scholarly journals Dynamics Modeling of a Continuum Robotic Arm with a Contact Point in Planar Grasp

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Mohammad Dehghani ◽  
S. Ali A. Moosavian
Keyword(s):  
Degrees Of Freedom ◽  
Contact Point ◽  
Nonlinear Behavior ◽  
Robotic Arm ◽  
Dynamics Modeling ◽  
Real Time Control ◽  
Time Control ◽  
Cosserat Rod ◽  
Circular Arcs ◽  
Continuum Robotic

Grasping objects by continuum arms or fingers is a new field of interest in robotics. Continuum manipulators have the advantages of high adaptation and compatibility with respect to the object shape. However, due to their extremely nonlinear behavior and infinite degrees of freedom, continuum arms cannot be easily modeled. In fact, dynamics modeling of continuum robotic manipulators is state-of-the-art. Using the exact modeling approaches, such as theory of Cosserat rod, the resulting models are either too much time-taking for computation or numerically unstable. Thus, such models are not suitable for applications such as real-time control. However, based on realistic assumptions and using some approximations, these systems can be modeled with reasonable computational efforts. In this paper, a planar continuum robotic arm is modeled, considering its backbone as two circular arcs. In order to simulate finger grasping, the continuum arm experiences a point-force along its body. Finally, the results are validated using obtained experimental data.

Real Time, Near-Optimal Trajectory Planning of an Omni-Directional Vehicle

2001 ◽  
Author(s):  
Tamás Kalmár-Nagy ◽  
Pritam Ganguly ◽  
Raffaello D’Andrea
Keyword(s):  
Real Time ◽  
Optimal Trajectory ◽  
Degrees Of Freedom ◽  
Dynamic Environments ◽  
Real Time Control ◽  
Uncertain Environments ◽  
Time Control ◽  
Innovative Method ◽  
Optimal Trajectory Planning ◽  
Three Degrees Of Freedom

Abstract In this paper, we discuss an innovative method of generating near-optimal trajectories for a robot with omni-directional drive capabilities, taking into account the dynamics of the actuators and the system. The relaxation of optimality results in immense computational savings, critical in dynamic environments. In particular, a decoupling strategy for each of the three degrees of freedom of the vehicle is presented, along with a method for coordinating the degrees of freedom. A nearly optimal trajectory for the vehicle can typically be calculated in less than 1000 floating point operations, which makes it attractive for real-time control in dynamic and uncertain environments.

scholarly journals Virtualization of Robotic Hands Using Mobile Devices †

Robotics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 81
Author(s):  
Santiago T. Puente ◽  
Lucía Más ◽  
Fernando Torres ◽  
and Francisco A. Candelas
Keyword(s):  
Degrees Of Freedom ◽  
Robotic Hand ◽  
Robot Hand ◽  
Real Time Control ◽  
Robotic Hands ◽  
Robot Operating System ◽  
Server Side ◽  
Time Control ◽  
Unity 3D ◽  
Real Robot

This article presents a multiplatform application for the tele-operation of a robot hand using virtualization in Unity 3D. This approach grants usability to users that need to control a robotic hand, allowing supervision in a collaborative way. This paper focuses on a user application designed for the 3D virtualization of a robotic hand and the tele-operation architecture. The designed system allows for the simulation of any robotic hand. It has been tested with the virtualization of the four-fingered Allegro Hand of SimLab with 16 degrees of freedom, and the Shadow hand with 24 degrees of freedom. The system allows for the control of the position of each finger by means of joint and Cartesian co-ordinates. All user control interfaces are designed using Unity 3D, such that a multiplatform philosophy is achieved. The server side allows the user application to connect to a ROS (Robot Operating System) server through a TCP/IP socket, to control a real hand or to share a simulation of it among several users. If a real robot hand is used, real-time control and feedback of all the joints of the hand is communicated to the set of users. Finally, the system has been tested with a set of users with satisfactory results.

scholarly journals Noninvasive neuroimaging enhances continuous neural tracking for robotic device control

2019 ◽  
Vol 4 (31) ◽  
pp. eaaw6844 ◽  
Author(s):  
B. J. Edelman ◽  
J. Meng ◽  
D. Suma ◽  
C. Zurn ◽  
E. Nagarajan ◽  
...  
Keyword(s):  
Neural Control ◽  
Robotic Arm ◽  
User Engagement ◽  
Robotic Device ◽  
Real Time Control ◽  
Source Imaging ◽  
Home Setting ◽  
Time Control ◽  
Eeg Source Imaging ◽  
Noninvasive Neuroimaging

Brain-computer interfaces (BCIs) using signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems greatly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly improve the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework using electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the “brain” and “computer” components by increasing, respectively, user engagement through a continuous pursuit task and associated training paradigm and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by more than 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Last, this framework was deployed in a physical task, demonstrating a near-seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications for the eventual development and implementation of neurorobotics by means of noninvasive BCI.

