基于人工免疫网络机器人路径规划算法的进一步研究

Further Study of Robot Path Planning Algorithm Based on Artificial Immune Net Theory

本文进一步完善了基于人工免疫网络的移动机器人路径发现与规划算法,给出了算法的设计思想和流程详细的描述;并基于马尔可夫链理论,从数学上证明了该算法的收敛性;通过势场法、神经网络算法和遗传算法三种常用的移动机器人路径发现与规划算法的对比实验,表明文章所设计的算法具有很好的柔性,能够适应于不同的规划环境,解决了其它规划算法无法克服的规划难题以及欺骗性问题,表现出了高度的智能性。

Based on the artificial immune net theory, the moving robot path finding and planning algorithm is perfected in the paper. The design ideas and the detail programs are depicted. The convergence of the algorithm was proven by the Markov-Chain. The experiments comparing with the gravitation algorithm, the artificial neural networks and the gene algorithm have been done. It shows that the algorithm has very nice flexibility, is suitable for the different planning conditions, solutes the cheat problem and reveals its higher intelligence.