Task based design of modular robot manipulator using efficient genetic algorithm

Impact manipulation instantaneously generates a large force making it effective for pressfitting. We model pressfitting and analyze it for realization by a robot manipulator, analyzing the relationship between hit speed and pressfitting depth to determine the hit speed required for different pressfitting depths. We use an under-actuated manipulator for hitting the “sweet spot” of the end effector, introducing a simple genetic algorithm to plan manipulator movement to generate the desired hit speed. Results of experiments on pressfitting for driving an under-actuated manipulator verified the feasibility of our proposal.

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2A1-E13 Method for Solving Inverse Kinematics of Robot Manipulator using Genetic Algorithm and Neural Network

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ◽

10.1299/jsmermd.2006._2a1-e13_1 ◽

2006 ◽

Vol 2006 (0) ◽

pp. _2A1-E13_1-_2A1-E13_4

Author(s):

Seiji AOYAGI ◽

Junichi MARUYAMA ◽

Takaaki Tanaka

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Inverse Kinematics ◽

Robot Manipulator

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Evolutionary Motion Synthesis for a Modular Robot Using Genetic Algorithm

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2003.p0227 ◽

2003 ◽

Vol 15 (2) ◽

pp. 227-237 ◽

Cited By ~ 5

Author(s):

Eiichi Yoshida ◽

◽

Satoshi Murata ◽

Akiya Kamimura ◽

Kohji Tomita ◽

...

Keyword(s):

Genetic Algorithm ◽

Fitness Function ◽

Synthesis Method ◽

Three Dimensional ◽

Modular Robot ◽

Motion Synthesis ◽

Natural Manner ◽

Simple Description ◽

Motion Sequence ◽

Feasible Solutions

An evolutionary motion synthesis method using genetic algorithm (GA) is presented for self-reconfigurable modular robot M-TRAN designed to realize various robotic motions and three-dimensional structures. The proposed method is characterized by its capacity to derive feasible solutions for complex synthesis problem of M-TRAN through natural genetic representation. For this purpose, the behavior of the robot is described using a motion sequence including both the dynamic motions and configuration changes of the robot. It is a series of segments each of which can specify simultaneous motor actuations and selfreconfiguration by connection/disconnection, starting from a given initial configuration. This simple description can be straightforwardly encoded into genetic representation to which genetic operations can be applied in a natural manner. We adopt traveling distance achieved by the evolved motion as the fitness function of GA. To verify the effectiveness of the proposed method, we have conducted simulations of evolutionary motion synthesis for certain initial configurations. Consequently, we confirm various adaptive motions are acquired according to different initial configurations and fitness functions. We also verify the physical feasibility of the evolved motions through experiments using hardware module M-TRAN II.

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