A Genetic Algorithm as Support in the Movement Optimisation of a Redundant Serial Robot

2006 ◽  
Author(s):  
Massimo Antonini ◽  
Alberto Borboni ◽  
Roberto Bussola ◽  
Rodolfo Faglia
Keyword(s):  
Genetic Algorithm ◽  
Inverse Kinematics ◽  
Inverse Matrix ◽  
Working Environment ◽  
End Effector ◽  
Redundant Robot ◽  
Linear Motors ◽  
Inverse Kinematics Problem ◽  
Serial Robot ◽  
Pseudo Inverse

The paper illustrates the application of a genetic algorithm as a methodology to choose and improve the motion law governing the movement of a redundant robot. The subject of this research is an innovative system developed to introduce the laser ray technology in the on-line tube cutting. This technique allows a quality improvement in the pipe-cutting sector, thanks to the various goals. Firstly we underline the improvement of the working environment due to the elimination of cutting noise taking off the tool changing and the steel shaving creation. Secondarily there is a drastic reduction of the cutting cycle time and an improvement of the productivity accomplished by the use of brushless drives and linear motors. In the robot design, particular attention was dedicated to the masses distributions allowing a good natural machine dynamics. The ability of the robot to avoid the cutting object, the demand to maintain the laser torch orthogonal to the cutting surfaces, as well the necessity to impose a velocity behaviour as constant as possible during the cutting operation, suggested the introduction of a redundant degree of freedom. This aspect gives a lot of opportunities in the choose of movements because there are thousands motion profiles for the joints whichever satisfying the conditions imposed to the end-effector path. In this context, we have proposed the idea to combine the procedure to solve the inverse kinematics problem with the contemporaneous optimization of the trajectory. Literature offers a series of algorithms to solve well-known inverse kinematics problem of redundant robot. These are based on the inversion of the matrix representing the link between the end-effector co-ordinates and the joints. The presence of redundancy makes this matrix rectangular and requires the use of the pseudo-inverse matrix to solve the problem in several points of the trajectory. The introduction of some weights, one for each joint co-ordinate, allows to obtain a different distribution of the joint movements computing the pseudo-inverse matrix. If we change these weights in a continuous way in time domain, we can supervise the dynamic behaviors of the machine. The new idea we propose here is the use of an adapted multi-objective genetic algorithm to define a several of particular motion laws reducing vibrations and realizing “special harmonies” in the robot motion. The procedure, that will be completely discussed in the full paper, is actually working on a laser pipe cutting machine. This robot awarded the first prize between two thousand competitor at the EMO MILANO 2003 exhibition.

scholarly journals Hybrid Mutation Fruit Fly Optimization Algorithm for Solving the Inverse Kinematics of a Redundant Robot Manipulator

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jianping Shi ◽  
Yuting Mao ◽  
Peishen Li ◽  
Guoping Liu ◽  
Peng Liu ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Inverse Kinematics ◽  
Robot Manipulator ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Redundant Manipulators ◽  
End Effector ◽  
Redundant Robot ◽  
Fruit Fly Optimization ◽  
Inverse Kinematics Problem

The inverse kinematics of redundant manipulators is one of the most important and complicated problems in robotics. Simultaneously, it is also the basis for motion control, trajectory planning, and dynamics analysis of redundant manipulators. Taking the minimum pose error of the end-effector as the optimization objective, a fitness function was constructed. Thus, the inverse kinematics problem of the redundant manipulator can be transformed into an equivalent optimization problem, and it can be solved using a swarm intelligence optimization algorithm. Therefore, an improved fruit fly optimization algorithm, namely, the hybrid mutation fruit fly optimization algorithm (HMFOA), was presented in this work for solving the inverse kinematics of a redundant robot manipulator. An olfactory search based on multiple mutation strategies and a visual search based on the dynamic real-time updates were adopted in HMFOA. The former has a good balance between exploration and exploitation, which can effectively solve the premature convergence problem of the fruit fly optimization algorithm (FOA). The latter makes full use of the successful search experience of each fruit fly and can improve the convergence speed of the algorithm. The feasibility and effectiveness of HMFOA were verified by using 8 benchmark functions. Finally, the HMFOA was tested on a 7-degree-of-freedom (7-DOF) manipulator. Then the results were compared with other algorithms such as FOA, LGMS-FOA, AE-LGMS-FOA, IFOA, and SFOA. The pose error of end-effector corresponding to the optimal inverse solution of HMFOA is 10−14 mm, while the pose errors obtained by FOA, LGMS-FOA, AE-LGMS-FOA, IFOA, and SFOA are 102 mm, 10−1 mm, 10−2 mm, 102 mm, and 102 mm, respectively. The experimental results show that HMFOA can be used to solve the inverse kinematics problem of redundant manipulators effectively.

