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 AbstractBoosting Electrical Response toward Trace Volatile Organic Compounds Molecules via Pulsed Temperature Modulation of Pt Anchored WO(3) Chemiresistor    Next AbstractEffect of 6-Methoxybenzoxazolinone on the Cecal Microbiota of Adult Male Brandt's Vole »

Front Neurorobot


Title:Mobile Robot Path Planning Based on Ant Colony Algorithm With A(*) Heuristic Method
Author(s):Dai X; Long S; Zhang Z; Gong D;
Address:"School of Mechatronics Engineering, University of Electronic Science and Technology of China, Chengdu, China. Center of Robot, University of Electronic Science and Technology of China, Chengdu, China"
Journal Title:Front Neurorobot
Year:2019
Volume:20190416
Issue:
Page Number:15 -
DOI: 10.3389/fnbot.2019.00015
ISSN/ISBN:1662-5218 (Print) 1662-5218 (Electronic) 1662-5218 (Linking)
Abstract:"This paper proposes an improved ant colony algorithm to achieve efficient searching capabilities of path planning in complicated maps for mobile robot. The improved ant colony algorithm uses the characteristics of A(*) algorithm and MAX-MIN Ant system. Firstly, the grid environment model is constructed. The evaluation function of A(*) algorithm and the bending suppression operator are introduced to improve the heuristic information of the Ant colony algorithm, which can accelerate the convergence speed and increase the smoothness of the global path. Secondly, the retraction mechanism is introduced to solve the deadlock problem. Then the MAX-MIN ant system is transformed into local diffusion pheromone and only the best solution from iteration trials can be added to pheromone update. And, strengths of the pheromone trails are effectively limited for avoiding premature convergence of search. This gives an effective improvement and high performance to ACO in complex tunnel, trough and baffle maps and gives a better result as compare to traditional versions of ACO. The simulation results show that the improved ant colony algorithm is more effective and faster"
Keywords:A* algorithm ant colony algorithm bending suppression path planning retraction mechanism;
Notes:"PubMed-not-MEDLINEDai, Xiaolin Long, Shuai Zhang, Zhiwen Gong, Dawei eng Switzerland 2019/05/07 Front Neurorobot. 2019 Apr 16; 13:15. doi: 10.3389/fnbot.2019.00015. eCollection 2019"

 
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 27-12-2024