Resolution of seven-axis manipulator redundancy: A heuristic issue

An approach is presented for the resolution of the redundancy of a seven-axis manipulator arm from the AI and expert systems point of view. This approach is heuristic, analytical, and globally resolves the redundancy at the position level. When compared with other approaches, this approach has several improved performance capabilities, including singularity avoidance, repeatability, stability, and simplicity

A comparison study of the numerical integration methods in the trajectory tracking application of redundant robot manipulators

Differential kinematic has a wide range application area in robot kinematics. The main advantage of the differential kinematic is that it can be easily implemented any kind of mechanisms. In differential kinematic method, Jacobian is used as a mapping operator in the velocity space. The joint velocities are required to be integrated to obtain the pose of the robot manipulator. This integration can be evaluated by using numerical integration methods, since the inverse kinematic equations are highly complex and nonlinear. Thus, the performances of the numerical integration methods affect the trajectory tracking application. This paper compares the performances of numerical integration methods in the trajectory tracking application of redundant robot manipulators. Four different and widely used numerical integration methods are implemented to the trajectory tracking application of the 7-DOF redundant robot manipulator named PA-10 and simulation results are given

An artificial neural network for redundant robot inverse kinematics computation

A redundant manipulator can be defined as a manipulator that has more degrees of freedom than necessary to determine the position and orientation of the end effector. Such a manipulator has dexterity, flexibility, and the ability to maneuver in presence of obstacles. One important and necessary step in utilizing a redundant robot is to relate the joint coordinates of the manipulator with the position and orientation of the end-effector. This specification is termed as the direct kinematics problem and can be written as x = f(q) where x is a vector representing the position and orientation of the end-effector, q is the Joint vector, and f is a continuous non-linear function defined by the design of the manipulator. The inverse kinematics problem can be stated as: Given a position and orientation of the end-effector, determine the joint vector that specifies this position a q = f -1(x). and orientation. That is, For non-trivial designs, f -1 cannot be expressed analytically. This paper presents a solution to the inverse kinematics problem for a redundant robot based on multilayer feed-forward artificial neural network

Inverse Kinematics Based on Fuzzy Logic and Neural Networks for the WAM-Titan II Teleoperation System

The inverse kinematic problem is crucial for robotics. In this paper, a solution algorithm is presented using artificial intelligence to improve the pseudo-inverse Jacobian calculation for the 7-DOF Whole Arm Manipulator (WAM) and 6-DOF Titan II teleoperation system. An investigation of the inverse kinematics based on fuzzy logic and artificial neural networks for the teleoperation system was undertaken. Various methods such as Adaptive Neural-Fuzzy Inference System (ANFIS), Genetic Algorithms (GA), Multilayer Perceptrons (MLP) Feedforward Networks, Radial Basis Function Networks (RBF) and Generalized Regression Neural Networks (GRNN) were tested and simulated using MATLAB. Each method for identification of the pseudo-inverse problem was tested, and the best method was selected from the simulation results and the error analysis. From the results, the Multilayer Perceptrons with Levenberg-Marquardt (MLP-LM) method had the smallest error and the fastest computation among the other methods. For the WAM-Titan II teleoperation system, the new inverse kinematics calculations for the Titan II were simulated and analyzed using MATLAB. Finally, extensive C code for the alternative algorithm was developed, and the inverse kinematics based on the artificial neural network with LM method is implemented in the real system. The maximum error of Cartesian position was 1.3 inches, and from several trajectories, 75 % of time implementation was achieved compared to the conventional method. Because fast performance of a real time system in the teleoperation is vital, these results show that the new inverse kinematics method based on the MLP-LM is very successful with the acceptable error

Singularity resolution in equality and inequality constrained hierarchical task-space control by adaptive non-linear least-squares

International audienceWe propose a robust method to handle kinematic and algorithmic singularities of any kinematically redundant robot under task-space hierarchical control with ordered equalities and inequalities. Our main idea is to exploit a second order model of the non-linear kinematic function, in the sense of the Newton's method in optimization. The second order information is provided by a hierarchical BFGS algorithm omitting the heavy computation required for the true Hessian. In the absence of singularities, which is robustly detected, we use the Gauss-Newton algorithm that has quadratic convergence. In all cases we keep a least-squares formulation enabling good computation performances. Our approach is demonstrated in simulation with a simple robot and a humanoid robot, and compared to state-of-the-art algorithms

An evolutionary approach to the trajectory planning of redundant robots

Several kinematic techniques for the trajectory optimization of redundant manipulators control the gripper using the pseudoinverse of the Jacobian. Nevertheless, these algorithms lead to a kind of chaotic motion with unpredictable arm configurations. This paper presents a new technique for solving the inverse kinematics problem for redundant manipulators that combines the closed-loop pseudoinverse method with genetic algorithms.N/

A new finger inverse kinematics method for an anthropomorphic hand

Proceedings of: 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO), December 7-11, 2011, Phuket (Thailand)In this paper, a new method for solving the inverse kinematics of the fingers of an anthropomorphic hand is proposed. Our approach combines a Modified Selectively Damped Least Squares (MSDLS) and Jacobian Transpose (JT) methods. The main advantages of this method with respect to the ordinary SDLS are: optimal Cartesian increment, shorter computation time and better response near singularity configurations. The original JT method exhibits a strong shattering with small magnitudes which occurs near the goal position or in the case of unreachable positions. Like in the SDLS, a damping factor was applied to each input singular vector to filter the undesirable behavior. A comparative study between the MSDLS applied to the inverse Jacobian and JT matrix is developed to investigate manipulator performance in critical end-point positions of the index finger of a commercial anthropomorphic robotic hand and also to evaluate the impact of the increment length on computation time.European Community's Seventh Framework Progra