Kinematics of a Fully-Decoupled Remote Center-of-Motion Parallel Manipulator for Minimally Invasive Surgery

2012 ◽  
Vol 6 (2) ◽  
Author(s):  
Chin-Hsing Kuo ◽  
Jian S. Dai
Keyword(s):  
Minimally Invasive Surgery ◽  
Minimally Invasive ◽  
Invasive Surgery ◽  
Inverse Kinematics ◽  
Parallel Manipulator ◽  
Degrees Of Freedom ◽  
Surgical Instruments ◽  
End Effector ◽  
Minimally Invasive Surgical ◽  
Inverse Kinematics Problem

A crucial design challenge in minimally invasive surgical (MIS) robots is the provision of a fully decoupled four degrees-of-freedom (4-DOF) remote center-of-motion (RCM) for surgical instruments. In this paper, we present a new parallel manipulator that can generate a 4-DOF RCM over its end-effector and these four DOFs are fully decoupled, i.e., each of them can be independently controlled by one corresponding actuated joint. First, we revisit the remote center-of-motion for MIS robots and introduce a projective displacement representation for coping with this special kinematics. Next, we present the proposed new parallel manipulator structure and study its geometry and motion decouplebility. Accordingly, we solve the inverse kinematics problem by taking the advantage of motion decouplebility. Then, via the screw system approach, we carry out the Jacobian analysis for the manipulator, by which the singular configurations are identified. Finally, we analyze the reachable and collision-free workspaces of the proposed manipulator and conclude the feasibility of this manipulator for the application in minimally invasive surgery.

Dynamics of redundant robots – inverse solutions

Robotica ◽  
1986 ◽  
Vol 4 (4) ◽  
pp. 263-267 ◽  
Author(s):  
Ronald L. Huston ◽  
Timothy P. King
Keyword(s):  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Solution Algorithm ◽  
Redundant Robot ◽  
Redundant Robots ◽  
Revolute Joints ◽  
Inverse Kinematics Problem ◽  
Inverse Solutions ◽  
Natural Dynamics ◽  
Least Energy

SUMMARYThe dynamics of “simple, redundant robots” are developed. A “redundant” robot is a robot whose degrees of freedom are greater than those needed to perform a given kinetmatic task. A “simple” robot is a robot with all joints being revolute joints with axes perpendicular or parallel to the arm segments. A general formulation, and a solution algorithm, for the “inverse kinematics problem” for such systems, is presented. The solution is obtained using orthogonal complement arrays which in turn are obtained from a “zero-eigenvalues” algorithm. The paper concludes with an assertion that this solution, called the “natural dynamics solution,” is optimal in that it requires the least energy to drive the robot.

A Simple Method for Inverse Kinematic Analysis of the General 6R Serial Robot

2006 ◽  
Author(s):  
Zhi Xin Shi ◽  
Yu Feng Luo ◽  
Lu Bing Hang ◽  
Ting Li Yang
Keyword(s):  
Kinematic Analysis ◽  
Inverse Kinematics ◽  
Algebraic Method ◽  
Inverse Kinematic ◽  
New Method ◽  
One Dimension ◽  
Parallel Mechanisms ◽  
Simple Method ◽  
Inverse Kinematics Problem ◽  
Serial Robot

Because the solution to inverse kinematics problem of the general 5R serial robot is unique and its assembly condition has been derived, a simple effective method for inverse kinematics problem of general 6R serial robot or forward kinematics problem of general 7R single-loop mechanism is presented based on one-dimension searching algorithm. The new method has the following features: (1) Using one-dimension searching algorithm, all the real inverse kinematic solutions are obtained and it has higher computing efficiency; (2) Compared with algebraic method, it has evidently reduced the difficulty of deducing formulas. The principle of the new method can be generalized to kinematic analysis of parallel mechanisms.

Inverse Kinematics of Discretely-Actuated Manipulators Within a Bounded Grid Element

2006 ◽  
Author(s):  
Deanne C. Kemeny ◽  
Raymond J. Cipra
Keyword(s):  
Inverse Kinematics ◽  
Lower Cost ◽  
Robotic Manipulators ◽  
Forward Kinematics ◽  
End Effector ◽  
Grid Element ◽  
The Third ◽  
Inverse Kinematics Problem

Discretely-actuated manipulators are defined in this paper as serial planar chains of many links and are an alternative to traditional robotic manipulators, where continuously variable actuators are replaced with discrete, or digital actuators. Benefits include reduced weight and complexity, and predictable manipulation at lower cost. Challenges to using digital manipulators are the discrete end-effector positions which make the inverse kinematics problem difficult to solve. Furthermore, for a specific application position in the manipulator workspace, there may not be an actual end-effector position. This research has relaxed the inverse kinematics problem around this challenge making each application position an element of a grid in which the end effector must reach. There may be many possible end-effector positions that would reach the element goal, the solution uses the first one that is found. The inverse kinematics solution assumes the assembly configuration of the digital manipulator is already solved specifically for the application grid. The Jacobian function, normally used to solve joint velocities, can be used to identify the exact shift vectors that are used for the inverse kinematics. Three methods to solve this problem are discussed and the third method was implemented as a four-part solution that is a directed and manipulated search for the inverse kinematics solution where all four solutions may be needed. A discussion of forward kinematics and the Jacobian function in relation to digital manipulators is also presented.

