Comparison of Three Different Curves Used in Path Planning Problems Based on Particle Swarm Optimizer

Abstract

In path planning problems, the most important task is to find a suitable collision-free path which satisfies some certain criteria (the shortest path length, security, feasibility, smoothness, and so on), so defining a suitable curve to describe path is essential. Three different commonly used curves are compared and discussed based on their performance on solving a set of path planning problems. Dynamic multiswarm particle swarm optimizer is employed to optimize the necessary parameters for these curves. The results show that Bezier curve is the most suitable curve for producing path for the certain path planning problems discussed in this paper. Safety criterion is considered as a constrained condition. A new constraint handling method is proposed and compared with other two constraint handling methods. The results show that the new method has a better characteristic to improve the performance of algorithm.

Pattern recognition and correction method for skeleton lines at small patch boundaries

“…A Bezier curve is a parametric polynomial curve and is often employed in the field of computer graphics (Bezier, 1974; Elhoseny, Tharwat, & Hassanien, 2018; Liang, Song, Qu, & Lu, 2014; Tharwat, Elhoseny, Hassanien, Gabel, & Kumar, 2019). The cubic Bezier curve has the following advantages for skeleton line correction at small patch boundaries: (1) the curve runs through the first and last fitting points, and it is tangent to the patch boundary at the location of the first and last fitting points, thus preserving the natural extension directions of the boundaries; (2) the curve is smooth, and the overall shape of the curve is controlled by plane discrete points; and (3) the clear math rules and the running stable computer program ensure the reliability of the curve.…”

A self-organizing multimodal multi-objective pigeon-inspired optimization algorithm

“…Since the concept of bio-inspired computing was derived from the simulation of complex ecosystems in the natural world, the related bio-inspired algorithms are provided as a sound scientific basis for solving practical problems. Swarm intelligence algorithms, such as PSO [21][22][23] and ant colony algorithm (ACO) [24], have been widely adopted for many real-world problems. A novel intelligent computation method based on bionics, known as pigeon-inspired optimization (PIO), was proposed by Duan et al [25] in 2014.…”

PSO‐Based Robot Path Planning for Multisurvivor Rescue in Limited Survival Time

“…PSO was originally proposed by Kennedy and Eberhard [12]. Since it has a simple structure, fast convergence, and few parameters to be set, it has been widely applied in the field of robot path planning [13][14][15]. However, there have been no studies on robot rescue path planning with time-constraint.…”