Inverse Kinematics Of Redundant Robots Research Articles

Solving redundant inverse kinematics of CMOR based on chaos-driven particle swarm optimization algorithm

The China Fusion Engineering Test Reactor (CFETR) multipurpose overload robot (CMOR) is used to maintain the vacuum chamber inner components damaged by heat loads, electromagnetic fields, and nuclear radiation. The CMOR is a redundant robot whose configuration does not meet the condition of the Pieper criterion. Redundant kinematic problems can only be solved by numerical methods, not analytical methods. Conventional numerical iterative methods include considerable computational load, accumulated errors, and the Jacobian matrix's singularity. To solve redundant inverse kinematics efficiently, we compared the behaviors of 25 versions of particle swarm optimization (PSO) algorithms with 12 one-dimensional chaotic maps under unimodal and multimodal test functions. Moreover, we selected three chaos-driven PSO algorithms with optimal convergence performance to address CMOR inverse kinematics. The experimental results indicated that chaos-driven PSO algorithms have higher computational efficiency and can effectively improve the speed and accuracy of algorithm convergence. This proposed algorithm delivers a novel and efficient method for inverse kinematics of redundant robots based on chaotic maps.

Deep-learning damped least squares method for inverse kinematics of redundant robots

In the robot field, it has always been a hard issue of solving inverse kinematics (IK) problems of redundant robot. Although many researchers have come up with solutions for redundant robots with different configurations, there is still an issue of computational efficiency for redundant robot with complex configuration. In this paper, we proposed a novel optimization method which solves redundant robot IK with ultra-high speed and accuracy. On the basis of damped least square (DLS) method with a good stability, we introduced an optimal enhanced coefficient for the first time to achieve faster iteration and more accurate convergence. This is also due to the good performance of the deep neural network we designed in coefficient prediction. 106 data points were selected from the robot working space as the original data training network. The simulation result showed that, unlike other algorithms which take hundreds of iterations to achieve convergence, through the optimized method proposed in this paper, the average number of iteration was just less than 10 and the solving speed was greatly improved on the basis of ensuring stability. In addition, the convergence of the algorithm was greatly improved and when the error threshold was 0.01 mm, the convergence reached 95.47%, which is better than most existing algorithms. In this paper, we evaluated the common IK methods, and the results showed that this method performed better in convergence, accuracy and speed.

A Method of Extended Jacobian and Firefly Algorithm for the Kinematic Analysis of Planar Robots

Planar robots are one of the optimal robot form impacted in typical Cartesian plane. It consists of fixed divisions and connectors positioned in series which offers like working of human arm. The one end of robot arm position is fixed and the other arm of the robot move through the Cartesian plane by modifying the framework of arm joints. The kinematic analysis on planar robot includes position, velocity and acceleration are validated not by considering the force which cause motion to robot. The manipulator with lack of design and fault tolerant operation is analytical for application in remote and threat environment where periodic maintenance and improvements are not available. The most advanced architecture and operational flexibility of robots offer new probability and advancement in a large scale of fabrication process. This paper proposes inverse kinematic analysis of PUMA 560 robotic arm to conclude long range of fault tolerance. The proposed work incorporates Jacobian and Firefly algorithm are generally useful for determining inverse kinematics for redundant robots.

Closed-Loop Inverse Kinematics for Redundant Robots: Comparative Assessment and Two Enhancements

Motivated by the need of a robust and practical inverse kinematics (IK) algorithm for the WAM robot arm, we reviewed the most used closed-loop IK methods for redundant robots, analyzing their main points of concern: convergence, numerical error, singularity handling, joint limit avoidance, and the capability of reaching secondary goals. As a result of the experimental comparison, we propose two enhancements. The first is a new filter for the singular values of the Jacobian matrix that guarantees that its conditioning remains stable, while none of the filters found in the literature is successful at doing so. The second is to combine a continuous task priority strategy with selective damping to generate smoother trajectories. Experimentation on the WAM robot arm shows that these two enhancements yield an IK algorithm that improves on the reviewed state-of-the-art ones, in terms of the good compromise it achieves between time step length, Jacobian conditioning, multiple task performance, and computational time, thus constituting a very solid option in practice. This proposal is general and applicable to other redundant robots.

