MPPTM: A Bio-Inspired Approach for Online Path Planning and High-Accuracy Tracking of UAVs

被引：7

作者：

Yi, Xin ^{[1
]}

Zhu, Anmin ^{[1
]}

Yang, S. X. ^{[2
]}

机构：

[1] Shenzhen Univ, Res Inst Intelligence Technol & Syst Integrat, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China

[2] Univ Guelph, Sch Engn, Adv Robot & Intelligent Syst ARIS Lab, Guelph, ON, Canada

来源：

FRONTIERS IN NEUROROBOTICS | 2022年 / 15卷

基金：

中国国家自然科学基金;

关键词：

multi-robot system; path planning; neural dynamics; path tracking; neural network; TARGET TRACKING; MULTIROBOT;

D O I：

10.3389/fnbot.2021.798428

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The path planning and tracking problem of the multi-robot system (MRS) has always been a research hotspot and applied in various fields. In this article, a novel multi-robot path planning and tracking model (MPPTM) is proposed, which can carry out online path planning and tracking problem for multiple mobile robots. It considers many issues during this process, such as collision avoidance, and robot failure. The proposed approach consists of three parts: a neural dynamic path planner, a hyperbolic tangent path optimizer, and an error-driven path tracker. Assisted by Ultra-wideband positioning system, the proposed MPPTM is a low-cost solution for online path planning and high-accurate tracking of MRS in practical environments. In the proposed MPPTM, the proposed path planner has good time performance, and the proposed path optimizer improves tracking accuracy. The effectiveness, feasibility, and better performance of the proposed model are demonstrated by real experiments and comparative simulations.

引用

页数：13

共 31 条

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