Optimal stable gait for nonlinear uncertain humanoid robot using central force optimization algorithm

2019 ◽  
Vol 36 (2) ◽  
pp. 599-621 ◽  
Author(s):  
Tran Thien Huan ◽  
Ho Pham Huy Anh
Keyword(s):  
Differential Evolution ◽  
Humanoid Robot ◽  
Differential Evolution Algorithm ◽  
Humanoid Robots ◽  
Central Force ◽  
Content Type ◽  
Force Optimization ◽  
Gait Parameters ◽  
Stable Gait ◽  
Central Force Optimization

Purpose The purpose of this paper is to design a novel optimized biped robot gait generator which plays an important role in helping the robot to move forward stably. Based on a mathematical point of view, the gait design problem is investigated as a constrained optimum problem. Then the task to be solved is closely related to the evolutionary calculation technique. Design/methodology/approach Based on this fact, this paper proposes a new way to optimize the biped gait design for humanoid robots that allows stable stepping with preset foot-lifting magnitude. The newly proposed central force optimization (CFO) algorithm is used to optimize the biped gait parameters to help a nonlinear uncertain humanoid robot walk robustly and steadily. The efficiency of the proposed method is compared with the genetic algorithm, particle swarm optimization and improved differential evolution algorithm (modified differential evolution). Findings The simulated and experimental results carried out on the small-sized nonlinear uncertain humanoid robot clearly demonstrate that the novel algorithm offers an efficient and stable gait for humanoid robots with respect to accurate preset foot-lifting magnitude. Originality/value This paper proposes a new algorithm based on four key gait parameters that enable dynamic equilibrium in stable walking for nonlinear uncertain humanoid robots of which gait parameters are initiatively optimized with CFO algorithm.

scholarly journals Optimal nature-walking gait for humanoid robot using Jaya optimization algorithm

2019 ◽  
Vol 11 (11) ◽  
pp. 168781401988808 ◽  
Author(s):  
Tran Thien Huan ◽  
Ho Pham Huy Anh ◽  
Cao Van Kien
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Humanoid Robot ◽  
Biped Robot ◽  
Humanoid Robots ◽  
Walking Gait ◽  
Identification Approach ◽  
Stable Gait ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms

This article proposes a new method used to optimize the design process of nature-walking gait generator that permits biped robot to stably and naturally walk with preset foot-lift magnitude. The new Jaya optimization algorithm is innovatively applied to optimize the biped gait four key parameters initiatively applied to ensure the uncertain nonlinear humanoid robot walks robustly and steadily. The efficiency of the proposed Jaya-based identification approach is compared with the central force optimization and improved differential evolution (modified differential evolution) algorithms. The simulation and experimental results tested on the original small-sized biped robot HUBOT-4 convincingly demonstrate that the novel proposed algorithm offers an efficient and stable gait for humanoid robots with precise height of foot-lift value.

The Pransky interview: Dr Jun Ho Oh, Professor and Director of Humanoid Robot Research Center, KAIST; Cofounder, Rainbow Robotics Co.

2017 ◽  
Vol 44 (6) ◽  
pp. 695-699
Author(s):  
Joanne Pransky
Keyword(s):  
Mechanical Engineering ◽  
Humanoid Robot ◽  
Industrial Robot ◽  
Science And Technology ◽  
Vice President ◽  
Humanoid Robots ◽  
Real Time Control ◽  
Commercial Development ◽  
Content Type ◽  
The University

Purpose The following paper is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD-turned-entrepreneur regarding the commercialization and challenges of bringing a technological invention to market. This paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr Jun Ho Oh, Professor of Mechanical Engineering at the Korea Advanced Institute of Science and Technology (KAIST) and Director of KAIST’s Hubolab. Determined to build a humanoid robot in the early 2000s to compete with Japan’s humanoids, Dr Oh and KAIST created the KHR1. This research led to seven more advanced versions of a biped humanoid robot and the founding of the Robot for Artificial Intelligence and Boundless Walking (Rainbow) Co., a professional technological mechatronics company. In this interview, Dr Oh shares the history and success of Korea’s humanoid robot research. Findings Dr Oh received his BSc in 1977 and MSc in Mechanical Engineering in 1979 from Yonsei University. Oh worked as a Researcher for the Korea Atomic Energy Research Institute before receiving his PhD from the University of California (UC) Berkeley in mechanical engineering in 1985. After his PhD, Oh remained at UC Berkeley to do Postdoctoral research. Since 1985, Oh has been a Professor of Mechanical Engineering at KAIST. He was a Visiting Professor from 1996 to 1997 at the University of Texas Austin. Oh served as the Vice President of KAIST from 2013-2014. In addition to teaching, Oh applied his expertise in robotics, mechatronics, automatic and real-time control to the commercial development of a series of humanoid robots. Originality/value Highly self-motivated and always determined, Dr Oh’s initial dream of building the first Korean humanoid bipedal robot has led him to become one of the world leaders of humanoid robots. He has contributed widely to the field over the nearly past two decades with the development of five versions of the HUBO robot. Oh led Team KAIST to win the 2015 DARPA Robotics Challenge (DRC) and a grand prize of US$2m with its humanoid robot DRC-HUBO+, beating 23 teams from six countries. Oh serves as a robotics policy consultant for the Korean Ministry of Commerce Industry and Energy. He was awarded the 2016 Changjo Medal for Science and Technology, the 2016 Ho-Am Prize for engineering, and the 2010 KAIST Distinguished Professor award. He is a member of the Korea Academy of Science and Technology.

scholarly journals Comparative stable walking gait optimization for small-sized biped robot using meta-heuristic optimization algorithms

