THE CFD-BASED NEURAL DYNAMIC PATH PLANNER FOR MULTI-ROBOT SYSTEM

Xin Yi; Anmin Zhu; Chaofan LiDianqing ZhaoSimon X. Yang

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THE CFD-BASED NEURAL DYNAMIC PATH PLANNER FOR MULTI-ROBOT SYSTEM

机译：THE CFD-BASED NEURAL DYNAMIC PATH PLANNER FOR MULTI-ROBOT SYSTEM

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摘要

Real-time path planning is a crucial research issue for multi-robot systems (MRSs), particularly in a 3-D environment with obstacles. In this paper, combining with computational fluid dynamics (CFD), an improved neural dynamic path planner is proposed for MRS. It is capable of real-time planning the paths while avoiding obstacles for MRS in complex, dynamic and large-scale environments. The proposed path planner has lower computational complexity than traditional ones. The computational complexity of the proposed path planner is not sensitive to the number of individuals in MRS. The validity of the proposed approach is demonstrated by comparative simulations with different simulation scenarios.

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来源
《International Journal of Robotics & Automation》 |2022年第5期|445-452|共8页
作者
Xin Yi; Anmin Zhu; Chaofan LiDianqing ZhaoSimon X. Yang;
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作者单位

Research Institute of Intelligence Technology and System Integration, College of Computer Science and Software Engineering, Shenzhen University;

ARIS Laboratory, School of Engineering, University of Guelph;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类机器人技术;
关键词
Multi-robot; Path planning; Obstacles avoidance; Neural dynamics;

THE CFD-BASED NEURAL DYNAMIC PATH PLANNER FOR MULTI-ROBOT SYSTEM

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