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.

Dynamic Motion Planning for Autonomous Wheeled Robot using Minimum Fuzzy Rule based Controller with Avoidance of Moving Obstacles

“…Therefore, an automated wheeled robot is required, which can do these types of tasks autonomously. Many artificial intelligence and nature-inspired methods such as Hybrid Fuzzy Controller [1], Neural network Technique [2], Neuro-fuzzy Controller [3], Adaptive Neuro-fuzzy (ANFIS) [4], Simulated annealing algorithm (SA) [5], Particle swarm optimization (PSO) algorithm [6], Genetic algorithm (GA) [7], and Ant colony optimization algorithm (ACO) [8] have been designed and implemented by researchers for wheeled robot motion planning and static/dynamic collision avoidance. Most of the researchers [2,4,[6][7][8] have focused on static or non-moving obstacle avoidance based motion planning.…”

“…Most of the researchers [2,4,[6][7][8] have focused on static or non-moving obstacle avoidance based motion planning. However, few of them [1,3,5] have considered dynamic or moving obstacles for navigation and obstacle avoidance. In the article [3], the authors have developed the adaptive neuro-fuzzy sensor-based navigation and control system for swarm wheeled robots in unknown static environments.…”

“…However, few of them [1,3,5] have considered dynamic or moving obstacles for navigation and obstacle avoidance. In the article [3], the authors have developed the adaptive neuro-fuzzy sensor-based navigation and control system for swarm wheeled robots in unknown static environments. In their work, they have used 48-Fuzzy rules with two behaviours, namely goal searching and collision avoidance.…”