摘要
根据移动机器人的导航任务,提出基于粒子群优化(PSO)算法的行为参数多目标分层优化方法。将导航方向与导航速度相关的参数按优先级进行PSO算法分层选取,使机器人在路径近似最优的基础上实现导航时间最少。仿真结果表明,该方法可以提高导航效率,实现导航决策的逐步求精,从而改善机器人在未知环境下的自主导航性能。
This paper presents a multi-objective hierarchical optimization method based on Particle Swarm Optimization（PSO） algorithm for behavior parameters by analyzing the navigation task of mobile robots. To minimize the navigation time of mobile robot based on near optimal path, the behavior parameters of navigation direction and velocity are optimized by PSO-based optimization. Simulation results show that the method can improve the efficiency of optimization. It can realize the stepwise accurate decision-making, and improve the performance of robot＇s autonomous navigation under unknown environment.
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第4期190-192,共3页
Computer Engineering
基金
宁波市自然科学基金资助项目(2009A610102)
浙大宁波理工学院科研启动基金资助项目(1140657G801)
关键词
自主移动机器人
行为动力学模型
多目标分层优化
粒子群优化
autonomous mobile robot
behavior dynamics model
multi-objective hierarchical optimization
Particle Swarm Optimization（PSO）
