Stable Gait Optimization for Small-Sized Humanoid Robot Using CFO

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.

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

Engineering Computations ◽

10.1108/ec-03-2018-0154 ◽

2019 ◽

Vol 36 (2) ◽

pp. 599-621 ◽

Cited By ~ 4

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.

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Posture control for humanoid robot on uneven ground and slopes using inertial sensors

Advances in Mechanical Engineering ◽

10.1177/1687814020957181 ◽

2020 ◽

Vol 12 (9) ◽

pp. 168781402095718

Author(s):

Shu-Yin Chiang ◽

Jin-Long Wang

Keyword(s):

Humanoid Robot ◽

Inertial Sensors ◽

Balance Control ◽

Gait Pattern ◽

Posture Control ◽

Balance System ◽

Open Platform ◽

Mean Filter ◽

Stable Gait ◽

Control Balance

We designed a stable gait pattern and posture-control balance system to enable a biped humanoid robot to maintain balance and avoid falling when walking on uneven ground or slopes. In this study, we first examined the problem of gait generation and the balance of a humanoid robot and then proposed a posture-control balance system using the inertial sensors of a gyroscope and accelerometer to sense the tilt angle of the robot according to the environment. To process the data obtained by the sensors, the mean filter was applied to eliminate the noise in the data, and the complementary filter was used to properly combine the data from both the gyroscope and accelerometer. The system further modifies the gait and posture of the robot based on the results obtained through a fuzzy system to attain the angle of balance and stabilization. A robot with an open platform was used to test the implementation of the proposed algorithm, and the experimental results demonstrated that the robot could successfully maintain balance when walking uphill and downhill on uneven surfaces. Moreover, because only one parameter needs to be adjusted when applying the balance-control system, the system can be easily extended to any related humanoid robot.

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Offline path planning, dynamic modeling and gait optimization of a 2D humanoid robot

2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM) ◽

10.1109/icrom.2014.6990889 ◽

2014 ◽

Cited By ~ 6

Author(s):

Majid Sadedel ◽

Aghil Yousefi-koma ◽

Majid Khadiv

Keyword(s):

Path Planning ◽

Dynamic Modeling ◽

Humanoid Robot ◽

Gait Optimization

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Humanoid robot gait optimization: Stretched simulated annealing and genetic algorithm a comparative study

10.1063/1.4825562 ◽

2013 ◽

Author(s):

Ana I. Pereira ◽

José Lima ◽

Paulo Costa

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Comparative Study ◽

Humanoid Robot ◽

Gait Optimization

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

Advances in Mechanical Engineering ◽

10.1177/1687814019888082 ◽

2019 ◽

Vol 11 (11) ◽

pp. 168781401988808 ◽

Cited By ~ 2

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.

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Combining gait optimization with passive system to increase the energy efficiency of a humanoid robot walking movement

10.1063/1.4912422 ◽

2015 ◽

Author(s):

Ana I. Pereira ◽

José Lima ◽

Paulo Costa

Keyword(s):

Energy Efficiency ◽

Humanoid Robot ◽

Passive System ◽

Gait Optimization

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Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm

Frontiers in Neurorobotics ◽

10.3389/fnbot.2020.600885 ◽

2021 ◽

Vol 14 ◽

Author(s):

Chongben Tao ◽

Jie Xue ◽

Zufeng Zhang ◽

Feng Cao ◽

Chunguang Li ◽

...

Keyword(s):

Humanoid Robot ◽

Group Structure ◽

Particle Swarm ◽

Humanoid Robots ◽

Optimization Method ◽

Gait Training ◽

Walking Ability ◽

Particle Swarm Optimizer ◽

Training Environment ◽

Gait Optimization

To improve the fast and stable walking ability of a humanoid robot, this paper proposes a gait optimization method based on a parallel comprehensive learning particle swarm optimizer (PCLPSO). Firstly, the key parameters affecting the walking gait of the humanoid robot are selected based on the natural zero-moment point trajectory planning method. Secondly, by changing the slave group structure of the PCLPSO algorithm, the gait training task is decomposed, and a parallel distributed multi-robot gait training environment based on RoboCup3D is built to automatically optimize the speed and stability of bipedal robot walking. Finally, a layered learning approach is used to optimize the turning ability of the humanoid robot. The experimental results show that the PCLPSO algorithm achieves a quickly optimal solution, and the humanoid robot optimized possesses a fast and steady gait and flexible steering ability.

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Walking Trajectory Optimization Algorithm For Robot Humanoid on Synthetic Grass

EMITTER International Journal of Engineering Technology ◽

10.24003/emitter.v6i1.229 ◽

2018 ◽

Vol 6 (1) ◽

pp. 35-61 ◽

Cited By ~ 1

Author(s):

Dimas Pristovani Riananda ◽

Ardik Wijayanto ◽

Ali Husein Alasiry ◽

A. Subhan Khalilullah

Keyword(s):

Control System ◽

Optimization Algorithm ◽

Trajectory Optimization ◽

Humanoid Robot ◽

Main Issue ◽

Robot Soccer ◽

The Stability ◽

Trajectory System ◽

Gait Optimization

Synthetic grass surface is a new rule in international robot soccer competition (RoboCup). The main issue in the development of the RoboCup competition today is about how to make a humanoid robot walk above the field of synthetic grass. Because of that, the humanoid robot needs a system that can be implemented into the walking algorithm. This paper describes how to maintain the stability of humanoid robot called EROS by using walking trajectory algorithm without a control system. The establishment of the walking trajectory system is combined with a process of landing optimization using deceleration and heel-strikes gait optimization. This system has been implemented into a humanoid robot with 52 cm of height and walking on synthetic grass with different speeds. By adding optimization, the robot walks more stable from 32% to 80% of stability. In the next research, the control system will be added to improve the stability.

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