OPPOSITION-BASED LEARNING PARTICLE SWARM OPTIMIZATION OF RUNNING GAIT FOR HUMANOID ROBOT
| Autor: | Liang Yang, Chunjian Deng, Song Xijia |
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| Rok vydání: | 2015 |
| Předmět: |
Yaw Moment
Computer science business.industry lcsh:T ZMP Opposition based learning Humanoid Robot Particle swarm optimization opposition learning lcsh:Technology Computer Science::Robotics Running gait Control and Systems Engineering lcsh:Technology (General) lcsh:T1-995 Artificial intelligence Electrical and Electronic Engineering gait planning business Humanoid robot |
| Zdroj: | International Journal on Smart Sensing and Intelligent Systems, Vol 8, Iss 2 (2015) |
| Popis: | This paper investigates the problem of running gait optimization for humanoid robot. In order to reduce energy consumption and guarantee the dynamic balance including both horizontal and vertical stability, a novel running gait generation based on opposition-based learning particle swarm optimization (PSO) is proposed which aims at high energy efficiency with better stability. In the proposed scheme of running gait generation, a population initiation policy based on domain knowledge is employed, which helps to guide searching direction guidance at the beginning. A population update mechanism based on opposition learning is proposed for speeding up the convergence and improving the diversity. Simulation results validate the proposed method. |
| Databáze: | OpenAIRE |
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