Motion generation and stability of the marionette style elastic tendon driven robot arm

2016 ◽  
Author(s):  
K. Mitobe ◽  
J. Togashi ◽  
T. Seki ◽  
G. Capi
Keyword(s):  
Robot Arm ◽  
Motion Generation

A Robot Arm Motion Generation Based on Learning of Objects Shape and Human Motion

2018 ◽  
Vol 2018 (0) ◽  
pp. 2A1-F14
Author(s):  
Sho TAJIMA ◽  
Tokuo TSUJI ◽  
Yosuke SUZUKI ◽  
Tetsuyou WATANABE ◽  
Kenichi MOROOKA ◽  
...  
Keyword(s):  
Human Motion ◽  
Robot Arm ◽  
Motion Generation ◽  
Arm Motion

scholarly journals Biologically Inspired Robotic Arm Control Using an Artificial Neural Oscillator

2010 ◽  
Vol 2010 ◽  
pp. 1-16 ◽  
Author(s):  
Woosung Yang ◽  
Jaesung Kwon ◽  
Nak Young Chong ◽  
Yonghwan Oh
Keyword(s):  
Neural Oscillator ◽  
Robot Arm ◽  
Motion Generation ◽  
Biologically Inspired ◽  
Neural Oscillators ◽  
Control Scheme ◽  
Robot Arm Control ◽  
Simulation And Experiment ◽  
Self Adaptation ◽  
Robotic Arm Control

We address a neural-oscillator-based control scheme to achieve biologically inspired motion generation. In general, it is known that humans or animals exhibit novel adaptive behaviors regardless of their kinematic configurations against unexpected disturbances or environment changes. This is caused by the entrainment property of the neural oscillator which plays a key role to adapt their nervous system to the natural frequency of the interacted environments. Thus we focus on a self-adapting robot arm control to attain natural adaptive motions as a controller employing neural oscillators. To demonstrate the excellence of entrainment, we implement the proposed control scheme to a single pendulum coupled with the neural oscillator in simulation and experiment. Then this work shows the performance of the robot arm coupled to neural oscillators through various tasks that the arm traces a trajectory. With these, the real-time closed-loop system allowing sensory feedback of the neural oscillator for the entrainment property is proposed. In particular, we verify an impressive capability of biologically inspired self-adaptation behaviors that enables the robot arm to make adaptive motions corresponding to an unexpected environmental variety.

scholarly journals Energy and Path Optimization of Robot Arm Simulator Via Multi-Objective Evolutionary Algorithm

2018 ◽  
Vol 7 (4.36) ◽  
pp. 404
Author(s):  
Z. Mohamed ◽  
N. S. Khusaini ◽  
M. A.M Anuar ◽  
R. Ramly ◽  
M. A Anuar ◽  
...  
Keyword(s):  
Minimum Distance ◽  
Minimum Energy ◽  
Path Optimization ◽  
Intelligent Robot ◽  
Robot Arm ◽  
Motion Generation ◽  
Neural Controller ◽  
New Approach ◽  
Neural Controllers ◽  
Arm Motion

The performance of robot arm motion generated via neural network are presented in this paper. The robot arm motion for obstacle avoidance simultaneously optimizing three functions; minimum distance, minimum time and minimum energy are generated. Four different initial and goal position had been chosen to test and analyze the performance of generated neural controller. The same neural controllers can be employed to a different range of initial and goal position. The motion generated yield good results in the simulator. In this research a new approach for intelligent robot arm path and motion generation are successfully implemented. 

Scoop the Semi-Liquid Objects Using a Spoon-Equipped Robot Arm for Meal Support

2018 ◽  
Author(s):  
Daiki Yoshioka ◽  
Ming Ding ◽  
Gustavo Alfonso Garcia Ricardez ◽  
Jun Takamatsu ◽  
Tsukasa Ogasawara
Keyword(s):  
Robot Arm ◽  
Motion Generation ◽  
Auxiliary Operation ◽  
Inner Surface

In this research, a scooping motion generation method is proposed to scoop the semi-liquid objects from different containers automatically for meal support purpose. A spoon equipped robot arm is used. Based on the pre-measured shape of the containers, the robot arm can move the spoon to trace the inner surface of containers continuously. We also control the rotation of the spoon to scoop more semi-liquid object every time by imitating human’s scooping motion. A scraping motion is also generated as the auxiliary operation to gather the remaining semi-liquid object, which can realize an increase in the scooping amount. In the experiment, we tested the generated scooping motion for two containers and four type of semi-liquid objects. The scooped amount and the scooping times are measured and compared. The result shows that about 85.9% object on average could be scooped out.

scholarly journals Dynamic manipulation of unknown string by robot arm: Realizing momentary string shapes

2020 ◽  
Author(s):  
Kenta Tabata ◽  
Hiroaki Seki ◽  
Tokuo Tsuji ◽  
Tatsuhiro Hiramitsu ◽  
Masatosh Hikizu
Keyword(s):  
Angular Velocity ◽  
High Speed ◽  
3D Model ◽  
String Model ◽  
Robot Motion ◽  
Robot Arm ◽  
Velocity Data ◽  
Motion Generation ◽  
Motion Data ◽  
Dynamic Manipulation