2018 ◽  
Vol 40 (4) ◽  
pp. 407-424
Author(s):  
Tran Thien Huan ◽  
Ho Pham Huy Anh
Keyword(s):  
Humanoid Robot ◽  
Differential Evolution Algorithm ◽  
Biped Robot ◽  
The Novel ◽  
Biped Robots ◽  
Walking Gait ◽  
Gait Parameters ◽  
Lift Height ◽  
Evolution Algorithm ◽  
Gait Optimization

This paper proposes a new way to optimize the biped walking gait design for biped robots that permits stable and robust stepping with pre-set foot lifting magnitude. The new meta-heuristic CFO-Central Force Optimization algorithm is initiatively applied to optimize the biped gait parameters as to ensure to keep biped robot walking robustly and steadily. The efficiency of the proposed method is compared with the GA-Genetic Algorithm, PSO-Particle Swarm Optimization and Modified Differential Evolution algorithm (MDE). The simulated and experimental results carried on the prototype small-sized humanoid robot demonstrate that the novel meta-heuristic CFO algorithm offers an efficient and stable walking gait for biped robots with respect to a pre-set of foot-lift height value.

Follow-the-leader motion strategy for multi-section continuum robots based on differential evolution algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
GuoHua Gao ◽  
Pengyu Wang ◽  
Hao Wang
Keyword(s):  
Differential Evolution ◽  
Kinematic Model ◽  
Differential Evolution Algorithm ◽  
Open Loop ◽  
Continuum Robots ◽  
Content Type ◽  
Loop Control ◽  
Continuum Robot ◽  
Motion Strategy ◽  
The Continuum

Purpose The purpose of this paper is to present a follow-the-leader motion strategy for multi-section continuum robots, which aims to make the robot have the motion ability in a confined environment and avoid a collision. Design/methodology/approach First, the mechanical design of a multi-section continuum robot is introduced and the forward kinematic model is built. After that, the follow-the-leader motion strategy is proposed and the differential evolution (DE) algorithm for calculating optimal posture parameters is presented. Then simulations and experiments are carried out on a series of predefined paths to analyze the performance of the follow-the-leader motion. Findings The follow-the-leader motion can be well performed on the continuum robots this study proposes in this research. The experimental results show that the deviation from the path is less than 9.7% and the tip error is no more than 15.6%. Research limitations/implications Currently, the follow-the-leader motion is affected by the following factors such as gravity and continuum robot design. Furthermore, the position error is not compensated under open-loop control. In future work, this paper will improve the accuracy of the robot and introduce a closed-loop control strategy to improve the motion accuracy. Originality/value The main contribution of this paper is to present an algorithm to generate follow-the-leader motion of the continuum robot based on DE. This method is suitable for solving new arrangements in the process of following a nonlinear path. Then, it is expected to promote the engineering application of the continuum robot.

An optimal wavelet-based multi-modality medical image fusion approach based on modified central force optimization and histogram matching

2019 ◽  
Vol 78 (18) ◽  
pp. 26373-26397 ◽  
Author(s):  
Heba M. El-Hoseny ◽  
Zeinab Z. El Kareh ◽  
Wael A. Mohamed ◽  
Ghada M. El Banby ◽  
Korany R. Mahmoud ◽  
...  
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Central Force ◽  
Medical Image Fusion ◽  
Force Optimization ◽  
Histogram Matching ◽  
Fusion Approach ◽  
Central Force Optimization

scholarly journals An Improved Central Force Optimization Algorithm for Multimodal Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jie Liu ◽  
Yu-ping Wang
Keyword(s):  
Central Force ◽  
Multimodal Optimization ◽  
High Dimensions ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Clustering Technique ◽  
Force Optimization ◽  
Comparison Results ◽  
Complex Statistical Analysis ◽  
Central Force Optimization

This paper proposes the hybrid CSM-CFO algorithm based on the simplex method (SM), clustering technique, and central force optimization (CFO) for unconstrained optimization. CSM-CFO is still a deterministic swarm intelligent algorithm, such that the complex statistical analysis of the numerical results can be omitted, and the convergence intends to produce faster and more accurate by clustering technique and good points set. When tested against benchmark functions, in low and high dimensions, the CSM-CFO algorithm has competitive performance in terms of accuracy and convergence speed compared to other evolutionary algorithms: particle swarm optimization, evolutionary program, and simulated annealing. The comparison results demonstrate that the proposed algorithm is effective and efficient.

Human-Like Gait Adaptation to Slippery Surfaces for the NAO Robot Wearing Instrumented Shoes

2020 ◽  
Vol 17 (03) ◽  
pp. 2050007
Author(s):  
João P. Ferreira ◽  
Guilherme Franco ◽  
A. Paulo Coimbra ◽  
Manuel Crisóstomo
Keyword(s):  
Friction Coefficient ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Field Of Study ◽  
Daily Lives ◽  
Gait Adaptation ◽  
Nao Robot ◽  
Walking Pattern ◽  
Gait Parameters ◽  
Instrumented Shoes

Gait development for bipedal/humanoid robots has been a field of study with a lot of attention for several years and is becoming increasingly important as robots slowly become part of our daily lives. Therefore, it is expectable that robots should adopt human-like behaviors in order to make their interactions with humans more natural and studies have been made involving robots that have a natural, human-like gait. However, very few focus on scenarios with slippery floors. In this paper, the humanoid robot NAO is used and the effects of a human-based walking pattern on the robot’s balance when walking on floors with different slipperiness degrees were analyzed. The simulations are done having the robot equipped with specially developed shoes that enable the measurement of the friction coefficient. From that analysis, an algorithm that automatically adapts the gait parameters to the floor’s slipperiness was developed, in order to prevent the robot from suffering unexpected disturbances and possibly falling over. This paper focusses on preventing balance disturbances, instead of correcting them.

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