Natural resolution of ill-posedness of inverse kinematics for redundant robots: a challenge to Bernstein's degrees-of-freedom problem

A robot designed to mimic a human becomes kinematically redundant, i.e.its total degrees of freedom becomes larger than the number of physical variables required for description of a given task. Kinematic redundancy may contribute to enhancement of dexterity and versatility, but incurs a problem of ill-posedness of inverse kinematics from the task-description space to the robot joint space. Such ill-posedness of inverse kinematics was originally found by A.N. Bernstein as the 'degrees-of-freedom problem' in physiology, who also pointed out the importance of unveiling the secret of the central nervous system in how nicely it coordinates a skeletomotor system with so many degrees of freedom interacting in complex ways. However, in the history of robotics research, such ill-posedness of inverse kinematics has not yet been tackled directly, but circumvented by introducing an artificial performance index and determining uniquely an inverse kinematics solution by minimizing it. This paper aims at challenging one of Bernstein's problems and proposes a new method for resolving such an ill-posedness problem in a natural way and in a dynamic sense without invoking any artificial performance index. Instead, a novel concept named 'stability on a manifold' is introduced and it is shown that there exists a sensory feedback signal from the task space to the joint space such that it enables the overall closed-loop dynamics to converge naturally and coordinately to a lower-dimensional manifold describing a set of joint states fulfilling a given motion task. This result is also extended by using the concept of 'transferability to a submanifold' in the case of short- or middle-range movement such as a task of multi-joint reaching.

1P2-N-028 障害物に拘束された冗長ロボットの逆運動学問題の解法 : 2つの厚い壁の隙間の通り抜け(冗長自由度ロボット・メカニズムとその制御,生活を支援するロボメカ技術のメガインテグレーション)

1P2-N-028 障害物に拘束された冗長ロボットの逆運動学問題の解法 : 2つの厚い壁の隙間の通り抜け(冗長自由度ロボット・メカニズムとその制御,生活を支援するロボメカ技術のメガインテグレーション)

The Inverse Kinematics Algorithm of Redundant DOF Robot

A new analytic model for the inverse kinematics of redundant robot is developed based on optimization measure that is defined as a criterion to determine the optimization adaptability. A smoothly continuous function of optimization measure is derived. The redundant seven-DOF robot is kinematically simulated using MATLAB software. Results show that self-motion of joints can vary continuously and the joint angular velocity can be controlled within the smaller range by using the new model for the inverse kinematics of redundant robot.

Natural Resolution of Ill-Posedness of Inverse Kinematics for Redundant Robots Under Constraints

In order to enhance dexterity in execution of robot tasks, a redundant number of degrees-of-freedom (DOF) is adopted for design of robotic mechanisms like robot arms and multi- fingered robot hands. Associated with such redundancy in DOFs relative to the number of physical variables necessary and sufficient for description of a given task, an extra performance index is in- troduced for controlling such a redundant robot in order to avoid arising of ill-posedness of inverse kinematics from the task space to the joint space. This paper shows that such an ill-posedness prob- lem of DOF redundancy can be resolved in a natural way on the basis of construction of sensory feedback signals from the task space and a novel concept named stability on a manifold. To show this, two illustrative robot tasks are analyzed in details, which are 1) posture control of an object via rolling contact by a redundant multi-DOF finger and 2) stable pinching and object manipulation by a pair of multi-DOF robot fingers.

Simulation of Robot Motions by Kinematic Configuration Control

The kinematic configuration control technology bases on a new approach for redundancy resolution. The approach allows to obtain a closed-form solution of the inverse kinematics for redundant robots with regular kinematic chains, as for common nonredundant industrial robots. The off-line evaluated inverse functions need low on¬line computing power and allow to perform local real-time robot motion control in environments with obstacles. Computer simulations of robot motions resulting from the kinematic configuration control are presented by example of a redundant 7 DoF manipulator.

