Desired Motion Trajectory Research Articles

Effect of the deformation in the inertia forces on the inverse dynamics of planar flexible mechanical systems

The inverse dynamics problem for articulated structural systems such as robotic manipulators is the problem of the determination of the joint actuator forces and motor torques such that the system components follow specified motion trajectories. In many of the previous investigations, the open loop control law was established using an inverse dynamics procedure in which the centrifugal and Coriolis inertia forces are linearized such that these forces in the flexible model are the same as those in the rigid body model. In some other investigations, the effect of the nonlinear centrifugal and Coriolis forces is neglected in the analysis and control system design of articulated structural systems. It is the objective of this investigation to study the effect of the linearization of the centrifugal and Coriolis forces on the nonlinear dynamics of constrained flexible mechanical systems. The virtual work of the inertia forces is used to define the complete nonlinear centrifugal and Coriolis force model. This nonlinear model that depends on the rate of the finite rotation and the elastic deformation of the deformable bodies is used to obtain the solution of the inverse dynamics problem, thus defining the joint torques that produce the desired motion trajectories. The effect of the linearization of the mass matrix as well as the centrifugal and Coriolis forces on the obtained feedforward control law is examined numerically. The results presented in this investigation are obtained using a slider crank mechanism with a flexible connecting rod.

A neural network based identification of environments models for compliant control of space robots

Many space robotic systems would be required to operate in uncertain or even unknown environments. The problem of identifying such environment for compliance control is considered. In particular, neural networks are used for identifying environments that a robot establishes contact with. Both function approximation and parameter identification (with fixed nonlinear structure and unknown parameters) results are presented. The environment model structure considered is relevant to two space applications: cooperative execution of tasks by robots and astronauts, and sample acquisition during planetary exploration. Compliant motion experiments have been performed with a robotic arm, placed in contact with a single-degree-of-freedom electromechanical environment. In the experiments, desired contact forces are computed using a neural network, given a desired motion trajectory. Results of the control experiments performed on robot hardware are described and discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Analysis of Motion and Internal Loading of Objects Grasped by Multiple Cooperating Manipulators

The motion and internal loads induced on an object manipulated by two or more robotic mechanisms are con sidered. In particular, for a desired motion trajectory of the object, the question of load distribution among the arms is analyzed, with particular attention given to the internal loading of the object. A new representation of the load distribution problem is given by the introduction of a particular "nonsqueezing" pseudoinverse, which is shown to possess properties that expose the underlying structure of the problem. It is expected that by using this pseudoin verse new insight will be gained and necessary analysis simplified in a number of aspects of multiple manipulator research. A number of these aspects are detailed and illustrated using a two-armed example.

Realization of robot motion based on a learning method

To make a robot track a given desired motion trajectory, a learning control scheme is proposed which is based on the repeatability of robot motion. In this scheme the robot obtains a desired motion by repeating trials (test motion). A merit of this control scheme is that the input torque pattern that generates the desired motion can be formed without estimating the physical parameters of robot dynamics. In practice, to allow the robot motion to approach the desired one in each trial, the input torque given to the robot at the present trial is modified only by the velocity signal of the real robot motion at the previous trial and the desired one. The convergence to the desired motion is theoretically proved for a linear time-varying mechanical system, which is an approximate representation of nonlinear robot dynamics in the vicinity of the desired motion. The effectiveness of this control scheme is demonstrated through actual experiments in which a revolute-type manipulator with three degrees of freedom is used, and the desired motion trajectory is given not only in terms of joint-angle coordinates but also in terms of task-oriented coordinates.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Discrete Output Decentralized Control of Robotic Manipulators

Abstract The task of a robotic manipulator's controller consists in the control of the robot's end effector in such a way that it is able to track a desired motion trajectory. All the proposed control techniques assume that manipulator state variables (joint positions and velocities) are all measurable. In this paper a new discrete time decentralized control strategy is developed, which only makes use of joint position measurements. The boundedness on the position tracking error is obtained via a Lyapunov-based stability analysis. The trade-off existing between control computational burden and position tracking accuracy is discussed and a case study is finally worked out

Generalized algorithm for computing the 4‐3‐4 N‐axis robotic trajectory

AbstractA generalized inversion technique and algorithm is presented for the purpose of computing the 9n coefficients pertaining to the three segments—i.e., liftoff, transitory, and setdown—of a motion trajectory of an n‐axis robot manipulator. The technique and the computer program presented is general enough to be immediately applicable to an n‐axis robot manipulator if only the desired initial and final position, velocity, acceleration, as well as the intermediate “via” positions are given. A number of examples are presented for a wide spectrum of these constraint conditions on the desired motion trajectories.

Bettering operation of Robots by learning

AbstractThis article proposes a betterment process for the operation of a mechanical robot in a sense that it betters the next operation of a robot by using the previous operation's data. The process has an iterative learning structure such that the (k + 1)th input to joint actuators consists of the kth input plus an error increment composed of the derivative difference between the kth motion trajectory and the given desired motion trajectory. The convergence of the process to the desired motion trajectory is assured under some reasonable conditions. Numerical results by computer simulation are presented to show the effectiveness of the proposed learning scheme.