Utah/MIT Dextrous Hand [25 Years Ago]

2013 ◽  
Vol 33 (1) ◽  
pp. 13-13
Keyword(s):  
Dextrous Hand

Optimization in Trajectory Planning of Multi-Jointed Fingers in Dextrous Hand Designs

1991 ◽  
Author(s):  
A. Meghdari ◽  
H. Sayyaadi
Keyword(s):  
Dynamic Programming ◽  
Motion Control ◽  
Trajectory Planning ◽  
Degrees Of Freedom ◽  
Optimization Technique ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Kinematics And Dynamics ◽  
Feasible Solutions ◽  
Dextrous Hand

Abstract An optimization technique based on the well known Dynamic Programming Algorithm is applied to the motion control trajectories and path planning of multi-jointed fingers in dextrous hand designs. A three fingered hand with each finger containing four degrees of freedom is considered for analysis. After generating the kinematics and dynamics equations of such a hand, optimum values of the joints torques and velocities are computed such that the finger-tips of the hand are moved through their prescribed trajectories with the least time or/and energy to reach the object being grasped. Finally, optimal as well as feasible solutions for the multi-jointed fingers are identified and the results are presented.

Analysis of the Grasp Planning Method for Manipulation of BH-4 Dextrous Hand

2007 ◽  
Author(s):  
Weidong Guo ◽  
Mileta M. Tomovic ◽  
Jiting Li
Keyword(s):  
Coordinate System ◽  
Inverse Kinematic ◽  
Planning Method ◽  
Experimental Result ◽  
Robot Arm ◽  
Grasp Planning ◽  
Physical Prototype ◽  
Dexterous Hand ◽  
Dextrous Hand ◽  
Kinematic Solution

The paper presents method for planning robotic dexterous hand grasping task using example of the Beihang University’s BH-4 dexterous hand. The grasping planning method is devised through modeling and simulation and experimentally verified using physical prototype. The paper presents the method for forward and inverse kinematic solutions of the BH-4 robot 4-DOF finger, including transformation matrix between the palm coordinate system and the finger base coordinate system. In addition, the method of the idiographic manipulation is presented using example of ball grasping. The simulation results and physical experiment verify that the inverse kinematic solution is correct, and kinematic grasping and operating planning is valid and feasible. Finally, the experiment with the complex system integrated robot arm with dexterous hand is carried out. Experimental result shows that the more complicated grasping task can be done by a dexterous hand integrated in the robot arm system.

An Electropneumatic Actuation System for the Utah/MIT Dextrous Hand

1985 ◽  
pp. 271-279 ◽  
Author(s):  
S. C. Jacobsen ◽  
D. F. Knutti ◽  
K. B. Biggers ◽  
E. K. Iversen ◽  
J. E. Wood
Keyword(s):  
Actuation System ◽  
Dextrous Hand

A Kinematic Study of a Robot and a Dextrous Hand

1989 ◽  
pp. 132-138
Author(s):  
Ranvir S. Solanki ◽  
Kuldip S. Rattan ◽  
Ints Kaleps ◽  
Daniel W. Repperger
Keyword(s):  
Kinematic Study ◽  
Dextrous Hand

A Neurofuzzy Knowledge Based Architecture for Robotic Hand Manipulation Forces Learning

2013 ◽  
Vol 3 (2) ◽  
pp. 16-38 ◽  
Author(s):  
Ebrahim Mattar
Keyword(s):  
Force Distribution ◽  
Robotic Hand ◽  
Knowledge Based ◽  
Optimal Force ◽  
Learning Technique ◽  
Optimization Formulation ◽  
Hand Grasp ◽  
Optimal Set ◽  
Dextrous Hand ◽  
Optimal Force Distribution

Optimal distribution of forces for manipulation by a robot hand, is a hard computational issue, specifically once a whole hand grasp is needed. It becomes a complicated issue, once a robotic hand is equipped with human like deformable sensory touching materials. For computing optimal set of manipulation forces, grip transform and inverse hand Jacobian play major roles for such purposes. This manuscript is discussing a Neurofuzzy learning technique for learning optimal force distribution by a dextrous hand. For learning purposes, optimal set of forces patterns were gathered in advanced using optimization formulation technique. After that, to let a Neurofuzzy system to learn the nonlinear kinematics-dynamics relations needed for force distribution. This is done by considering the computational requirements for the inverse hand Jacobian, in addition to the interaction between hand fingers and the object. Training patterns clustering, and generation of the fuzzy initial memberships, and updated shape of memberships, are considered as vital information to build upon for more reasoning of fuzzy interrelation. The technique is novel in a sense, that the adopted Neurofuzzy architecture was transparent in terms of revealing the learned hand optimal forces if then rules.

Study of the Non-Dominated Sorting Genetic Algorithm for the Optimal Structural Parameter of the Tooth-Arrangement Multi-Fingered Dextrous Hand

2013 ◽  
Vol 756-759 ◽  
pp. 4082-4089
Author(s):  
Zhan Li Li ◽  
Xiang Ting He
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Experimental Results ◽  
Structural Parameter ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective ◽  
Dextrous Hand

Firstly, the structural parameter optimization of the tooth-arrangement multi-fingered dextrous hand is studied. Secondly, as to the shortcomings that the Pareto solution of multi-objective optimization was distributed unevenly in NSGA-II, a non-dominated sorting genetic algorithm based on immune principle is proposed. Lastly, the structural parameter of the medical tooth-arrangement multi-fingered dextrous hand is optimized using the proposed algorithm. The experimental results show that this algorithm can optimize structural parameter of tooth-arrangement multi-fingered dextrous hand very well.

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