A novel relative navigation control strategy based on relation space method for autonomous underground articulated vehicles
type: Item
instance of: scholarly article
title: A novel relative navigation control strategy based on relation space method for autonomous underground articulated vehicles
zbMATH Open document ID: 1398.93238
DOI: 10.1155/2016/2352805
author: Yanyan Li
published in: Journal of Control Science and Engineering
publication date: 2018-10-08T00:00:00Z
review text: Summary: This paper proposes a novel relative navigation control strategy based on the Relation Space Method (RSM) for articulated underground trackless vehicles. In the RSM, a self-organizing, competitive neural network is used to identify the space around the vehicle, and the spatial geometric relationships of the identified space are used to determine the vehicle's optimal driving direction. For driving control, the trajectories of the articulated vehicles are analyzed, and data-based steering and speed control modules are developed to reduce modeling complexity. Simulation shows that the proposed RSM can choose the correct directions for articulated vehicles in different tunnels. The effectiveness and feasibility of the resulting novel relative navigation control strategy are validated through experiments.
Mathematics Subject Classification ID: 93C85
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 93B27
zbMATH DE Number: 6948718
zbMATH Keywords: navigation control
zbMATH Keywords: relation space method
zbMATH Keywords: autonomous underground articulated vehicles
Wikidata QID: Q59125999
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/2352805
OpenAlex ID: W2530098738
cites work: Reactive navigation and opportunistic localization for autonomous underground mining vehicles
cites work: An efficient data-driven fuzzy approach to the motion planning problem of a mobile robot
description: scientific article; zbMATH DE number 6948718