Kinematic and Dynamic Approaches in Gait Optimization for Humanoid Robot Locomotion

Khusainov, Ramil; Klimchik, Alexandr; Magid, Evgeni

doi:10.1007/978-3-319-55011-4_15

Cited by 8 publications

(8 citation statements)

References 31 publications

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“…However, owing to the rapid growth of computational complexity with degrees-of-freedom, most of these studies were confined to models with a small number of degrees-of-freedom (often as few as two). Regarding motor learning as equivalent to some form of optimization process, real-time optimization of Lagrangian dynamic systems with as few as tens of degrees-of-freedom is profoundly challenging [Martínez-del Rincón et al, 2007, Khusainov et al, 2018. Due to what Richard Bellman called the "curse of dimensionality", optimization suffers deeply from the exponential growth of computational complexity, and often fails to converge to an optimal solution [Bellman, 2015].…”

Section: Why Study a Whip?-complexity Is Informativementioning

confidence: 99%

Dynamic Primitives Facilitate Manipulating a Whip

Nah

Krotov

Russo

et al. 2020

2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)

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Human dexterity far exceeds that of modern robots, despite a much slower neuromuscular system. Understanding how this is accomplished may lead to improved robot control. The slow neuromuscular system of humans implies that prediction based on some form of internal model plays a prominent role. However, the nature of the model itself remains unclear. To address this problem, we focused on one of the most complex and exotic tools humans can manipulate-a whip. We tested (in simulation) whether a distant target could be reached with a whip using a (small) number of dynamic primitives, whose parameters could be learned through optimization. This approach was able to manage the complexity of an (extremely) high degree-of-freedom system and discovered the optimal parameters of the upper-limb movement that achieved the task. A detailed model of the whip dynamics was not needed for this approach, which thereby significantly relieved the computational burden of task representation and performance optimization. These results support our hypothesis that composing control using dynamic motor primitives may be a strategy which humans use to enable their remarkable dexterity. A similar approach may contribute to improved robot control.

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Section: Why Study a Whip?-complexity Is Informativementioning

confidence: 99%

Dynamic Primitives Facilitate Manipulating a Whip

Nah

Krotov

Russo

et al. 2020

2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)

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“…Thus, in order for biped robot to obtain a steady gait with the footlift set up in advance, we find the minimum value of the two objective functions f 1 and f 2 , or similarly to find the minimum of the function f as (10) in which, F x1 + F x2 and F y1 + F y2 is the length and width of the biped robot foot, (0 ≤ ≤ 1) can be used to satisfactorily select the priority between the walking stability ( increase) and the variance with the desired foot-lifting magnitude ( decreased).…”

Section: Optimization Of the Biped Robot Trajectorymentioning

confidence: 99%

“…Maximo et al [9] introduced a new stable and quick model-free gait with specific arms movement for biped robots. 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].…”

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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“…Maximo et al 9 introduced a new stable and quick model-free gait with specific arm movement for biped robots. Khusainov et al 10 successfully combined kinematics and dynamics approaches in biped gait optimization for humanoid robot locomotion. Moreover, 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 and central force optimization (CFO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

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

Huan

Ánh

Kien

2019

Advances in Mechanical Engineering

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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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Kinematic and Dynamic Approaches in Gait Optimization for Humanoid Robot Locomotion

Cited by 8 publications

References 31 publications

Dynamic Primitives Facilitate Manipulating a Whip

Dynamic Primitives Facilitate Manipulating a Whip

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

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

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