Novel Stable Walking for Humanoid Robot Using Particle Swarm Optimization Algorithm

Huan, Tran Thien; Ánh, Hồ Phạm Huy

doi:10.2991/aiie-15.2015.90

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…In (11), the mass distribution m i and coordinates (x i , y i , z i ) of the stages are defined in Fig. 6.…”

Section: Zmp Trajectory Calculationmentioning

confidence: 99%

“…Khusainov et al (2018) [10] successfully combined kinematic and dynamic approaches in gait optimization for humanoid robot locomotion. Intelligent algorithms are applied in this method to optimize the gait generator for humanoid robots such as genetic algorithm (GA) [8], algorithm for optimal swarm (PSO) [11], modified differential algorithm (MDE) [12,13]. Shaffi in [14] introduces the humanoid robot achieved a stable gait by using the Fourier series gait generator.…”

Section: Introductionmentioning

confidence: 99%

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

Huan

Ánh

2018

Vietnam J. Mech.

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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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“…In (11), the mass distribution m i and coordinates (x i , y i , z i ) of the stages are defined in Fig. 6.…”

Section: Zmp Trajectory Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Huan

Ánh

2018

Vietnam J. Mech.

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“…Gait planning is a research hotspot for humanoid robots, and it provides some technical support for humanoid robots walking like humans. The methods of gait planning can be broadly divided into three categories: human walking parameter-based methods (Baoping et al, 2015 ; Hereid et al, 2018 ), humanoid walking model-based methods (Sato et al, 2010 ; Winkler et al, 2018 ), and intelligent algorithm-based methods (Huan and Anh, 2015 ; Elhosseini et al, 2019 ). The method based on human walking parameters makes the gait of humanoid robots more similar to the way humans walk, but it costs a lot of time to find suitable gait parameters from human walking data and apply them to humanoid robots.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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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“…Khusainov et al [10] successfully combined kinematics and dynamics approaches in gait optimization for humanoid robot locomotion. The fact is that, nowadays, intelligent algorithms are being increasingly applied in this domain to optimize the gait generator parameters for humanoid robots such as GA [8], Particle Swarm Optimization (PSO) algorithm [11], Modified Differential Evolution (MDE) algorithm [12], Central Force Optimization (CFO) algorithm [13]. Shaffi in [14] introduced the humanoid robot achieved a stable gait by using the Fourier seriesbased gait generator.…”

Section: Introductionmentioning

confidence: 99%

Optimal Walking Gait Generator for Biped Robot Using Modified Jaya Optimization Technique

Ánh¹,

Huan²

2020

IJCIS

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This paper treats the optimization of the biped walking trajectory that can be used as a reference trajectory for control. The biped robot is modeled as a kinetic chain of 11 links connected by 10 joints. The inverse kinematics of the biped is derived for the specified positions of the hips and feet. The objective is to optimize the biped robot able to stably and naturally walking with preset foot-lift magnitude (or preset hip-shift, or preset step-length). The stability of the biped robot is quantified by the distance between the ZMP and the foot center in the step cycle, which represents the first objective function. Additionally, for the biped robot to follow the preset foot-lift value, the difference between the magnitude of foot-lift value and the foot-lift preset value represents the second objective function. Specifically, we minimize the value of the two objective functions by considering the gait parameters of biped robot as variables. The new Jaya optimization algorithm is innovatively applied to optimize the biped gait parameters as to ensure the biped robot walking robustly and steadily. The efficiency of the proposed Jaya-based identification method is compared with the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), the Central Force Optimization (CFO) and improved Differential Evolution (DE) [Modified Differential Evolution (MDE)] algorithms. The simulation results tested on the real small-sized biped robot system HUBOT-4 demonstrate that the novel proposed algorithm offers an efficient and stable gait for biped robot with precise foot-lift value.

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Novel Stable Walking for Humanoid Robot Using Particle Swarm Optimization Algorithm

Cited by 8 publications

References 6 publications

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

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

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

Optimal Walking Gait Generator for Biped Robot Using Modified Jaya Optimization Technique

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