Abstract

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.

Highlights

  • Human beings always have an urge to complete all the works and jobs automatically through automated machines, which inspires the researchers to focus on the design of mobile robots

  • The existing robot path planning methods can be classified into two categories: classical algorithms and heuristic optimization algorithm. e main classical algorithms include cell decomposition, artificial potential field, and sampling-based methods [4]

  • A full path was formed by interpolating on the path of the starting point, control points, and target point. e main contribution is to use chaotic adaptive particle swarm optimization (CAPSO) algorithm to present a novel algorithm that is used to optimize control points in cubic spline interpolation. e fitness function of CAPSO synthesizes two evaluation functions that consider path length after cubic spline interpolation and obstacle risk degree separately. e main improvement of the CAPSO algorithm is illustrated below

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Summary

Introduction

Human beings always have an urge to complete all the works and jobs automatically through automated machines, which inspires the researchers to focus on the design of mobile robots. A cubic spline interpolation-based path planning method has been proposed to maintain the smoothness of moving the robot’s path. Several path nodes were selected as control points for cubic spline interpolation. E main contribution is to use chaotic adaptive particle swarm optimization (CAPSO) algorithm to present a novel algorithm that is used to optimize control points in cubic spline interpolation. The diversity of particle group traversal is optimized, and the original random characteristics of the standard PSO algorithm are retained, which is able to effectively prevent the PSO from plunging into local optimal and make the particles proceed with searching in other regions of the solution space.

Background
Problem Formulation for Robot Path Planning
Chaotic Adaptive Particle Swarm Optimization Algorithm
Experiments
First Experiment
Second Experiment
Fourth Experiment
Fifth Experiment
Sixth Experiment
Full Text
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