The kinematic roadmap: a motion planning based global approach for inverse kinematics of redundant robots

This paper proposes a novel and global approach to solving the point-to-point inverse kinematics problem for highly redundant manipulators. Given an initial configuration of the robot, the problem is to find a reachable configuration that corresponds to a desired position and orientation of the end-effector. Central to our approach is the novel notion of kinematic roadmap for a manipulator. The kinematic roadmap captures the connectivity of the connected component of the free configuration space of the manipulator in a finite graph like structure. The point-to-point inverse kinematics problem is then solved using this roadmap. We provide completeness results for our algorithm. Our implementation of SEARCH is an efficient closed form solution, albeit local, to inverse kinematics that exploits the serial kinematic structure of serial manipulator arms. Initial experiments with a 7-DOF manipulator have been extremely successful.

A motion planning based approach for inverse kinematics of redundant robots: the kinematic roadmap

We propose a new approach to solving the point-to-point inverse kinematics problem for highly redundant manipulators. It is inspired by recent motion planning research and explicitly takes into account constraints related to joint limits and self-collisions. Central to our approach is the novel notion of kinematic roadmap for a manipulator. The kinematic roadmap captures the connectivity of the configuration space of a manipulator in a finite graph-like structure. The standard formulation of inverse kinematics problem is then solved using this roadmap. Our current implementation, based on Ariadne's Clew Algorithm, is composed of two sub-algorithms: EXPLORE, an appealingly simple algorithm that builds the kinematic roadmap by placing landmarks in the configuration space; and SEARCH, a local planner, that uses this roadmap to reach the desired end-effector configuration. Our implementation of SEARCH is an extremely efficient closed form solution, albeit local, to inverse kinematics that exploits the serial kinematic structure of serial manipulator arms. Initial experiments with a 7- dof manipulator have been extremely successful.

Inversion of multilayer neural network with modelling error compensation

The main contribution of this paper is to develop a method, using the Newton-Raphson me/hod, to search for the unknown part of the inputs of a multilayer neural network with given outputs and known inputs. To use the Newton-Raphson method, a method of expressing a jacobian by neural network parameters is developed first. A locally linearized relation between inputs and outputs of neural network is then derived. With this, iterative Newton-Raphson searches are performed until satisfactory results are obtained. The method shows rapid convergence, compared with previous approaches. While deriving the inverse of the neural network, some types of optimality, which are problem dependent, are resolved. Although the method shows fast convergence, this type of solution yields some inversion error due to the neural network modelling error. The second contribution of this paper is to propose a novel structure which can eliminate the inversion error caused by the neural network modelling error. The proposed method has a simple structure, but shows good performance as it has a feedforward structure and other beneficial features. Through computer experiments, the proposed methods show good performances in solving inverse kinematics of redundant robots and controlling nonlinear plant.

Outline of a method for the integration of linguistically expressed control strategies into the robot controller

In this paper a control scheme for the incorporation of linguistically formulated control strategies into the robot controller is discussed. The controller architecture is based upon the behavioral decomposition of activities, thus, it consists of multiple parallel working layers. The overall behavior of the system is the result of the fusion of several, possibly conflicting behaviors. Each behavior producing layer is encoded as a fuzzy algorithm. In its first layer the controller contains a recently introduced fuzzy algorithm for the solution of the inverse kinematics for redundant robots. The derivation of that iterative fuzzy algorithm is discussed and its application to a three link planar manipulator is demonstrated. As second layer of the controller, a linguistically formulated strategy for collision avoidance is developed. The performance of the proposed method is demonstrated by simulation results.

Recursive Formulation for the Inverse Kinematics of Redundant Robots Performing Tasks with Priority Order

A recursive arrangement is given for the inverse kinematic differential equations of redundant robots performing tasks with priority order. This formulation leads to simple and easy-to-handle equations, particularly suitable to be implemented in computer algorithms, for any number of sub-tasks. Moreover the recursive structure is extended to the solution at the acceleration level.