Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm

• Published in: Cluster computing Vol. 22; no. Suppl 2; pp. 4745 - 4766
• Main Authors: Tharwat, Alaa, Elhoseny, Mohamed, Hassanien, Aboul Ella, Gabel, Thomas, Kumar, Arun
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2019; Springer Nature B.V
• Subjects: Algorithms; Collision avoidance; Computer Communication Networks; Computer Science; Curves; Genetic algorithms; Operating Systems; Optimization algorithms; Particle swarm optimization; Processor Architectures; Robots; Shortest-path problems; Smoothness; Unmanned aerial vehicles; Chaos; Path planning; Bézier curve; Particle Swarm Optimization (PSO)
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-018-2360-3

Path planning algorithms have been used in different applications with the aim of finding a suitable collision-free path which satisfies some certain criteria such as the shortest path length and smoothness; thus, defining a suitable curve to describe path is essential. The main goal of these algorithms is to find the shortest and smooth path between the starting and target points. This paper makes use of a Bézier curve-based model for path planning. The control points of the Bézier curve significantly influence the length and smoothness of the path. In this paper, a novel Chaotic Particle Swarm Optimization (CPSO) algorithm has been proposed to optimize the control points of Bézier curve, and the proposed algorithm comes in two variants: CPSO-I and CPSO-II. Using the chosen control points, the optimum smooth path that minimizes the total distance between the starting and ending points is selected. To evaluate the CPSO algorithm, the results of the CPSO-I and CPSO-II algorithms are compared with the standard PSO algorithm. The experimental results proved that the proposed algorithm is capable of finding the optimal path. Moreover, the CPSO algorithm was tested against different numbers of control points and obstacles, and the CPSO algorithm achieved competitive results.