Dynamic Control of Curve-Constrained Hyper-Redundant Manipulators

2004 ◽  
Vol 16 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Shugen Ma ◽  
◽  
Mitsuru Watanabe ◽  
Keyword(s):  
Degrees Of Freedom ◽  
Dynamic Control ◽  
Redundant Manipulators ◽  
Redundant Manipulator ◽  
Real Time Control ◽  
Feed Forward ◽  
Manipulator Dynamics ◽  
Time Control ◽  
Control Scheme ◽  
Feed Forward Controller

Hyper-redundant manipulators have high number of kinematic degrees of freedom, and possess unconventional features such as the ability to enter narrow spaces while avoiding obstacles. To control these hyper-redundant manipulators accurately, manipulator dynamics should be considered. This is, however, time-comsuming and makes implementation of real-time control difficult. In this paper, we propose a dynamic control scheme for hyper-redundant manipulators, which is based on analysis in defined posture space where three parameters were used to determine the manipulator posture. Manipulator dynamics are modeled on the parameterized form with the parameter of the posture space path. The posture space path-tracking feed-forward controller is then formulated on the basis of a parameterized dynamic equation. Computer simulation, in which a hyper-redundant manipulator traces the posture space path well by using the proposed feed-forward controller, proved that the hyper-redundant manipulator tracks the workspace path accurately.

Design and its characteristic analysis of a wheeled train uncoupling robot with multi-degrees-of-freedom

2015 ◽  
Vol 230 (10) ◽  
pp. 1673-1684 ◽  
Author(s):  
Jianjun Yao ◽  
Yuxuan Huang ◽  
Guilin Jiang ◽  
Shuang Gao ◽  
Rui Xiao ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Robot Manipulator ◽  
Vital Role ◽  
Real Time Control ◽  
Characteristic Analysis ◽  
Time Control ◽  
Operational Space ◽  
Wheeled Robot ◽  
Marshalling Yard ◽  
Cargo Transportation

Freight trains play a vital role in cargo transportation in the world. The freight cars need to be redistributed for marshalling according to different destinations in the hump yard. Humans are usually employed to uncouple the freight cars in the marshalling yard. However, the work environment is difficult to work in, because of its potential danger and the effects of the surrounding environment can have a very serious impact on human’s health. A wheeled robot is developed to replace humans to finish the uncoupling task. It has four degrees-of-freedom with flexible motion. Based on the D-H method, the kinematics, including the forward and the inverse kinematics, is firstly analysed. The dynamic analysis is then studied by Newton–Euler equations. The workspace is lastly investigated to verify its operational space such that the coupler can be easily reached by the robot manipulator. Those characteristic analyses provide a basis for motion planning and real-time control of the robot.

REACTIVE CONTROL OF EPILEPTIFORM DISCHARGES IN REALISTIC COMPUTATIONAL NEURONAL MODELS WITH BISTABILITY

2012 ◽  
Vol 23 (01) ◽  
pp. 1250032 ◽  
Author(s):  
MARC KOPPERT ◽  
STILIYAN KALITZIN ◽  
DEMETRIOS VELIS ◽  
FERNANDO LOPES DA SILVA ◽  
MAX A. VIERGEVER
Keyword(s):  
Degrees Of Freedom ◽  
Reactive Control ◽  
Real Time Control ◽  
Neuronal Dynamics ◽  
Seizure Reduction ◽  
Novel Technique ◽  
Time Control ◽  
Epileptiform Discharges ◽  
Stimulation Parameters ◽  
Bistable Dynamics

