer’s hand in the exoskeleton, where multiple feedback sources are used to determine symmetric and asymmetric behaviors related to torque, position, trajectory, and laws of motion. Observations collected during the experimental testing proved to be valuable information in the field of augmenting the human body with robotic devices.

Balancing of Flexible Rotors Using Convex Optimization Techniques: Optimum Min-Max LMI Influence Coefficient Balancing

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
Costin D. Untaroiu ◽  
Paul E. Allaire ◽  
William C. Foiles
Keyword(s):  
Convex Optimization ◽  
Optimization Theory ◽  
Numerical Algorithms ◽  
Optimization Methods ◽  
Industrial Applications ◽  
Optimization Techniques ◽  
Linear Matrix ◽  
Influence Coefficient ◽  
Flexible Rotors ◽  
Optimum Solution

In some industrial applications, influence coefficient balancing methods fail to find the optimum vibration reduction due to the limitations of the least-squares optimization methods. Previous min-max balancing methods have not included practical constraints often encountered in industrial balancing. In this paper, the influence coefficient balancing equations, with suitable constraints on the level of the residual vibrations and the magnitude of correction weights, are cast in linear matrix inequality (LMI) forms and solved with the numerical algorithms developed in convex optimization theory. The effectiveness and flexibility of the proposed method have been illustrated by solving two numerical balancing examples with complicated requirements. It is believed that the new methods developed in this work will help in reducing the time and cost of the original equipment manufacturer or field balancing procedures by finding an optimum solution of difficult balancing problems. The resulting method is called the optimum min-max LMI balancing method.

Nonlinear Calculating Method for Multi-Bar Luffing System of Giant Crane Considering the Sagging Effect

2013 ◽  
Vol 477-478 ◽  
pp. 307-314
Author(s):  
Qi Cai Zhou ◽  
Wen Jun Li ◽  
Qing Long Wu ◽  
Xiao Lei Xiong
Keyword(s):  
Theoretical Analysis ◽  
Engineering Design ◽  
Theoretical Basis ◽  
Linear Method ◽  
Structural Behavior ◽  
Nonlinear Method ◽  
Equilibrium Equations ◽  
Calculating Method ◽  
Flexible Cables ◽  
Luffing System

The luffing system of giant jib crane is usually consisted of rigid bars and flexible cables, and the weight of components causes sagging deformation and large displacement. In order to study the nonlinear method for multi-bar luffing system of giant jib crane, this paper completed theoretical analysis and example verification. Based on catenary theory, the equation of luffing strand was obtained. Spatial multi-bar equation of three bars was proposed and it was extended to any more bars. Then, equilibrium equations of components were written in sequence, and the equations of multi-bar luffing system were established with the elastic deformation of multi-bar and luffing strand. Finally, by taking the 2500-ton ring crane as an example, the results demonstrated that: the nonlinearity of combined multi-bar luffing system negatively influences the structural behavior, and the results calculated by the proved nonlinear method is more reasonable and closer to the actuality than the linear method, so it provides theoretical basis for engineering design and construction.

scholarly journals Similar sensorimotor transformations control balance during standing and walking

2021 ◽  
Vol 17 (6) ◽  
pp. e1008369
Author(s):  
Maarten Afschrift ◽  
Friedl De Groote ◽  
Ilse Jonkers
Keyword(s):  
Gait Cycle ◽  
Walking Speed ◽  
Balance Control ◽  
Center Of Mass ◽  
Linear Feedback ◽  
Sensorimotor Transformations ◽  
Robotic Devices ◽  
Walking Balance ◽  
Mass Position ◽  
Task Level

Standing and walking balance control in humans relies on the transformation of sensory information to motor commands that drive muscles. Here, we evaluated whether sensorimotor transformations underlying walking balance control can be described by task-level center of mass kinematics feedback similar to standing balance control. We found that delayed linear feedback of center of mass position and velocity, but not delayed linear feedback from ankle angles and angular velocities, can explain reactive ankle muscle activity and joint moments in response to perturbations of walking across protocols (discrete and continuous platform translations and discrete pelvis pushes). Feedback gains were modulated during the gait cycle and decreased with walking speed. Our results thus suggest that similar task-level variables, i.e. center of mass position and velocity, are controlled across standing and walking but that feedback gains are modulated during gait to accommodate changes in body configuration during the gait cycle and in stability with walking speed. These findings have important implications for modelling the neuromechanics of human balance control and for biomimetic control of wearable robotic devices. The feedback mechanisms we identified can be used to extend the current neuromechanical models that lack balance control mechanisms for the ankle joint. When using these models in the control of wearable robotic devices, we believe that this will facilitate shared control of balance between the user and the robotic device.

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