Abstract
The robot control consists of kinematic control and dynamic control. Control methods of the robot involve forward kinematics and inverse kinematics (IK). In Inverse kinematics the joint angles are found for a given position and orientation of the end effector. Inverse kinematics is a nonlinear problem and has multiple solutions. This computation is required to control the robot arms. A Genetic Algorithm (GA) and Hybrid genetic algorithm (HGA) (Genetic Algorithm in conjunction with Nelder-Mead technique) are proposed for solving the inverse kinematics of a robotic arm. HGA introduces two concepts exploration, exploitation. In an exploration phase, the GA identifies the good areas in entire search space and then exploitation phase is performed inside these areas by using Nelder- mead technique Binary Simulated Crossover and niching strategy for binary tournament selection operator is used. Proposed algorithms can be used on any type of manipulator and the only requirement is the forward kinematic equations, which are easily obtained. As a case study inverse kinematics of a Two Link Elbow Manipulator and PUMA manipulator are solved using GA and HGA in MATLAB. The algorithm is able to find all solutions without any error
Highlights
In designing a robotic system the important step is solving the the inverse kinematics problem .It is a nonlinear problem and has multiple solutions
This paper presents a real-coded genetic algorithm that applies a Nelder-Mead technique to solutions produced by the genetic operators The Nelder–Mead is a powerful local descent algorithm, which makes no use of the objective function derivatives
Fitness Function obtained in eqn.18 is minimized using Genetic Algorithm (GA) and Hybrid Genetic Algorithm (HGA) in MATLAB
Summary
In designing a robotic system the important step is solving the the inverse kinematics problem .It is a nonlinear problem and has multiple solutions. There are different procedures available for solving the inverse kinematics problem. These include the geometric, algebraic and numerical iterative methods. Analytical expressions are possible only if robots’s configuration satisfies one of following conditions three adjacent joint axes intersect in one point. Three adjacent joint axes are parallel to each other Another method to solve the IK problem is to use numerical methods. Most of numerical methods are divergence based so they converge to a solution which is closest to the starting point To overcome this evolutionary algorithms like genetic algorithms have been used
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