An optimal force distribution scheme for cooperating multiple robot manipulators

Robotica ◽  
1993 ◽  
Vol 11 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Yong Dal Shin ◽  
Myung Jin Chung
Keyword(s):  
Efficient Method ◽  
Optimal Solution ◽  
Inequality Constraints ◽  
Force Distribution ◽  
Weak Point ◽  
Contact Points ◽  
Optimal Force ◽  
Distribution Scheme ◽  
Equality And Inequality Constraints ◽  
Optimal Force Distribution

SUMMARYIn this paper, we suggest an optimal force distribution scheme by weak point force minimization and we also present an efficient method to solve the problem. The concept of a weak point is a generalized one which is applicable to any points of interest, as well as joints or contact points between end-effectors and an object. The problem is formulated by a quadratic objective function of the forces exerted at weak points subject to the linear equality and inequality constraints, and its optimal solution is obtained by an efficient method. As regards the solution of the problem, the original problem is reformulated to a reduced order dual problem after the equality constraints are eliminated by force decomposition.

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.

scholarly journals Minimizing the Energy Consumption for a Hexapod Robot Based on Optimal Force Distribution

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 5393-5406 ◽  
Author(s):  
Guanyu Wang ◽  
Liang Ding ◽  
Haibo Gao ◽  
Zongquan Deng ◽  
Zhen Liu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Force Distribution ◽  
Hexapod Robot ◽  
Optimal Force ◽  
Optimal Force Distribution

scholarly journals Existence of Local Saddle Points for a New Augmented Lagrangian Function

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Wenling Zhao ◽  
Jing Zhang ◽  
Jinchuan Zhou
Keyword(s):  
Augmented Lagrangian ◽  
Saddle Points ◽  
Optimal Solution ◽  
Inequality Constraints ◽  
Augmented Lagrangian Function ◽  
Lagrangian Functions ◽  
New Class ◽  
Close Relationship ◽  
Equality And Inequality Constraints ◽  
Local Optimal Solution

We give a new class of augmented Lagrangian functions for nonlinear programming problem with both equality and inequality constraints. The close relationship between local saddle points of this new augmented Lagrangian and local optimal solutions is discussed. In particular, we show that a local saddle point is a local optimal solution and the converse is also true under rather mild conditions.

scholarly journals OPTIMIZATION OF PIECEWISE NON-LINEAR MULTI CONSTRAINED ECONOMIC POWER DISPATCH PROBLEM USING AN IMPROVED GENETIC ALGORITHM

2010 ◽  
pp. 320-326
Author(s):  
B. Padmanabhan ◽  
R. S. SivaKumar ◽  
J. Jasper
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Optimal Solution ◽  
Inequality Constraints ◽  
Local Optimum ◽  
Improved Genetic Algorithm ◽  
Non Linear ◽  
Equality And Inequality Constraints ◽  
Loading Effects ◽  
Economic Power Dispatch

In this paper, a more realistic formulation of the Economic Dispatch problem is proposed, which considers practical constraints and non linear characteristics. The proposed ED formulation includes ramp rate limits, valve loading effects, equality and inequality constraints, which usually are found simultaneously in realistic power systems. This paper presents a novel Genetic Algorithm to solve the economic load dispatch (ELD) problem of thermal generators of a power system. This method provides an almost global optimal solution, since they don’t get stuck at local optimum. The proposed method and its variants are validated for the two test systems consisting of 3 and 10 thermal units whose incremental fuel cost functions takes into account the valve-point loading effects.

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