Global Path Planning of Wheeled Robots Using a Multi-Objective Memetic Algorithm

被引：0
|
作者：
Wang, Fangxiao [1 ]
Zhu, Zexuan [1 ]
机构：
[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen City Key Lab Embedded Syst Design, Shenzhen 518060, Peoples R China
来源：
INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2013 | 2013年 / 8206卷
关键词：
Multi-objective optimization; Global path planning; Memetic algorithm;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes a multi-objective memetic algorithm (MOMA) for global path planning of wheeled robots. Particularly, MOMA is designed to simultaneously optimize the path length and smoothness. MOMA is featured with novel path encoding scheme, path rectification, and specific evolutionary operators. The experimental results on simulated maps show that MOMA is efficient in planning a set of valid trade-off paths in complex environments.
引用
收藏
页码：437 / 444
页数：8
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