COLLISION-FREE AUTONOMOUS ROBOT NAVIGATION IN UNKNOWN ENVIRONMENTS UTILIZING PSO FOR PATH PLANNING

被引:49
作者
Krell, Evan [1 ]
Sheta, Alaa [1 ]
Balasubramanian, Arun Prassanth Ramaswamy [1 ]
King, Scott A. [1 ]
机构
[1] Texas A&M Univ, Dept Comp Sci, Corpus Christi, TX 78412 USA
关键词
mobile robot; particle swarm optimization; path planning; OPTIMIZATION;
D O I
10.2478/jaiscr-2019-0008
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.
引用
收藏
页码:267 / 282
页数:16
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