Navigation of Multiple Mobile Robots using Rule-based-Neuro-Fuzzy Technique
This paper deals with motion planning of multiple
mobile robots. Mobile robots working together to achieve several
objectives have many advantages over single robot system. However,
the planning and coordination between the mobile robots is
extremely difficult. In the present investigation rule-based and rulebased-
neuro-fuzzy techniques are analyzed for multiple mobile
robots navigation in an unknown or partially known environment.
The final aims of the robots are to reach some pre-defined goals.
Based upon a reference motion, direction; distances between the
robots and obstacles; and distances between the robots and targets;
different types of rules are taken heuristically and refined later to find
the steering angle. The control system combines a repelling influence
related to the distance between robots and nearby obstacles and with
an attracting influence between the robots and targets. Then a hybrid
rule-based-neuro-fuzzy technique is analysed to find the steering
angle of the robots. Simulation results show that the proposed rulebased-
neuro-fuzzy technique can improve navigation performance in
complex and unknown environments compared to this simple rulebased
technique.
[1] N. T. G├╝rman, "The neural network model RuleNet and its application
to mobile robot navigation," Fuzzy Sets and Systems, Volume 85, Issue
2, 23 January 1997, pp. 287-303.
[2] W. Li, C. Ma, F. M. Wahl, "A neuro-fuzzy system architecture for
behavior-based control of a mobile robot in unknown environments, "
Fuzzy Sets and Systems, Volume 87, Issue 2, 16 April 1997, pp. 133-
140.
[3] C. W. Barfoot, M. Y. Ibrahim, "Development of an adaptive fuzzy
behavioural control system with experimental and industrial
applications," Computers & Industrial Engineering, Volume 34, Issue
4, September 1998, pp. 807-811.
[4] Jelena and Nigel, "Neuro-fuzzy control of a mobile robot,"
Neurocomputing, Volume 28, Issues 1-3, October 1999, pp. 127-143.
[5] L. Acosta, G.N. Marichal, L. Moreno, J. A. Méndez, J.J. Rodrigo,
"Obstacle Avoidance Using the Human Operator Experience for a
Mobile Robot," Journal of Intelligent and Robotic Systems, April 2000,
Volume 27, No. 4, pp. 305-319.
[6] G.N. Marichal, L. Acosta, L. Moreno, J.A. Mendez, J.J. Rodrigo, M.
Sigut, "Obstacle avoidance for a mobile robot: A neuro-fuzzy
approach," Fuzzy Sets and Systems, 1 December 2001, Volume 124, No.
2, pp. 171-179.
[7] K. Althoefer B. Krekelberg, D. Husmeier, L. Seneviratne ,
"Reinforcement learning in a rule-based navigator for robotic
manipulators," Neurocomputing, Volume 37, Issues 1-4 , April 2001,
pp. 51-70.
[8] S. Nefti, M. Oussalah, K. Djouani, J. Pontnau, "Intelligent Adaptive
Mobile Robot Navigation," Journal of Intelligent and Robotic Systems,
April 2001, Volume 30, No. 4, pp. 311-329.
[9] E. Tunstel, A. Howard, H. Seraji, "Rule-based reasoning and neural
network perception for safe off-road robot mobility," Expert Systems,
September 2002, Volume 19, No. 4, pp. 191-200.
[10] M. A. F. de Souza, M. A. G. V. Ferreira, "Designing reusable rulebased
architectures with design patterns," Expert Systems with
Applications, Volume 23, Issue 4, November 2002, pp. 395-403.
[11] Y-K. Na and S-Y. Oh, "Hybrid Control for Autonomous Mobile Robot
Navigation Using Neural Network Based Behavior Modules and
Environment Classification," Autonomous Robots, September 2003,
Volume 15, No. 2, pp. 193-206(14).
[12] J. Dietrich, A. Kozlenkov, M. Schroeder, G. Wagner, "Rule-based
agents for the semantic web," Electronic Commerce Research and
Applications, Volume 2, Issue 4, Winter 2003, pp. 323-338.
[13] B. S. McIntosh, R. I. Muetzelfeldt, C. J. Legg, S. Mazzoleni, P.
Csontos, "Reasoning with direction and rate of change in vegetation
state transition modeling," Environmental Modelling & Software,
Volume 18, Issue 10, December 2003, pp. 915-927.
[14] Clementine Software, Version-5, Available :
http://www.spss.com/Clementine/, 2000.
[15] J.L. Peterson, Petri Net theory and the Modelling of Systems (Prentice-
Hall, Englewood Cliff.N.J., 1981).
[16] D.T. Pham, D. R. Parhi, "Navigation of multiple mobile robots using a
neural network and a Petri net model," Robotica, U.K., Volume 21,
2003, pp. 79-93
[17] S. K. Pradhan, D. R. Parhi, A. K. Panda, "Neuro-fuzzy techniques for
navigation of multiple mobile robots," Fuzzy optimization and decision
making, to be published.
[1] N. T. G├╝rman, "The neural network model RuleNet and its application
to mobile robot navigation," Fuzzy Sets and Systems, Volume 85, Issue
2, 23 January 1997, pp. 287-303.