scholarly journals Generalized solution for inverse kinematics problem of a robot using hybrid genetic algorithms

2018 ◽  
Vol 7 (4.6) ◽  
pp. 250
Author(s):  
Pavan Kumar K ◽  
Murali Mohan J ◽  
Srikanth D
Keyword(s):  
Genetic Algorithm ◽  
Inverse Kinematics ◽  
Dynamic Control ◽  
Hybrid Genetic Algorithm ◽  
Search Space ◽  
Generalized Solution ◽  
Inverse Kinematics Problem ◽  
Tournament Selection ◽  
Selection Operator ◽  
Forward Kinematic

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  

scholarly journals Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141879299 ◽  
Author(s):  
Zhiyu Zhou ◽  
Hanxuan Guo ◽  
Yaming Wang ◽  
Zefei Zhu ◽  
Jiang Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Extreme Learning Machine ◽  
Inverse Kinematics ◽  
Robotic Manipulator ◽  
Computational Time ◽  
Improved Genetic Algorithm ◽  
Intelligent Algorithm ◽  
End Effector ◽  
Training Time ◽  
Learning Machine

This article presents an intelligent algorithm based on extreme learning machine and sequential mutation genetic algorithm to determine the inverse kinematics solutions of a robotic manipulator with six degrees of freedom. This algorithm is developed to minimize the computational time without compromising the accuracy of the end effector. In the proposed algorithm, the preliminary inverse kinematics solution is first computed by extreme learning machine and the solution is then optimized by an improved genetic algorithm based on sequential mutation. Extreme learning machine randomly initializes the weights of the input layer and biases of the hidden layer, which greatly improves the training speed. Unlike classical genetic algorithms, sequential mutation genetic algorithm changes the order of the genetic codes from high to low, which reduces the randomness of mutation operation and improves the local search capability. Consequently, the convergence speed at the end of evolution is improved. The performance of the extreme learning machine and sequential mutation genetic algorithm is also compared with that of a hybrid intelligent algorithm, and the results showed that there is significant reduction in the training time and computational time while the solution accuracy is retained. Based on the experimental results, the proposed extreme learning machine and sequential mutation genetic algorithm can greatly improve the time efficiency while ensuring high accuracy of the end effector.

A Simple Method for Inverse Kinematic Analysis of the General 6R Serial Robot

2006 ◽  
Vol 129 (8) ◽  
pp. 793-798 ◽  
Author(s):  
Shi Zhi Xin ◽  
Luo Yu Feng ◽  
Hang Lu Bing ◽  
Yang Ting Li
Keyword(s):  
Kinematic Analysis ◽  
Inverse Kinematics ◽  
Inverse Kinematic ◽  
New Method ◽  
One Dimension ◽  
Forward Kinematics ◽  
The Real ◽  
Inverse Kinematics Problem ◽  
Serial Robot ◽  
Inverse Kinematic Analysis

The inverse kinematic analysis of the general 6R serial robot has been a very significant and important problem in the theory of the spatial mechanisms. Because the solution to inverse kinematics problem of the general 5R serial robot is unique and its assembly condition has been derived, a simple effective method for inverse kinematics problem of general 6R serial robot or forward kinematics problem of general 7R single-loop mechanism is presented based on a one-dimension searching algorithm. All the real solutions to inverse kinematics problems of the general 6R serial robot or forward kinematics problems of the general 7R single-loop mechanism can be obtained. The new method has the following features: (1) using one-dimension searching algorithm, all the real inverse kinematic solutions are obtained and it has higher computing efficiency; and (2) compared with the algebraic method, it has evidently reduced the difficulty of deducing formulas. The principle of the new method can be generalized to kinematic analysis of parallel mechanisms.

scholarly journals Multi-Objective Swarm Intelligence Trajectory Generation for a 7 Degree of Freedom Robotic Manipulator

Robotics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 127
Author(s):  
Aryslan Malik ◽  
Troy Henderson ◽  
Richard Prazenica
Keyword(s):  
Swarm Intelligence ◽  
Inverse Kinematics ◽  
A Priori ◽  
Trajectory Generation ◽  
Robotic Manipulator ◽  
Degree Of Freedom ◽  
Control Effort ◽  
End Effector ◽  
Multi Objective ◽  
Inverse Kinematics Problem

This work is aimed to demonstrate a multi-objective joint trajectory generation algorithm for a 7 degree of freedom (DoF) robotic manipulator using swarm intelligence (SI)—product of exponentials (PoE) combination. Given a priori knowledge of the end-effector Cartesian trajectory and obstacles in the workspace, the inverse kinematics problem is tackled by SI-PoE subject to multiple constraints. The algorithm is designed to satisfy finite jerk constraint on end-effector, avoid obstacles, and minimize control effort while tracking the Cartesian trajectory. The SI-PoE algorithm is compared with conventional inverse kinematics algorithms and standard particle swarm optimization (PSO). The joint trajectories produced by SI-PoE are experimentally tested on Sawyer 7 DoF robotic arm, and the resulting torque trajectories are compared.

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