Integrated tracking and accident avoidance system for mobile robots

In the intelligent transportation field, various accident avoidance techniques have been applied. One of the most common issues with these is the collision, which remains an unsolved problem. To this end, we developed a Collision Warning and Avoidance System (CWAS), which was implemented in the wheeled mobile robot. Path planning is crucial for a mobile robot to perform a given task correctly. Here, a tracking system for mobile robots that follow an object is presented. Thus, we implemented an integrated tracking system and CWAS in a mobile robot. Both systems can be activated independently. Using the CWAS, the robot is controlled through a remotely controlled device, and collision warning and avoidance functions are performed. Using the tracking system, the robot performs tasks autonomously and maintains a constant distance from the followed object. Information on the surroundings is obtained through range sensors, and the control functions are performed through the microcontroller. The front, left, and right sensors are activated to track the object, and all the sensors are used for the CWAS. The proposed system was tested using the binary logic controller and the Fuzzy Logic Controller (FLC). The efficiency of the robot was improved by increasing the smoothness of motion via the FLC, achieving accuracy in tracking and increasing the safety of the CWAS. Finally, simulations and experimental outcomes have shown the usefulness of the system.

[1]  Seoyong Shin,et al.  Mobile robot navigation with distance control , 2012, 2012 International Conference of Robotics and Artificial Intelligence.

[2]  Gurdip Singh,et al.  A Communication Protocol for a Vehicle Collision Warning System , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[3]  Shigeru Okuma,et al.  Modeling of driver's collision avoidance maneuver based on controller switching model , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Kimon P. Valavanis,et al.  Autonomous vehicle navigation utilizing electrostatic potential fields and fuzzy logic , 2001, IEEE Trans. Robotics Autom..

[5]  Irfan Ullah,et al.  Real-time object following fuzzy controller for a mobile robot , 2011, International Conference on Computer Networks and Information Technology.

[6]  Péter Odry,et al.  Modeling and Fuzzy control of a four-wheeled mobile robot , 2012, 2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics.

[7]  Guizhen Yu,et al.  Vehicle collision warning system and collision detection algorithm based on vehicle infrastructure integration , 2011 .

[8]  Torsten Kröger,et al.  Opening the door to new sensor-based robot applications—The Reflexxes Motion Libraries , 2011, 2011 IEEE International Conference on Robotics and Automation.

[9]  M. J. Wierman,et al.  Empirical study of defuzzification , 2003, 22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003.

[10]  Seoyong Shin,et al.  Integrated collision avoidance and tracking system for mobile robot , 2012, 2012 International Conference of Robotics and Artificial Intelligence.

[11]  Seoyong Shin,et al.  Sensor-Based Autonomous Robot Navigation with Distance Control , 2012 .

[12]  Peter I. Corke,et al.  Robotics, Vision and Control - Fundamental Algorithms in MATLAB® , 2011, Springer Tracts in Advanced Robotics.

[13]  Marco Gruteser,et al.  ParkNet: drive-by sensing of road-side parking statistics , 2010, MobiSys '10.

[14]  Bernard De Baets,et al.  Only Smooth Rule Bases Can Generate Monotone Mamdani-Assilian Models Under Center-of-Gravity Defuzzification , 2009, IEEE Trans. Fuzzy Syst..

[15]  Jang-Myung Lee,et al.  Sliding mode control for trajectory tracking of mobile robot in the RFID sensor space , 2009 .

[16]  Irfan Ullah,et al.  Real-time object following fuzzy controller for a mobile robot , 2011 .

[17]  Irfan Ullah,et al.  A sensor based robotic model for vehicle collision reduction , 2011, International Conference on Computer Networks and Information Technology.

[18]  Tankut Acarman,et al.  Non-standard safety technology , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[19]  Jian Wang,et al.  A New Method for Distance and Relative Velocity Measurement in Vehicle Collision Warning System , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[20]  Vicente Milanés Montero,et al.  A fuzzy aid rear-end collision warning/avoidance system , 2012, Expert Syst. Appl..

[21]  Irfan Ullah,et al.  Sensor Based Robotic Navigation and Distance Control , 2010 .

[22]  Pongsathorn Raksincharoensak,et al.  Seat vibrotactile warning interface for forward vehicle collision avoidance , 2010, Proceedings of SICE Annual Conference 2010.

[23]  Moon G. Joo,et al.  Multi-object identification for mobile robot using ultrasonic sensors , 2012 .

[24]  A. Rakotonirainy,et al.  The Need of Intelligent Driver Training Systems for Road Safety , 2008, 2008 19th International Conference on Systems Engineering.

[25]  Seoyong Shin,et al.  Sensor-Based Robotic Model for Vehicle Accident Avoidance , 2012 .

[26]  Peter Xiaoping Liu,et al.  An embedded fuzzy controller for a behavior-based mobile robot with guaranteed performance , 2004, IEEE Transactions on Fuzzy Systems.

[27]  Robin R. Murphy,et al.  A case study of fuzzy-logic-based robot navigation , 2006, IEEE Robotics & Automation Magazine.