摘要
在对PUMA机器人空间路径进行BP算法环境建模与目标建模的基础上,针对传统粒子群优化(PSO)算法搜索空间有限、容易陷入局部最优点的缺陷,提出了一种改进的粒子群优化(MPSO)算法。该算法引入了基于全局信息反馈的重新初始化过程机制,并对PUMA机器人空间路径进行了优化。仿真实验表明,该算法的应用不仅降低了求解逆运动方程的难度,还能得到全局最优解,显著地提高了PUMA机器人空间路径优化的效率。
On the basis of target modeling and environment modeling with BP algorithm for PUMA robot space path, aiming at the problems of limited search space of traditional particle swarm optimization （ PSO ） algorithm and to easily bring into local optimal point, the modified PSO （ MPSO ） is proposed. The re-initial process mechanism based on global information feedback is introduced, and the space path of PUMA robot is optimized. The simulation indicates that the algorithm avoids the difficulty in solving the inverse motion equation, and ensures that the global optimal solution will be obtained. The efficiency of path optimization is increased greatly.
出处
《自动化仪表》
CAS
北大核心
2010年第2期12-15,20,共5页
Process Automation Instrumentation
