Multiple Mobile Robots Navigation and Obstacle Avoidance Using Minimum Rule Based ANFIS Network Controller in the Cluttered Environment

The ANFIS is the product of two methods, neural networks, and fuzzy systems. If both these intelligent methods are combined, better reasoning will be obtained in term of quality and quantity. In other words, both fuzzy reasoning and neural network calculation will be available simultaneously [7]. This ANFIS technique has been successfully applied by many researchers for sensor-based autonomous control mobile robots in the different environment. To address the best and optimal path, the robots required to move with suitable path planning algorithm, which calculates the minimum path length between any two points i.e. robot to obstacles or robot to the goal [3]. Pothal and Parhi [8] have proposed the sensor based Adaptive Neuro Fuzzy Inference System (ANFIS) controller for the navigation of single and multiple mobile robots in the highly cluttered environment. The authors have tried to design a control system architecture, which avoids the obstacle autonomously and reaches the target efficiently in all types of environments.