Abstract In this paper we propose dynamic manipulation for flexible object by using high speed robot arm. We consider dynamic manipulation for unknown string and describe how to manipulete it. Paticulary, we focus on the achived momentary string shapes. For example, momentary string shapes is like a J , C or d. In our strategy for dynamic manipulation of unknown string, manipulation is achieved through repeating 3steps: manipulation of string by robot arm, string parameter estimation. A string is described as the physical-dased 3D model. And ,motion data for robot arm is given as each joint angular velocity data. For simulation of motion, we input the each joint angular velocity data,and initial paramaretars of modeled string.This simulation calculate not only the motion of robot arm but also motion of modeled ropes which occured by robot motion. Parametar estimation is to string parametar by comparing image of the real manipulation with string model and motion generation by estimated model. Repeatly each step, we realize dynamic manipulation of unknown string. Finaly, we show the some experiment of dynamic manipulation ,and we demonstrate effective of parametar estimation and validity.

Adaptive arm motion generation of humanoid robot operating in dynamic environments

2014 ◽  
Vol 41 (2) ◽  
pp. 124-134 ◽  
Author(s):  
Zulkifli Mohamed ◽  
Mitsuki Kitani ◽  
Genci Capi
Keyword(s):  
Humanoid Robot ◽  
Dynamic Environments ◽  
Robot Motion ◽  
Robot Arm ◽  
Objective Functions ◽  
Motion Generation ◽  
Content Type ◽  
Neural Controllers ◽  
Wide Range ◽  
Arm Motion

Purpose – The purpose of this paper is to compare the performance of the robot arm motion generated by neural controllers in simulated and real robot experiments. Design/methodology/approach – The arm motion generation is formulated as an optimization problem. The neural controllers generate the robot arm motion in dynamic environments optimizing three different objective functions; minimum execution time, minimum distance and minimum acceleration. In addition, the robot motion generation in the presence of obstacles is also considered. Findings – The robot is able to adapt its arm motion generation based on the specific task, reaching the goal position in simulated and experimental tests. The same neural controller can be employed to generate the robot motion for a wide range of initial and goal positions. Research limitations/implications – The motion generated yield good results in both simulation and experimental environments. Practical implications – The robot motion is generated based on three different objective functions that are simultaneously optimized. Therefore, the humanoid robot can perform a wide range of tasks in real-life environments, by selecting the appropriate motion. Originality/value – A new method for adaptive arm motion generation of a mobile humanoid robot operating in dynamic human and industrial environments.

An Efficient Motion Generation Method for Redundant Humanoid Robot Arm Based on the Intrinsic Principles of Human Arm Motion

2018 ◽  
Vol 15 (06) ◽  
pp. 1850026 ◽  
Author(s):  
Meng Li ◽  
Weizhong Guo ◽  
Rongfu Lin ◽  
Changzheng Wu ◽  
Liangliang Han
Keyword(s):  
Muscle Strength ◽  
Humanoid Robot ◽  
Computing Time ◽  
Strength Properties ◽  
Robotic Arm ◽  
Robot Arm ◽  
Kinematic Redundancy ◽  
Motion Generation ◽  
Human Arm ◽  
Arm Motion

The aim of this paper is trying to propose an efficient method of inverse kinematics and motion generation for redundant humanoid robot arm based on the intrinsic principles of human arm motion. The intrinsic principle analysis takes into account both the skeletal kinematics and muscle strength properties. Firstly, this work analyzed the kinematic redundancy problem of a human arm. By analyzing the biological feature of a human arm, the kinematic redundancy boils down to the uncertainty of elbow position. Secondly, because the muscle’s kinematic and strength properties are critical for simulating biometric motion authentically, the muscle strength property was introduced as the criterion for configuration identification and motion generation. Three types of limb configuration, dog walking, gecko climbing, and human walking limb configuration were analyzed, and two geometrical configuration identification rules were deduced to generate biomimetic motion for humanoid robotic arms. By comparing the proposed method with other five IK methods, the proposed method significantly deduced the computing time. Finally, the configuration identification rules were used to generate motions for a 7-DoF humanoid robotic arm. The results showed that the biological rules can generate biomimetic, smooth arm motions for a redundant humanoid robotic arm.

Assistive Humanoid Robot Arm Motion Generation in Dynamic Environment Based on Neural Networks

2015 ◽  
Vol 3 (2) ◽  
pp. 115-121
Author(s):  
Genci Capi ◽  
Zulkifli Mohamed ◽  
Mitsuki Kitani ◽  
Shin-ichiro Kaneko
Keyword(s):  
Neural Networks ◽  
Humanoid Robot ◽  
Dynamic Environment ◽  
Robot Arm ◽  
Motion Generation ◽  
Arm Motion
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