Cascade Neuro-Fuzzy Architecture Based Mobile- Robot Navigation and Obstacle Avoidance in Static and Dynamic Environments

Abstract

Real-time navigation in the partially unknown environment is an interesting task for mobile robotics. This article presents the cascade neuro-fuzzy (CN-Fuzzy) architecture for intelligent mobile robot navigation and obstacle avoidance in static and dynamic environments. The array of ultrasonic range finder sensors and sharp infrared range sensors are used to read the front, left and right obstacle distances. The cascade neural network is used to train the robot to reach the goal. Its inputs are the different obstacle distance received from the sensors. The output of the neural network is a turning angle between the robot and goal. The fuzzy architecture is integrated with the cascade neural network to control the velocities of the robot. Successful simulation and experimental results verify the effectiveness of the proposed architecture in both static and dynamic environments. Moreover, the proposed CN-Fuzzy architecture gives better results (in terms of path length) as compared to previously developed techniques, which verifies the effectiveness of the proposed architecture. which can solve the real system problems using empirical data set (experimental or predicted). The neural network with fuzzy logic [1] improves the decision speed of the mobile robot for target seeking and obstacle avoidance. Target seeking and obstacle avoidance are the two important tasks for any mobile robot in the environment. Godjevac and Steele [2] have integrated the Takagi-Sugeno type fuzzy controller and Radial Basis Function Neural Network (RBFNN) to solve the mobile robot path planning. Where the fuzzy logic is used to handle the uncertainty of the environment, and the neural network is used to tune the parameters of membership functions. Rai and Rai, [3] have designed the Arduino UNO microcontroller-based DC motor speed control system using multilayer neural network and Proportional Integral Derivative (PID) controller. Yang and Meng [4] have applied the biologically inspired neural network to generate a collision-free path in a nonstationary environment. In [5], the authors have designed the Reinforcement Ant Optimized Fuzzy Controller (RAOFC) and applied it for wheeled mobile robot wall-following control under reinforcement learning environments. The inputs of the proposed controller are range-finding sonar sensors, and the output is a robot steering angle. Algabri, et al. [6] have combined the fuzzy logic with other soft computing techniques such as Genetic Algorithm (GA), Neural Networks (NN), and Particle Swarm Optimization (PSO) to optimize the membership function parameters of the fuzzy controller for improving the navigation performance of the mobile robot. Fuzzy reinforcement learning sensor-based mobile robot navigation has been presented by Beom and Cho [7] for complex environments. In [8], the authors have constructed behaviour-based neuro-fuzzy control architecture for mobile robot navigation in an unstructured environment. Rossomando and Soria, [9] have designed an adaptive neural network PID controller to solve the trajectory tracking control problem of a mobile robot. In [10], the authors have developed a genetic algorithm to choose the best membership parameters from the fuzzy inference system and implemented it to control the steering angle of a mobile robot in the partially unknown environment. In [11], the authors have presented the navigation method of the