A Chaotic Adaptive Particle Swarm Optimization for Robot Path Planning

Lian, Jianfang; Yu, Wentao; Liu, Weirong

doi:10.23919/chicc.2019.8865591

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…e main classical algorithms include cell decomposition, artificial potential field, and sampling-based methods [4]. However, classic methods are very time consuming and require ample storage memory [5]. us, heuristic optimization algorithms [6,7] are used frequently to optimize the path planning problem, such as differential evolutionary (DE) algorithm [8], genetic algorithm (GA) [9], A * algorithm [10], artificial bee colony (ABC) algorithm [11], annealing (SA) [12], particle swarm optimization (PSO) [13], and ant colony optimization (ACO) [14].…”

Section: Introductionmentioning

confidence: 99%

Cubic Spline Interpolation-Based Robot Path Planning Using a Chaotic Adaptive Particle Swarm Optimization Algorithm

Lian

Xiao

et al. 2020

Mathematical Problems in Engineering

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This paper proposed a cubic spline interpolation-based path planning method to maintain the smoothness of moving the robot’s path. Several path nodes were selected as control points for cubic spline interpolation. A full path was formed by interpolating on the path of the starting point, control points, and target point. In this paper, a novel chaotic adaptive particle swarm optimization (CAPSO) algorithm has been proposed to optimize the control points in cubic spline interpolation. In order to improve the global search ability of the algorithm, the position updating equation of the particle swarm optimization (PSO) is modified by the beetle foraging strategy. Then, the trigonometric function is adopted for the adaptive adjustment of the control parameters for CAPSO to weigh global and local search capabilities. At the beginning of the algorithm, particles can explore better regions in the global scope with a larger speed step to improve the searchability of the algorithm. At the later stage of the search, particles do fine search around the extremum points to accelerate the convergence speed of the algorithm. The chaotic map is also used to replace the random parameter of the PSO to improve the diversity of particle swarm and maintain the original random characteristics. Since all chaotic maps are different, the performance of six benchmark functions was tested to choose the most suitable one. The CAPSO algorithm was tested for different number of control points and various obstacles. The simulation results verified the effectiveness of the proposed algorithm compared with other algorithms. And experiments proved the feasibility of the proposed model in different dynamic environments.

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