43 research outputs found
Approximation of the inverse kinematics of a robotic manipulator using a neural network
A fundamental property of a robotic manipulator system is that it is capable of accurately
following complex position trajectories in three-dimensional space. An essential component
of the robotic control system is the solution of the inverse kinematics problem which allows
determination of the joint angle trajectories from the desired trajectory in the Cartesian
space. There are several traditional methods based on the known geometry of robotic
manipulators to solve the inverse kinematics problem. These methods can become
impractical in a robot-vision control system where the environmental parameters can alter.
Artificial neural networks with their inherent learning ability can approximate the inverse
kinematics function and do not require any knowledge of the manipulator geometry.
This thesis concentrates on developing a practical solution using a radial basis function
network to approximate the inverse kinematics of a robot manipulator. This approach is
distinct from existing approaches as the centres of the hidden-layer units are regularly
distributed in the workspace, constrained training data is used and the training phase is
performed using either the strict interpolation or the least mean square algorithms. An
online retraining approach is also proposed to modify the network function approximation
to cope with the situation where the initial training and application environments are
different. Simulation results for two and three-link manipulators verify the approach.
A novel real-time visual measurement system, based on a video camera and image
processing software, has been developed to measure the position of the robotic manipulator
in the three-dimensional workspace. Practical experiments have been performed with a
Mitsubishi PA10-6CE manipulator and this visual measurement system. The performance
of the radial basis function network is analysed for the manipulator operating in two and
three-dimensional space and the practical results are compared to the simulation results.
Advantages and disadvantages of the proposed approach are discussed
Navigation of Automatic Vehicle using AI Techniques
In the field of mobile robot navigation have been studied as important task for the new generation of mobile robot i.e. Corobot. For this mobile robot navigation has been viewed for unknown environment. We consider the 4-wheeled vehicle (Corobot) for Path Planning, an autonomous robot and an obstacle and collision avoidance to be used in sensor based robot. We propose that the predefined distance from the robot to target and make the robot follow the target at this distance and improve the trajectory tracking characteristics. The robot will then navigate among these obstacles without hitting them and reach the specified goal point. For these goal achieving we use different techniques radial basis function and back-propagation algorithm under the study of neural network. In this Corobot a robotic arm are assembled and the kinematic analyses of Corobot arm and help of Phidget Control Panel a wheeled to be moved in both forward and reverse direction by 2-motor controller have to be done. Under kinematic analysis propose the relationships between the positions and orientation of the links of a manipulator. In these studies an artificial techniques and their control strategy are shown with potential applications in the fields of industry, security, defense, investigation, and others. Here finally, the simulation result using the webot neural network has been done and this result is compared with experimental data for different training pattern
Adaptive Control
Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems
Physical Human-Robot Interaction Control of an Upper Limb Exoskeleton with a Decentralized Neuro-Adaptive Control Scheme
Within the concept of physical human-robot interaction (pHRI), the most
important criterion is the safety of the human operator interacting with a high
degree of freedom (DoF) robot. Therefore, a robust control scheme is in high
demand to establish safe pHRI and stabilize nonlinear, high DoF systems. In
this paper, an adaptive decentralized control strategy is designed to
accomplish the abovementioned objectives. To do so, a human upper limb model
and an exoskeleton model are decentralized and augmented at the subsystem level
to enable a decentralized control action design. Moreover, human exogenous
force (HEF) that can resist exoskeleton motion is estimated using radial basis
function neural networks (RBFNNs). Estimating both human upper limb and robot
rigid body parameters, along with HEF estimation, makes the controller
adaptable to different operators, ensuring their physical safety. The barrier
Lyapunov function (BLF) is employed to guarantee that the robot can operate in
a safe workspace while ensuring stability by adjusting the control law. Unknown
actuator uncertainty and constraints are also considered in this study to
ensure a smooth and safe pHRI. Then, the asymptotic stability of the whole
system is established by means of the virtual stability concept and virtual
power flows (VPFs) under the proposed robust controller. The experimental
results are presented and compared to proportional-derivative (PD) and
proportional-integral-derivative (PID) controllers. To show the robustness of
the designed controller and its good performance, experiments are performed at
different velocities, with different human users, and in the presence of
unknown disturbances. The proposed controller showed perfect performance in
controlling the robot, whereas PD and PID controllers could not even ensure
stable motion in the wrist joints of the robot
Development of an End-Effector Type Therapeutic Robot with Sliding Mode Control for Upper-Limb Rehabilitation
Geriatric disorders, strokes, spinal cord injuries, trauma, and workplace injuries are all prominent causes of upper limb disability. A two-degrees-of-freedom (DoFs) end-effector type robot, iTbot (intelligent therapeutic robot) was designed to provide upper limb rehabilitation therapy. The non-linear control of iTbot utilizing modified sliding mode control (SMC) is presented in this paper. The chattering produced by a conventional SMC is undesirable for this type of robotic application because it damages the mechanical structure and causes discomfort to the robot user. In contrast to conventional SMC, our proposed method reduces chattering and provides excellent dynamic tracking performance, allowing rapid convergence of the system trajectory to its equilibrium point. The performance of the developed robot and controller was evaluated by tracking trajectories corresponding to conventional passive arm movement exercises, including several joints. According to the results of experiment, the iTbot demonstrated the ability to follow the desired trajectories effectively
Machine Learning in Robot Assisted Upper Limb Rehabilitation: A Focused Review
Robot-assisted rehabilitation, which can provide repetitive, intensive and high-precision physics training, has a positive influence on motor function recovery of stroke patients. Current robots need to be more intelligent and more reliable in clinical practice. Machine learning algorithms (MLAs) are able to learn from data and predict future unknown conditions, which is of benefit to improve the effectiveness of robot-assisted rehabilitation. In this paper, we conduct a focused review on machine learning-based methods for robot-assisted upper limb rehabilitation. Firstly, the current status of upper rehabilitation robots is presented. Then, we outline and analyze the designs and applications of MLAs for upper limb movement intention recognition, human-robot interaction control and quantitative assessment of motor function. Meanwhile, we discuss the future directions of MLAs-based robotic rehabilitation. This review article provides a summary of MLAs for robotic upper limb rehabilitation and contributes to the design and development of future advanced intelligent medical devices