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.

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 Performance Comparison between Particle Swarm Optimization and Differential Evolution Algorithms for Postman Delivery Routing Problem

2021 ◽  
Vol 11 (6) ◽  
pp. 2703
Author(s):  
Warisa Wisittipanich ◽  
Khamphe Phoungthong ◽  
Chanin Srisuwannapa ◽  
Adirek Baisukhan ◽  
Nuttachat Wisittipanit
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Performance Comparison ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Metaheuristic Methods ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms ◽  
Current Practices

Generally, transportation costs account for approximately half of the total operation expenses of a logistics firm. Therefore, any effort to optimize the planning of vehicle routing would be substantially beneficial to the company. This study focuses on a postman delivery routing problem of the Chiang Rai post office, located in the Chiang Rai province of Thailand. In this study, two metaheuristic methods—particle swarm optimization (PSO) and differential evolution (DE)—were applied with particular solution representation to find delivery routings with minimum travel distances. The performances of PSO and DE were compared along with those from current practices. The results showed that PSO and DE clearly outperformed the actual routing of the current practices in all the operational days examined. Moreover, DE performances were notably superior to those of PSO.

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.

Visual Analysis of Differential Evolution Algorithms

2018 ◽  
pp. 512-521
Author(s):  
A. Mexicano-Santoyo ◽  
R. Rodríguez-Jorge ◽  
A. Abrego ◽  
M. A. Jiménez ◽  
R. Zúñiga-Treviño ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Visual Analysis ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms
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