Using PSO for a walk of a biped robot

Kherici, Nada; Ali, Yamina Mohamed Ben

doi:10.1016/j.jocs.2014.01.006

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…An unbounded number of applications use the techniques of trajectory generation aiming to speculate, plane and formalize the optimum path especially in robotics applications which are the subject of this research. Important applications in robotics such as the robots that use legs [1][2][3][4], navigation robots [5][6][7][8][9][10][11][12] and robot's manipulators [13][14][15][16][17][18][19][20][21][22][23] are in dire need of using the techniques of optimal trajectory in order to optimize the usage of these applications according to the estimates of peripheral conditions.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Optimization in Robotic Applications, Survey of Recent Developments

Almasri¹,

Mk²

2021

Preprint

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Trajectory Optimization (TO) is the sequence of processes that are considered in order to produce the best path that mends the overall performance or reduces the consumption of the resources where the restriction system remains maintained. In this survey, an inclusive review of the latest advancements in modeling and optimization of trajectory generation in robotic applications will be discussed broadly. In recent times, numerous studies have employed optimal control techniques involving direct and indirect methods in order to convert the authentic Trajectory Optimization problem into a constrained parameter optimization problem. Moreover, a huge variety of optimization algorithms such as Genetic Algorithms (GA), Simulated Annealing (SA), Sequential Quadratic Programming (SQP), and Particle Swarm Optimization (PSO) are used aiming to find the optimal solutions for trajectory planning. It is observable that, minimizing the jerk, energy consumption, and execution time among the most widely used design objectives, on the other hand, the robot’s joints configurations and the motor torques are the most used design variables. This paper aims to review the fundamental techniques and their coincident robotic applications in the field of trajectory optimization aiming to afford some steering for related researchers.

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Section: Introductionmentioning

confidence: 99%

Trajectory Optimization in Robotic Applications, Survey of Recent Developments

Almasri¹,

Mk²

2021

Preprint

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“…Concerning parameters identification, many techniques have been applied: conventional such as the simplex method [9], recursive prediction error method [16], subspace method [17], the calculus of state variables sensibilities [3], and the minimization of an Euclidean-distance criterion [18]; nonconventional like the neural networks [19,20] and evolutionary methods [21,22]. In this work, standing on the successful results obtained by the application of Particle Swarm Optimization (PSO) algorithm in various areas like robotics, solar energy, image segmentation, telecommunication [23][24][25][26][27], its use will be considered especially in the field of models' parameter identification [22,[28][29][30][31][32]. In this way, this algorithm will be applied for a model that describes an activated sludge wastewater treatment process.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of an Activated Sludge Wastewater Treatment Process Based on Particle Swarm Optimization Method

Khoja

Ladhari

Sakly

et al. 2018

Mathematical Problems in Engineering

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The current paper is entirely devoted to show the applicability of Particle Swarm Optimization (PSO) algorithm as a parameter identification method for a representative model of an Activated Sludge Wastewater Treatment Process (ASWWTP) with alternating phases. The model of identification is composed of two linear submodels: one for the aerobic phase and the other for the anoxic phase. In order to prove the efficiency of the proposed method, its performance is compared with another classical method called Simplex Search Algorithm (SSA) as well as with the experimental data.

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“…It has good characteristics including fast convergence, easy implementation, and few parameters. These characteristics make PSO a potential method for solving optimization problems in various areas [14][15][16], and PSO is considered especially in the field of models' parameter identification [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of the Vortex-Induced Vertical Force Model Using a New Adaptive PSO

Tian

Yan

Xiao

2018

Mathematical Problems in Engineering

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The paper proposes a new adaptive PSO (NAPSO) that adaptively adjust the inertial weight of every particle according to its own current fitness. In NAPSO, the searching ability of each particle is controlled by the inertial weight. In pursuit of the optimal solution, if a particle has a rather small value of normalized fitness, it has a small inertia weight so as to increase local searching ability; on the contrary, it has a large inertia weight to increase global searching ability. Simulation results include three parts: the NAPSO shows fast convergence and good stability compared with other PSOs; the NAPSO shows good fit and short run-time compared with GA and GALMA; according to the identified parameters, the time history of predicted vertical displacement is quite in accordance with the time history of measured displacement. As far as the nonlinear VIVF model is concerned, the NAPSO is a simple and effective identification method.

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Using PSO for a walk of a biped robot

Cited by 13 publications

References 13 publications

Trajectory Optimization in Robotic Applications, Survey of Recent Developments

Trajectory Optimization in Robotic Applications, Survey of Recent Developments

Parameter Identification of an Activated Sludge Wastewater Treatment Process Based on Particle Swarm Optimization Method

Parameter Identification of the Vortex-Induced Vertical Force Model Using a New Adaptive PSO

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