The identification of joint parameters for modular robots using fuzzy theory and a genetic algorithm

Robotica ◽  
2002 ◽  
Vol 20 (5) ◽  
pp. 509-517 ◽  
Author(s):  
Yangmin Li ◽  
Xiaoping Liu ◽  
Zhaoyang Peng ◽  
Yugang Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Dynamic Characteristics ◽  
Fuzzy Theory ◽  
Dynamic Control ◽  
Modular Robots ◽  
Optimal Solutions ◽  
Modular Robot ◽  
Redundant Robot ◽  
Joint Parameters

SummaryThis paper discusses a technique for identifying the joint parameters of a modular robot in order to study the dynamic characteristics of the whole structure and to realise dynamic control. A method for identifying the joint parameters of the structure applying fuzzy logic combined with a genetic algorithm has been studied using a 9-DOF modular redundant robot. A Genetic Algorithm was used in the fuzzy optimisation, which helped to avoid converging to locally optimal solutions and made the results identified much more reasonable. The joint parameters of a 9-DOF modular redundant robot have been identified.

scholarly journals Adjust genetic algorithm parameter by fuzzy system

2015 ◽  
Vol 37 ◽  
pp. 190
Author(s):  
Tayebe Noshadi ◽  
Marzieh Dadvar ◽  
Nastaran Mirza ◽  
Shima Shamseddini
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Genetic Evolution ◽  
Optimal Solutions ◽  
Heuristic Rules ◽  
Parallel Genetic Algorithms ◽  
Genetic Algorithm Parameter

Genetic algorithm is one of the random searches algorithm. Genetic algorithm is a method that uses genetic evolution as a model of problem solving. Genetic algorithm for selecting the best population, but the choices are not as heuristic information to be used in specific issues. In order to obtain optimal solutions and efficient use of fuzzy systems with heuristic rules that we would aim to increase the efficiency of parallel genetic algorithms using fuzzy logic immigration, which in fact do this by optimizing the parameters compared with the use of fuzzy system is done.

scholarly journals APPROACH TO RECONFIGURATION OF A MODULAR ROBOT SYSTEM WITH A SUBOPTIMAL SEARCH POLYNOMIAL ALGORITHM

2020 ◽  
pp. 48-51
Author(s):  
A. D. Kovalev
Keyword(s):  
Polynomial Algorithm ◽  
System Size ◽  
Robotic System ◽  
Modular Robots ◽  
Optimal Solutions ◽  
Modular Robot ◽  
Robot System

An approach to connecting modular robots is presented, which includes the algorithm of suboptimal reconfiguration search GreedyCM. This component allows to find nearly optimal solutions in timeframes, polynomially dependent on the robotic system size.

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

scholarly journals Selection of Scholarship Recipient by Implementing Genetic Algorithm and Fuzzy Logic

2021 ◽  
Vol 1933 (1) ◽  
pp. 012069
Author(s):  
Yohanssen Pratama ◽  
Monalisa Pasaribu ◽  
Joni Nababan ◽  
Dayani Sihombing ◽  
Dicky Gultom
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Selection Of

scholarly journals Cable Tension Adjustment Based upon Fuzzy Theory and Genetic Algorithm.

2000 ◽  
pp. 143-151
Author(s):  
Masakatsu KANEYOSHI ◽  
Hitoshi FURUTA ◽  
Hiroshi TANAKA
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Theory ◽  
Cable Tension

Modular Robot Connection Design

1992 ◽  
Author(s):  
Robert O. Ambrose ◽  
Delbert Tesar
Keyword(s):  
Product Line ◽  
Modular Robots ◽  
Modular Robotics ◽  
Modular Robot ◽  
Manufacturing Costs ◽  
Mechanical Connection ◽  
Automation Equipment ◽  
Connection Design

Abstract The ability to reconfigure automation equipment will reduce the manufacturing costs of obsolesence, training and maintenance while allowing for a faster response to changes in the product line. A modular philosophy will give the user these advantages, but only if based on a common connection standard. A mechanical connection was selected for the UT Modular Robotics Testbed and used in the designs of four robot joint modules and nine robot link modules. The standard was also used for assecories, such as the testand, loading fixtures and endeffectors. Three years of experiments with this connection standard are reviewed, and used as the basis for new connection designs. Experiments using multiple modules assembled as dextrous robots, as well as experiments focusing on the connection itself, will be described. Goals for future connection standards include designs with upward compatibility, combinations of both mechanical and electrical fittings, and robot triendly constraints that allow for automated or remote assembly of modular robots.

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