Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous Abstract"Afr1p mediates activation of the Slt2p MAP kinase induced by pheromone, heat shock and hypo-osmotic shock in Saccharomyces cerevisiae"    Next AbstractFouling analysis and permeate quality evaluation of mulberry wine in microfiltration process »

Front Neurorobot


Title:Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment
Author(s):Xiong N; Zhou X; Yang X; Xiang Y; Ma J;
Address:"College of Electronics and Information Engineering, Sichuan University, Chengdu, China. The Second Research Institute of Civil Aviation Administration of China, Chengdu, China. Civil Aviation Logistics Technology Company Limited, Chengdu, China"
Journal Title:Front Neurorobot
Year:2021
Volume:20210301
Issue:
Page Number:642733 -
DOI: 10.3389/fnbot.2021.642733
ISSN/ISBN:1662-5218 (Print) 1662-5218 (Electronic) 1662-5218 (Linking)
Abstract:"This article aims to improve the problem of slow convergence speed, poor global search ability, and unknown time-varying dynamic obstacles in the path planning of ant colony optimization in dynamic environment. An improved ant colony optimization algorithm using time taboo strategy is proposed, namely, time taboo ant colony optimization (TTACO), which uses adaptive initial pheromone distribution, rollback strategy, and pheromone preferential limited update to improve the algorithm's convergence speed and global search ability. For the poor global search ability of the algorithm and the unknown time-varying problem of dynamic obstacles in a dynamic environment, a time taboo strategy is first proposed, based on which a three-step arbitration method is put forward to improve its weakness in global search. For the unknown time-varying dynamic obstacles, an occupancy grid prediction model is proposed based on the time taboo strategy to solve the problem of dynamic obstacle avoidance. In order to improve the algorithm's calculation speed when avoiding obstacles, an ant colony information inheritance mechanism is established. Finally, the algorithm is used to conduct dynamic simulation experiments in a simulated factory environment and is compared with other similar algorithms. The experimental results show that the TTACO can obtain a better path and accelerate the convergence speed of the algorithm in a static environment and can successfully avoid dynamic obstacles in a dynamic environment"
Keywords:ant colony algorithm dynamic environment mobile robot path planning time taboo strategy;
Notes:"PubMed-not-MEDLINEXiong, Ni Zhou, Xinzhi Yang, Xiuqing Xiang, Yong Ma, Junyong eng Switzerland 2021/03/19 Front Neurorobot. 2021 Mar 1; 15:642733. doi: 10.3389/fnbot.2021.642733. eCollection 2021"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 22-11-2024