Autonomous Mobile Robot Navigation by Combining Local and Global Techniques

Abstract

The present article is devoted to develop an algorithm for obstacle avoidance of an autonomous mobile robot based on fuzzy logic/ The method of navigation proposed provides a way of blending the intelligence and optimality of global methods with the reactive dynamic behavior of local ones. This is achieved by using hybrid navigation system composed of two modules, one of which uses the apriori information and determines roughly the optimal route towards the goal, whereas the other carries out effective navigation decisions using the potential function based local approach. The fuzzy rules are constructed from intuitive and subjective human ways of collision avoidance. The results of the present study are compares favorably with those of well-established algorithms.

Wall-Following Behavior-Based Mobile Robot Using Particle Swarm Fuzzy Controller

“…Usually, the mobile robot should face unpredictable environment, perceive inaccurate sensor and act with unsatisfactory actuator in high speed response [1,2]. Behavior-based control architecture is an alternative method suitable to address these problems [3][4][5][6][7]. The architecture is able to act with fast real-time response, provides for higher level deliberation and has confirmed its reliable results in standard robotic activities.…”

Autonomous Navigation and Obstacle Avoidance for a Wheeled Mobile Robots: A Hybrid Approach

“…These algorithms aim to combine the advantages from both the local and global methods, and to also eliminate some of their weaknesses. In [19] a hybrid method blends the reactivity of local methods with the intelligence and optimality of global ones. The idea is to use global planning to determine only certain points (temporary goals) where the robot need to pass to reach the goal without getting stuck, and then do the actual navigation by a local algorithm.…”