We aim to derive fully autonomous seizure suppression paradigms based on reactive control of neuronal dynamics. A previously derived computational model of seizure generation describing collective degrees of freedom and featuring bistable dynamics is used. A novel technique for real-time control of epileptogenicity is introduced. The reactive control reduces practically all seizures in the model. The study indicates which parameters provide the maximal seizure reduction with minimal intervention. An adaptive scheme is proposed that optimizes the stimulation parameters in nonstationary situations.

scholarly journals A novel energy-motion model for continuous sEMG decoding: from muscle energy to motor pattern

2020 ◽  
Author(s):  
Gang Liu ◽  
Lu Wang ◽  
Jing Wang
Keyword(s):  
Real Time ◽  
Degrees Of Freedom ◽  
Motor Pattern ◽  
Human Hand ◽  
Motion Model ◽  
Real Time Control ◽  
Prosthetic Hands ◽  
Time Control ◽  
Energy Mode ◽  
Muscle Energy

Myoelectric prosthetic hands create the possibility for amputees to control their prosthetics like native hands. However, user acceptance of the extant myoelectric prostheses is low. Unnatural control, lack of sufficient feedback, and insufficient functionality are cited as primary reasons. Recently, although many multiple degrees-of-freedom (DOF) prosthetic hands and tactile-sensitive electronic skins have been developed, no non-invasive myoelectric interfaces can decode both forces and motions for five-fingers independently and simultaneously. This paper proposes a myoelectric interface based on energy allocation and fictitious forces hypothesis by mimicking the natural neuromuscular system. The energy-based interface uses a kind of continuous “energy mode” in the level of the entire hand. According to tasks itself, each energy mode can adaptively and simultaneously implement multiple hand motions and exerting continuous forces for a single finger. Also, a few learned energy modes could extend to the unlearned energy mode, highlighting the extensibility of this interface. We evaluate the proposed system through off-line analysis and operational experiments performed on the expression of the unlearned hand motions, the amount of finger energy, and real-time control. With active exploration, the participant was proficient at exerting just enough energy to five fingers on “fragile” or “heavy” objects independently, proportionally, and simultaneously in real-time. The main contribution of this paper is proposing the bionic energy-motion model of hand: decoding a few muscle-energy modes of the human hand (only ten modes in this paper) map massive tasks of bionic hand.

scholarly journals Modeling and synthesizing quasi-time control laws for two degrees of freedom robotic arm

2019 ◽  
Author(s):  
C. X. Nguyen ◽  
H. N. Phan ◽  
L. D. Hoang ◽  
H. T. Nguyen ◽  
KH. D. Truong ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Robotic Arm ◽  
Control Laws ◽  
Two Degrees Of Freedom ◽  
Time Control

scholarly journals Myoelectric digit action decoding with multi-label, multi-class classification: an offline analysis

2020 ◽  
Author(s):  
Agamemnon Krasoulis ◽  
Kianoush Nazarpour
Keyword(s):  
Real Time ◽  
Degrees Of Freedom ◽  
Real Time Control ◽  
Artificial Joints ◽  
Time Control ◽  
Offline Analysis ◽  
Prosthesis Control ◽  
Multi Class Classification ◽  
Prosthetic Finger ◽  
Finger Control

ABSTRACTThe ultimate goal of machine learning-based myoelectric control is simultaneous and independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial joints. For prosthetic finger control, regression-based methods are typically used to reconstruct position/velocity trajectories from surface electromyogram (EMG) signals. Although such methods have produced highly-accurate results in offline analyses, their success in real-time prosthesis control settings has been rather limited. In this work, we propose action decoding, a paradigm-shifting approach for independent, multi-digit movement intent decoding based on multi-label, multi-class classification. At each moment in time, our algorithm classifies movement action for each available DOF into one of three categories: open, close, or stall (i.e., no movement). Despite using a classifier as the decoder, arbitrary hand postures are possible with our approach. We analyse a public dataset previously recorded and published by us, comprising measurements from 10 able-bodied and two transradial amputee participants. We demonstrate the feasibility of using our proposed action decoding paradigm to predict movement action for all five digits as well as rotation of the thumb. We perform a systematic offline analysis by investigating the effect of various algorithmic parameters on decoding performance, such as feature selection and choice of classification algorithm and multi-output strategy. The outcomes of the offline analysis presented in this study will be used to inform the real-time implementation of our algorithm. In the future, we will further evaluate its efficacy with real-time control experiments involving upper-limb amputees.

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