PSO obstacle avoidance algorithm for robot in unknown environment

2013 ◽  
Author(s):  
Nivedita Supakar ◽  
A. Senthil
Keyword(s):  
Obstacle Avoidance ◽  
Unknown Environment

Obstacle Avoidance Optimization of Joint Robot Based on Partial PSO Algorithm

2011 ◽  
Vol 267 ◽  
pp. 574-577
Author(s):  
Jun Wei Zhao ◽  
Yan Qin Li ◽  
Guo Qiang Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Obstacle Avoidance ◽  
Inverse Kinematics ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Unknown Environment ◽  
Cartesian Space ◽  
Robot Path

Aiming at the joint robot path plan in unknown environment, the paper adopts the method of obstacle avoidance in X-Y plane. The obstacles exist in the Cartesian space are transformed into the joint blind regions in the Joint (C) space through geometry principle and inverse kinematics. The simulation using the partial particle swarm optimization (PSO) algorithm is utilized in seeking the angles that can avoid obstacles. Finally the path in the Cartesian space is obtained through transforming angles. The method is verified to be simple and effective.

scholarly journals Obstacle recognition and avoidance during robot navigation in unknown environment

2018 ◽  
Vol 7 (3) ◽  
pp. 1400 ◽  
Author(s):  
Neerendra Kumar ◽  
Zoltán Vámossy
Keyword(s):  
Obstacle Avoidance ◽  
Experimental Work ◽  
Robot Navigation ◽  
Unknown Environment ◽  
Simulink Model ◽  
Navigation Path ◽  
Advanced Algorithm ◽  
Standard Deviations

In this paper, firstly, a model for robot navigation in unknown environment is presented as a Simulink model. This model is applicable for obstacles avoidance during the robot navigation. However, the first model is unable to recognize the re-occurrences of the obstacles during the navigation. Secondly, an advanced algorithm, based on the standard deviations of laser scan range vectors, is proposed and implemented for robot navigation. The standard deviations of the lasers scans, robot positions and the time of scans with similar standard deviations are used to obtain the obstacle recognition feature. In addition to the obstacle avoidance, the second algorithm recognizes the re-appearances of the obstacles in the navigation path. Further, the obstacle recognition feature is used to break the repetitive path loop in the robot navigation. The experimental work is carried out on the simulated Turtlebot robot model using the Gazebo simulator.

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