Advance Particle Swarm Optimization-Based Navigational Controller For Mobile Robot

被引：0
作者：
B. B. V. L. Deepak
Dayal R. Parhi
B. M. V. A. Raju
机构：
[1] National Institute of Technology,Robotics Lab., Department of Mechanical Engineering
[2] National Institute of Technology,Department of Industrial Design
来源：
Arabian Journal for Science and Engineering | 2014年 / 39卷
关键词：
Mobile robot navigation; Local path planning; Obstacle avoidance; Particle swarm optimization; Swarm intelligence;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
While the robot is in motion, path planning should follow the three aspects: (1) acquire the knowledge from its environmental conditions. (2) determine its position in the environment and (3) decision-making and execution to achieve its highest-order goals. The present research work aims to develop an efficient particle swarm optimization-based path planner of an autonomous mobile robot. In this approach, a fitness function has been introduced for converting the mobile robot navigation problem into multi objective optimization problem. The fitness of the swarm mainly depends on two parameters: (1) distance between each particle of the swarm and target, (2) distance between each particle of the swarm and the nearest obstacle. From the obtained fitness values of the swarm, the global best position of the particle is selected in each cycle. Thereby, the robot reaches the global best position in sequence. The effectiveness of the developed algorithm in various environments has been verified by simulation modes.
引用
收藏
页码：6477 / 6487
页数：10
相关论文
共 19 条
  • [1] Oommen B.J.(1997)Robot navigation in unknown terrains using learned visibility graphs Part I: the disjoint convex obstacle case. IEEE J. Robot. Autom. 3 672-681
  • [2] Sitharama I.(2010)Car assembly line fault diagnosis based on robust wavelet SVC and PSO Exp. Syst. Appl. 37 5423-5429
  • [3] Nageswara S.V.R.(2010)A multi-objective PSO for job-shop scheduling problems Exp. Syst. Appl. 37 1065-1070
  • [4] Kashyap R.L.(2007)An improved PSO algorithm for solving non-convex NLP/MINLP problems with equality constraints Comput. Chem. Eng. 31 153-162
  • [5] Wu Q.(2010)Multi-Objective PSO- and NPSO-based algorithms for robot path planning Adv. Electr. Comput. Eng. 10 69-76
  • [6] Sha D.Y.(2006)Evolutionary computing based mobile robot localization Eng. Appl. Artif. Intell. 19 857-868
  • [7] Lin H.H.(2010)Sensor-based intelligent mobile robot navigation in unknown environments Int. J. Electr. Comput. Eng. Syst. 1 55-62
  • [8] Yiqing L.(2012)PSO based path planner of an autonomous mobile robot Cent. Eur. J. Comput. Sci. 2 152-168
  • [9] Xigang Y.(undefined)undefined undefined undefined undefined-undefined
  • [10] Yongjian L.(undefined)undefined undefined undefined undefined-undefined
12