DOF Planar Manipulator Research Articles

Torque Minimized Design of a Light Weight 3 DoF Planar Manipulator

The main objective of this paper is to design a robotic arm with all the motors located at the base so that the arm becomes lighter than the conventional arms where each motor is set on the links themselves which will lead to an extra weight of the motor to be held by previous motors. The main advantage of this development is to decrease the torque required to control the arm. The motion of motors transferred to links via aluminum pulleys, bearings, and timing belts that yield to the need of lighter motors and hence a lighter manipulator. The maximum torque of each joint has been calculated then compared with the torque required using conventional structures. The proposed arm has been built and tested. The simulation and experimental results reveal a noticeable improvement in the required maximum torque, which was reduced to less than half in some links.

A Newton-like algorithm to compute the inverse of a nonlinear map that converges in finite time

This paper deals with the problem of inverting a nonlinear map. The proposed solution consists in a nonlinear state observer, which mimics a Newton-like algorithm, that allows to determine the inverse of a given diffeomorphism in finite time. The results are illustrated by application to the inverse kinematics of a three DOF planar manipulator.

Manipulator End-Effector Position Control

Abstract The paper deals with manipulator end-effector position control. At first the problem of inverse kinematics is introduced. The dynamic model of manipulator by Euler – Lagrange method is derived. In order to achieve required end-effector position the feedback control method is introduced for non-linear differential equations system. In the conclusion all mentioned methods are demonstrated on 2 DOF planar manipulator.

Adaptive Force/Velocity control for opening unknown doors1

The problem of door opening is fundamental for robots operating in domestic environments. Since these environments are generally unstructured, a robot must deal with several types of uncertainties associated with the dynamics and kinematics of a door to achieve successful opening. The present paper proposes a dynamic force/velocity controller which uses adaptive estimation of the radial direction based on adaptive estimates of the door hinge's position. The control action is decomposed into estimated radial and tangential directions, which are proved to converge to the corresponding actual values. The force controller uses reactive compensation of the tangential forces and regulates the radial force to a desired small value, while the velocity controller ensures that the robot's end-effector moves with a desired tangential velocity. The performance of the control scheme is demonstrated in simulation with a 2 DoF planar manipulator opening a door.

Differential Flatness of a Class of $n$-DOF Planar Manipulators Driven by 1 or 2 Actuators

A fully actuated system can execute any joint trajectory. However, if a system is under-actuated, not all joint trajectories are attainable. The authors have actively pursued novel designs of under-actuated robotic arms which are both controllable and feedback linearizable. These robots can perform point-to-point motions in the state space, but potentially can be designed to work with fewer actuators, hence with lower cost. With this same spirit, the technical note investigates the property of differential flatness for a class of planar under-actuated open-chain robots having a specific inertia distribution, but driven by only one or two actuators. This technical note addresses the following theoretical question: what placement of one or two actuators will make an n-DOF planar robot differentially flat if it is designed so that its center of mass always lies at joint 2?

Neuro-fuzzy based approach for hybrid force/position robot control

This paper presents neuro-fuzzy control approach for MIMO systems. Motivated by the hybrid force/position control of robot manipulator problem, a systematic design procedure is proposed for fuzzy rules generation and optimization. The construction of the proposed neuro-fuzzy controller consists in three phases. In the first phase, which is called parameters learning phase, the neuro-fuzzy system is considered as feed-forward neural network and back-propagation learning algorithm is then applied for parameters identification in order to map Input/Output data. In the second phase, a new clustering algorithm based on the inclusion concept is used for optimal clusters identification. Finally, the fuzzy rules base is generated and optimized. The performances of the control approach are improved by introducing a Reference Model (RM), which is able to cope with Robot/Environment interaction variations. A 2 DOF planar manipulator force/position control simulation is presented and results discussed.

Dynamic simplification of three degree of freedom manipulators with closed chains

A method is presented for designing manipulators to have simplified dynamics. It is based on adding a link group to an open kinematic chain to form a closed chain without changing the degrees of freedom of the open chain. The mass property of the link group is designed to make the closed chain have a diagonal inertia matrix. The conditions of mass distribution are derived under which the inertia matrices become diagonal. The advantage of the proposed method is that the manipulator dynamics can be treated as a decoupled linear system thereby greatly simplifying the control implementation. As examples of the technique we apply it to the design of a 3 DOF planar manipulator and a 3 DOF spatial manipulator.

Collision: Modeling, simulation and identification of robotic manipulators interacting with environments

The performance of many robotic tasks depends greatly on their dynamic collision behavior. This article presents a simple method for modeling and simulating collision behavior in manipulators. The main goal in this task is to provide informative contact models. The proposed models encompasscollision attributes which comprise not only (local) contact surface properties but also structural properties of the environmental object and the manipulator. With this method, the entire dynamic and interactive motion of the manipulator with the environmental object can be simulated effectively. This is verified by our simulation results. To facilitate our investigation, a 2 DOF planar elbow manipulator with PD control is considered in the simulations as well as theoretical analysis. The simulation results are used to highlight the collision attributes which affect collision behavior and to study the effects of these attributes on the manipulator-work environment safety and performance. On the other hand, the reliable operation of intelligent robotic systems in unstructured environments requires the estimation of collision attributes before the prediction of the collision behavior can be completed. For this purpose, we introduce the notion ofcollision identification. The present paper introduces a framework for collision identification in robotic tasks. The proposed framework is based on Artificial Neural Networks (ANNs) and provides fast and relatively reliable identification of the collision attributes. The simulation results are used to generate training data for the set of ANNs. A modularized ANN-based architecture is also developed to reduce the training effort and to increase the accuracy of ANNs. The test results indicate the satisfactory performance of the proposed collision identification system.

Sliding Mode Adaptive Control of a 2 DOF Planar Manipulator

Abstract The application of a modified sliding mode controller to the adaptive control of a 2 dof planar manipulator is discussed . The novelty is in a controller structure characterized by the parallel composition of a linear plus a nonlinear switching component. Such a Structure allows one to obtain the smoothness and the disturbance rejection properties of the classical PID compensator while at the same time retaining the robustness properties of the more recently proposed sliding mode compensator. The controller gains are adjusted via self-tuning and adaptive pole placement.