The stable and precise motion control for multiple mobile robots

Authors:
Dayal Ramakrushna Parhi;Saroj Kumar Pradhan;Anup Kumar Panda;Rabindra Kumar Behera
Affiliations:
Department of Mechanical Engineering, N.I.T., Rourkela 769008, Orissa, India;Department of Mechanical Engineering, N.I.T., Hamirpur 769008, H.P., India;Department of Electrical Engineering, N.I.T., Rourkela 769008, Orissa, India;Department of Mechanical Engineering, N.I.T., Rourkela 769008, Orissa, India
Venue:
Applied Soft Computing
Year:
2009

Citing 9
Cited 1

The neural network model RuleNet and its application to mobile robot navigation

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Development of an adaptive fuzzy behavioural control system with experimental and industrial applications

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Petri Net Theory and the Modeling of Systems

Petri Net Theory and the Modeling of Systems
Obstacle Avoidance Using the Human Operator Experience for a Mobile Robot

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Intelligent Adaptive Mobile Robot Navigation

Journal of Intelligent and Robotic Systems
Hybrid Control for Autonomous Mobile Robot Navigation Using Neural Network Based Behavior Modules and Environment Classification

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Navigation of multiple mobile robots using a neural network and a Petri Net model

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Fuzzy logic techniques for navigation of several mobile robots

Applied Soft Computing

Intelligent neuro-controller for navigation of mobile robot

Proceedings of the International Conference on Advances in Computing, Communication and Control

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Abstract

Intelligent path planning of multiple mobile robots has been addressed in this paper. Cooperative behaviour can be achieved using several mobile robots, which require online inter-communication among themselves. In the present investigation rule-based and rule-based-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; 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 analyzed to find the steering angle of the robots. Results show that the proposed rule-based-neuro-fuzzy technique can improve navigation performance in complex and unknown environments compared to this simple rule-based technique.