.png){kind=icon}
返回
Optimal Path Planning Generation for Mobile Robots using Parallel Evolutionary Artificial Potential Field
.png){kind=icon}
PRE
AI
DOI：10.1007/s10846-014-0124-8.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we introduce the concept of Parallel Evolutionary Artificial Potential Field (PEAPF) as a new method for path planning in mobile robot navigation. The main contribution of this proposal is that it makes possible controllability in complex real-world sceneries with dynamic obstacles if a reachable configuration set exists. The PEAPF outperforms the Evolutionary Artificial Potential Field (EAPF) proposal, which can also obtain optimal solutions but its processing times might be prohibitive in complex real-world situations. Contrary to the original Artificial Potential Field (APF) method, which cannot guarantee controllability in dynamic environments, this innovative proposal integrates the original APF, evolutionary computation and parallel computation for taking advantages of novel processors architectures, to obtain a flexible path planning navigation method that takes all the advantages of using the APF and the EAPF, strongly reducing their disadvantages. We show comparative experiments of the PEAPF against the APF and the EAPF original methods. The results demonstrate that this proposal overcomes both methods of implementation; making the PEAPF suitable to be used in real-time applications.
Keyword:
Mobile robot navigation
Path planning
Artificial potential field
APF
PEAPF
Parallel evolutionary computation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
2.8
论文数:
3.9K
被引数:
6.9K
机构
暂无机构信息
引用论文
Effect of the Porosity of a Polymer of Intrinsic Microporosity (PIM) on Its Intrinsic Fluorescence
.png){kind=icon}
PREAI
分享收藏
查看更多内容