摘要
为了提高气动肌肉四足机器人路径全局规划和自动控制能力,提出基于粒子群智能优化的机器人路径全局规划算法。根据机器人规划路径移动路径进行运动学模型构造,构建气动肌肉四足机器人路径规划的控制约束参量,以机器人的关节自由运动项作为控制自变量,采用线性插值优化方法进行机器人路径空间分布式结构重组,提取气动肌肉四足机器人的关节力学参数和避障参数,采用多源信息融合方法进行运动力学与环境空间的特征匹配,采用粒子群智能优化方法进行机器人路径全局规划过程中的自适应寻优,根据环境中信息素进行粒子群寻优过程中的导引控制,实现机器人路径全局规划优化控制。仿真结果表明,采用该方法进行气动肌肉四足机器人路径规划的适应度较好,空间避障能力较强,能够快速确定最优路径。
In order to improve the global path planning and automatic control ability of pneumatic muscle quadruped robot,a robot path global planning algorithm based on particle swarm optimization is proposed.According to the kinematic model of robot planning path movement path,the control constraint parameters of pneumatic muscle quadruped robot path planning are constructed.Taking the joint free motion term of robot as the control independent variable,the linear interpolation optimization method is used to reconstruct the distributed structure of robot path space,and the joint mechanics parameters and obstacle avoidance parameters of pneumatic muscle quadruped robot are extracted.The multi-source information fusion method is used to match the characteristics of dynamics and environment space,and the particle swarm optimization method is used to optimize the robot path.According to the pheromone in the environment,the guidance control is carried out in the process of particle swarm optimization,and the optimal control of robot path global planning is realized.The simulation results show that the proposed method has good adaptability and strong ability to avoid obstacles,and can quickly determine the optimal path of pneumatic muscle quadruped robot.
作者
樊国根
蒙芳
Fan Guogen;Meng Fang(Guangzhou Huali Science and Technology Vocational College,Guangzhou 511325,China;Huali College Guangdong University of Technology,Guangzhou 511325,China)
出处
《电子测量技术》
2020年第7期41-45,共5页
Electronic Measurement Technology
关键词
粒子群
机器人
路径
全局规划
控制
particle swarm optimization
robot
path
global planning
control
