Inverse Kinematics Problem In Robotics Research Articles

Applying Neural Network Architecture for Inverse Kinematics Problem in Robotics

One of the most important problems in robot kinematics and control is, finding the solution of Inverse Kinematics. Inverse kinematics computation has been one of the main problems in robotics research. As the Complexity of robot increases, obtaining the inverse kinematics is difficult and computationally expensive. Traditional methods such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. As alternative approaches, neural networks and optimal search methods have been widely used for inverse kinematics modeling and control in robotics This paper proposes neural network architecture that consists of 6 sub-neural networks to solve the inverse kinematics problem for robotics manipulators with 2 or higher degrees of freedom. The neural networks utilized are multi-layered perceptron (MLP) with a back-propagation training algorithm. This approach will reduce the complexity of the algorithm and calculation (matrix inversion) faced when using the Inverse Geometric Models implementation (IGM) in robotics. The obtained results are presented and analyzed in order to prove the efficiency of the proposed approach.

뉴럴 러닝 기반 로봇 손가락의 역기구학

일반적으로 인간손에 있는 검지 손가락의 평면운동은 3개의 관절운동에 의해 이루어진다. 이러한 운동을 위해서는 기본적으로 역기구학 문제를 풀어야 하는데, 이것은 로봇 손을 이용한 파지나 조작행위에 있어서 필수적이다. 따라서 본 논문에서는 이러한 로봇 손가락의 역기구학 문제를 지능적으로 해결할 수 있는 뉴럴 러닝에 기반한 방법을 제안하고자 한다. 제안된 방법은 뉴럴 러닝에 있어서 동적인 학습율을 적용함으로써 보다 빠른 학습이 가능하고, 생체모방에 근거한 인간 손가락의 운동특성을 고려하는 것이 특징이다. 제안된 방법의 유용성을 입증하기 위하여 시뮬레이션을 수행한다. The planar motion of the index finger in general human hands is usually implemented by the actuation of three joints. This task requires a technique to determine the joint combination for each fingertip position which is well-known as the inverse kinematics problem in robotics. Especially, it is an essential work for grasping and manipulation tasks by robotic and humanoid fingers. In this paper, an intelligent neural learning scheme for solving such inverse kinematics is presented. Specifically, a multi-layered neural network is utilized for effective inverse kinematics, where a dynamic neural learning algorithm is employed for fast learning. Also, a bio-mimetic feature of general human fingers is incorporated to the learning scheme. The usefulness of the proposed approach is verified by simulations.

Reliability-based approach to the inverse kinematics solution of robots using Elman's networks

The solution of inverse kinematics problem of redundant manipulators is a fundamental problem in robot control. The inverse kinematics problem in robotics is the determination of joint angles for a desired cartesian position of the end effector. For the solution of this problem, many traditional solutions such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. Furthermore, many neural network approaches have been done to this problem. But the neural network-based solutions are not much reliable due to the error at the end of learning. Therefore, a reliability-based neural network inverse kinematics solution approach has been presented, and applied to a six-degrees of freedom (dof) robot manipulator in this paper. The structure of the proposed method is based on using three networks designed parallel to minimize the error of the whole system. Elman network, which has a profound impact on the learning capability and performance of the network, is chosen and designed according to the proposed solution method. At the end of parallel implementation, the results of each network are evaluated using direct kinematics equations to obtain the network with best result.

A dual neural network for kinematic control of redundant robot manipulators

The inverse kinematics problem in robotics can be formulated as a time-varying quadratic optimization problem. A new recurrent neural network, called the dual network, is presented in this paper. The proposed neural network is composed of a single layer of neurons, and the number of neurons is equal to the dimensionality of the workspace. The proposed dual network is proven to be globally exponentially stable. The proposed dual network is also shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators.

Inverse kinematics in robotics using neural networks

The inverse kinematics problem in robotics requires the determination of the jointangles for a desired position of the end-effector. For this underconstrained and ill-conditioned problem we propose a solution based on structured neural networks that can be trained quickly. The proposed method yields multiple and precise solutions and it is suitable for real-time applications.

Intelligent Computational Scheme for Robot Inverse Kinematic Transformations

This paper describes an intelligent computational scheme to obtain feasible solutions to the problem of inverse kinematics in robotics. The proposed scheme consists of a recurrent neural network and a knowledge-based (KB) system. The latter, based on the requirements and specifications of the robot task-space provided by the user, determines the neural network configuration and checks the robot link angles during the process of computation against the physical constraints. On the other hand, a recurrent neural network provides low-level computational features such as functional approximation, parallelism, learning and adaptation capabilities. The inverse kinematics problem in robotics involves the determination of joint variables for a desired end-effector position in the robot task-space. This problem is difficult in the sense that for a given end-effector position there can be many solutions, and some of these solutions may not be practically feasible due to the physical limitations imposed by the robot...

Solution to the inverse kinematics problem in robotics by neural networks: [formula omitted], Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104

Solution to the inverse kinematics problem in robotics by neural networks: [formula omitted], Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104