An efficient optimization algorithm for quadratic programming problem and its applications to mobile robot path planning

The quadratic programming problem has broad applications in mobile robot path planning. This article presents an efficient optimization algorithm for globally solving the quadratic programming problem. By utilizing the convexity of univariate quadratic functions, we construct the linear relaxation p...

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Bibliographic Details
Published in:International journal of advanced robotic systems Vol. 15; no. 1
Main Authors: Cai, Lei, Yang, Juanjuan, Zhao, Li, Wu, Lan
Format: Journal Article
Language:English
Published: London, England SAGE Publications 01.01.2018
Sage Publications Ltd
SAGE Publishing
Subjects:
Algorithms
Computing time
Convexity
Optimization
Optimization algorithms
Path planning
Pruning
Quadratic equations
Quadratic programming
Robots
linear relaxation programming
path planning
Quadratic programming problem
global optimization
pruning technique
branch-and-bound optimization algorithm
ISSN:1729-8806, 1729-8814
Online Access:Get full text
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Summary:The quadratic programming problem has broad applications in mobile robot path planning. This article presents an efficient optimization algorithm for globally solving the quadratic programming problem. By utilizing the convexity of univariate quadratic functions, we construct the linear relaxation programming problem of the quadratic programming problem, which can be embedded within a branch-and-bound structure without introducing new variables and constraints. In addition, a new pruning technique is inserted into the branch-and-bound framework for improving the speed of the algorithm. The global convergence of the proposed algorithm is proved. Compared with some known algorithms, numerical experiment not only demonstrates the higher computational efficiency of the proposed algorithm but also proves that the proposed algorithm is an efficient approach to solve the problems of path planning for the mobile robot.
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ISSN:1729-8806
1729-8814
DOI:10.1177/1729881417748445