Obstacle recognition for path planning in autonomous mobile robots

Computer vision is an important task in robotics applications. This work proposes an approach for autonomous mobile robot navigation using the integration of the template-matching filters for obstacle detection and the evolutionary artificial potential field method for path planning. The recognition system employs a digital camera to sense the environment of a mobile robot. The captured scene is processed by a bank of space variant filters in order to find the obstacles and a feasible area for the robot navigation. The path planning employs evolutionary artificial potential fields to derive optimal potential field functions using evolutionary computation. Simulation results to validate the analysis and implementation are provided; they were specifically made to show the effectiveness and the efficiency of the proposal.

[1]  Bahram Javidi,et al.  Design of filters to detect a noisy target in nonoverlapping background noise , 1994 .

[2]  Vitaly Kober,et al.  Real-time tracking of multiple objects using adaptive correlation filters with complex constraints , 2013 .

[3]  Vitaly Kober,et al.  Accurate three-dimensional pose recognition from monocular images using template matched filtering , 2016 .

[4]  S. N. Sivanandam,et al.  Introduction to genetic algorithms , 2007 .

[5]  Oscar Montiel,et al.  Optimal Path Planning Generation for Mobile Robots using Parallel Evolutionary Artificial Potential Field , 2015, J. Intell. Robotic Syst..

[6]  Roberto Sepúlveda,et al.  Pseudo-Bacterial Potential Field Based Path Planner for Autonomous Mobile Robot Navigation , 2015 .

[7]  Ting Chen,et al.  An Obstacle Avoidance Method of Soccer Robot Based on Evolutionary Artificial Potential Field , 2012 .

[8]  B. V. K. Vijaya Kumar,et al.  Correlation filters with controlled scale response , 2006, IEEE Transactions on Image Processing.

[9]  Vitaly Kober,et al.  Accuracy of location measurement of a noisy target in a nonoverlapping background , 1996 .

[10]  Kay Chen Tan,et al.  Evolutionary artificial potential fields and their application in real time robot path planning , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[11]  Vitaly Kober,et al.  Target tracking in nonuniform illumination conditions using locally adaptive correlation filters , 2014 .

[12]  David B. Fogel,et al.  Evolutionary Computation: The Fossil Record , 1998 .

[13]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[14]  Roberto Sepúlveda,et al.  Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles , 2015, Expert Syst. Appl..

[15]  B. Kumar,et al.  Performance measures for correlation filters. , 1990, Applied optics.