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

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.

scholarly journals Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Jun Zheng ◽  
Hai-Feng Ling ◽  
Qiu Guan
Keyword(s):  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Method ◽  
Evolutionary Information ◽  
Adaptive Mechanism ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Adaptive Parameters ◽  
Individual Particles

Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.

PARTICLE SWARM OPTIMIZATION METHOD FOR IMAGE CLUSTERING

2005 ◽  
Vol 19 (03) ◽  
pp. 297-321 ◽  
Author(s):  
M. OMRAN ◽  
A. P. ENGELBRECHT ◽  
A. SALMAN
Keyword(s):  
Satellite Images ◽  
State Of The Art ◽  
Particle Swarm ◽  
Optimization Method ◽  
Image Clustering ◽  
Control Parameters ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Harmonic Means ◽  
Better Than

An image clustering method that is based on the particle swarm optimizer (PSO) is developed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together with similar image primitives. To illustrate its wide applicability, the proposed image classifier has been applied to synthetic, MRI and satellite images. Experimental results show that the PSO image classifier performs better than state-of-the-art image classifiers (namely, K-means, Fuzzy C-means, K-Harmonic means and Genetic Algorithms) in all measured criteria. The influence of different values of PSO control parameters on performance is also illustrated.

scholarly journals SIMULATION AND ANALYZES OF INVERSE-KINEMATIC MODEL OF HUMANOID ROBOT HAND

2019 ◽  
pp. 117-122
Author(s):  
Martin Varga ◽  
Filip Filakovský ◽  
Ivan Virgala
Keyword(s):  
Numerical Modeling ◽  
Humanoid Robot ◽  
Least Squares Method ◽  
Mechanical Design ◽  
Kinematic Model ◽  
Humanoid Robots ◽  
Optimization Method ◽  
Inverse Kinematic ◽  
Robot Hand ◽  
Kinematic Modeling

Urgency of the research. Nowadays robotics and mechatronics come to be mainstream. With development in these areas also grow computing fastidiousness. Since there is significant focus on numerical modeling and algorithmization in kinematic and dynamic modeling. Target setting. Suitable approach for numerical modeling is important from the view of time consumption as well as stability of computing. Actual scientific researches and issues analysis. Designing and modeling of humanoid robots have high interest in the field of robotics. The hardware and mechanical design of robots is on significantly higher level in comparison with software of robots. So, modeling and control of robots is in the interest of researchers. Uninvestigated parts of general matters defining. Comparison of methods for numerical modeling of inverse kinematics. The research objective. Comparing four methods from the view of performance and stability. The statement of basic materials. This paper investigates the area of kinematic modeling of humanoid robot hand and simulation in MATLAB. Conclusions. The paper investigated inverse kinematic model approaches. There were analyzed pseudoinverse method, transpose of Jacobian method, damped least squares method as an optimization method. The results of the simulations show the advantages of optimization method. During the simulations it never fail in comparison with other tested methods.

scholarly journals Rolling Optimization Method for Humanoid Robots

2015 ◽  
Vol 20 (2) ◽  
pp. 78
Author(s):  
Riadh Zaier
Keyword(s):  
Humanoid Robot ◽  
Humanoid Robots ◽  
Optimization Method ◽  
Initial Value ◽  
Walking Motion ◽  
Zero Moment ◽  
Rolling Optimization ◽  
Single Support Phase ◽  
Locomotion Controller ◽  
Support Phase

In this paper, a method of optimizing the rolling amplitude needed for a stable and smooth walking movement of a humanoid robot is considered. The optimization algorithm was based on minimizing a cost function defined by the rolling overshoot. The amplitude of the rolling during locomotion was calculated using the lateral zero moment point (ZMP) position. The initial value of the rolling was the static rolling that corresponds to the position of the ZMP at the center of the support polygon. The algorithm consisted of performing a ZMP calculation at two points that correspond to single support phases. Simplifying the robot as an inverted pendulum, the gyro feedback controller parameters were tuned to have a passive-like walking motion and a faster response of the robot state to the equilibrium point at single support phase. Experimental results, using HOAP-3 of Fujitsu, showed that the algorithm was successfully implemented along with the locomotion controller. With the optimal rolling technique, the humanoid robot could exhibit a stable and smooth walking movement.  

scholarly journals Motion coordination for humanoid jumping using maximized joint power

2021 ◽  
Vol 13 (6) ◽  
pp. 168781402110284
Author(s):  
Xuechao Chen ◽  
Wenxi Liao ◽  
Zhangguo Yu ◽  
Haoxiang Qi ◽  
Xinyang Jiang ◽  
...  
Keyword(s):  
Output Power ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Optimization Method ◽  
Motion Coordination ◽  
Joint Power ◽  
Simulation And Experiment ◽  
Solid Foundation ◽  
Environmental Adaptability ◽  
Practical Factors

Jumping capability of humanoid robots can be considered as one of the cruxes to improve the performance of future humanoid robot applications. This paper presents an optimization method on a three-linkage system to achieve a jumping behavior, which is followed by the clarification of the mathematical modeling and motor-joint model with practical factors considered. In consideration of the constraints of ZMP and the performance of the motor, the output power of the joint motors is maximized as much as possible to achieve a higher height. Finally, the optimization method is verified by the simulation and experiment. Different from other electric driven robots, which take the output power of the joint as the constraint, we maximize the output power of the joint to optimize the hopping performance of the robot. Realizing dynamic jumping of humanoid robots can also provide a solid foundation for further research on running, which can greatly enhance the environmental adaptability.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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