Solving the Path Planning Problem in Mobile Robotics with the Multi-Objective Evolutionary Algorithm

Path planning problems involve finding a feasible path from the starting point to the target point. In mobile robotics, path planning (PP) is one of the most researched subjects at present. Since the path planning problem is an NP-hard problem, it can be solved by multi-objective evolutionary algori...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied sciences Jg. 8; H. 9; S. 1425
Hauptverfasser: Xue, Yang, Sun, Jian-Qiao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.09.2018
Schlagworte:
Elitism
Genetic algorithms
Geometry
Heuristic
Methods
multi-objective evolutionary algorithm
multi-objective optimization
Neural networks
Objectives
Optimization algorithms
path planning
Product design
refiner operators
robotics
Robots
United States > US
ISSN:2076-3417, 2076-3417
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Path planning problems involve finding a feasible path from the starting point to the target point. In mobile robotics, path planning (PP) is one of the most researched subjects at present. Since the path planning problem is an NP-hard problem, it can be solved by multi-objective evolutionary algorithms (MOEAs). In this article, we propose a multi-objective method for solving the path planning problem. It is a population evolutionary algorithm and solves three different objectives (path length, safety, and smoothness) to acquire precise and effective solutions. In addition, five scenarios and another existing method are used to test the proposed algorithm. The results show the advantages of the algorithm. In particular, different quality metrics are used to assess the obtained results. In the end, the research indicates that the proposed multi-objective evolutionary algorithm is a good choice for solving the path planning problem.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app8091425