[2] W. Li, C. Ma, F. M. Wahl, "A neuro-fuzzy system architecture for
behavior-based control of a mobile robot in unknown environments, "
Fuzzy Sets and Systems, Volume 87, Issue 2, 16 April 1997, pp. 133-
140.
[3] C. W. Barfoot, M. Y. Ibrahim, "Development of an adaptive fuzzy
behavioural control system with experimental and industrial
applications," Computers & Industrial Engineering, Volume 34, Issue
4, September 1998, pp. 807-811.
[4] Jelena and Nigel, "Neuro-fuzzy control of a mobile robot,"
Neurocomputing, Volume 28, Issues 1-3, October 1999, pp. 127-143.
[5] L. Acosta, G.N. Marichal, L. Moreno, J. A. Méndez, J.J. Rodrigo,
"Obstacle Avoidance Using the Human Operator Experience for a
Mobile Robot," Journal of Intelligent and Robotic Systems, April 2000,
Volume 27, No. 4, pp. 305-319.
[6] G.N. Marichal, L. Acosta, L. Moreno, J.A. Mendez, J.J. Rodrigo, M.
Sigut, "Obstacle avoidance for a mobile robot: A neuro-fuzzy
approach," Fuzzy Sets and Systems, 1 December 2001, Volume 124, No.
2, pp. 171-179.
[7] K. Althoefer B. Krekelberg, D. Husmeier, L. Seneviratne ,
"Reinforcement learning in a rule-based navigator for robotic
manipulators," Neurocomputing, Volume 37, Issues 1-4 , April 2001,
pp. 51-70.
[8] S. Nefti, M. Oussalah, K. Djouani, J. Pontnau, "Intelligent Adaptive
Mobile Robot Navigation," Journal of Intelligent and Robotic Systems,
April 2001, Volume 30, No. 4, pp. 311-329.
[9] E. Tunstel, A. Howard, H. Seraji, "Rule-based reasoning and neural
network perception for safe off-road robot mobility," Expert Systems,
September 2002, Volume 19, No. 4, pp. 191-200.
[10] M. A. F. de Souza, M. A. G. V. Ferreira, "Designing reusable rulebased
architectures with design patterns," Expert Systems with
Applications, Volume 23, Issue 4, November 2002, pp. 395-403.
[11] Y-K. Na and S-Y. Oh, "Hybrid Control for Autonomous Mobile Robot
Navigation Using Neural Network Based Behavior Modules and
Environment Classification," Autonomous Robots, September 2003,
Volume 15, No. 2, pp. 193-206(14).
[12] J. Dietrich, A. Kozlenkov, M. Schroeder, G. Wagner, "Rule-based
agents for the semantic web," Electronic Commerce Research and
Applications, Volume 2, Issue 4, Winter 2003, pp. 323-338.
[13] B. S. McIntosh, R. I. Muetzelfeldt, C. J. Legg, S. Mazzoleni, P.
Csontos, "Reasoning with direction and rate of change in vegetation
state transition modeling," Environmental Modelling & Software,
Volume 18, Issue 10, December 2003, pp. 915-927.
[14] Clementine Software, Version-5, Available :
http://www.spss.com/Clementine/, 2000.
[15] J.L. Peterson, Petri Net theory and the Modelling of Systems (Prentice-
Hall, Englewood Cliff.N.J., 1981).
[16] D.T. Pham, D. R. Parhi, "Navigation of multiple mobile robots using a
neural network and a Petri net model," Robotica, U.K., Volume 21,
2003, pp. 79-93
[17] S. K. Pradhan, D. R. Parhi, A. K. Panda, "Neuro-fuzzy techniques for
navigation of multiple mobile robots," Fuzzy optimization and decision
making, to be published.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:64188", author = "Saroj Kumar Pradhan and Dayal Ramakrushna Parhi and Anup Kumar Panda", title = "Navigation of Multiple Mobile Robots using Rule-based-Neuro-Fuzzy Technique", abstract = "This paper deals with motion planning of multiple
mobile robots. Mobile robots working together to achieve several
objectives have many advantages over single robot system. However,
the planning and coordination between the mobile robots is
extremely difficult. In the present investigation rule-based and rulebased-
neuro-fuzzy techniques are analyzed for multiple mobile
robots navigation in an unknown or partially known environment.
The final aims of the robots are to reach some pre-defined goals.
Based upon a reference motion, direction; distances between the
robots and obstacles; and distances between the robots and targets;
different types of rules are taken heuristically and refined later to find
the steering angle. The control system combines a repelling influence
related to the distance between robots and nearby obstacles and with
an attracting influence between the robots and targets. Then a hybrid
rule-based-neuro-fuzzy technique is analysed to find the steering
angle of the robots. Simulation results show that the proposed rulebased-
neuro-fuzzy technique can improve navigation performance in
complex and unknown environments compared to this simple rulebased
technique.", keywords = "Mobile robots, Navigation, Neuro-fuzzy, Obstacle
avoidance, Rule-based, Target seeking", volume = "1", number = "10", pages = "599-11", }
