Revista Politécnica (Sep 2024)
A Review on Wheeled mobile robot using different Navigation Techniques
Abstract
Mobile robots are autonomous agents capable of intelligent navigation anywhere Using sensor actuator control technology. Autonomous application Mobile robots that are active in many fields such as industry, space, defense, transportation, etc., and other social sectors are growing day by day. Mobile robots do many things rescue operations, patrols, disaster relief, and planetary exploration, That’s why we need intelligent mobile robots. It can move autonomously in various static and dynamic environments. Several techniques have been applied to mobile robots by various researchers. Navigation and obstacle avoidance. In this article, Intelligent navigation technology can navigate mobile robots Autonomously in static and dynamic environments. Navigating robots in obstacle-filled environments remains a challenge. This work describes the navigational difficulties of WMRs (wheeled mobile robots). WMR navigation mechanisms and strategies to address sub-problems are mappings, localization, and path planning. Planning can be used in all aspects of robot navigation. We will discuss some existing approaches. Accurate robot navigation is very important in agriculture applications . You have to deal with many activities in a complex agricultural environment. Focusing on the complexity of specific agricultural environments, this study anticipates the use of answers to WMR navigation problems in agricultural engineering and demonstrates that this project aims to address the challenges of precise navigation in agricultural areas. This paper presents a rigorous survey of mobile robot navigation techniques used so far. Here, a stepwise investigation of classical and reactive approaches is undertaken to understand the development of pathway planning strategies under different environmental conditions and to identify research gaps. Classical approaches such as cell decomposition (CD), roadmap approach (RA) and artificial potential field (APF). Genetic Algorithm (GA), Fuzzy Logic (FL), Neural Network (NN), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bacteria Search Optimization (BFO), Artificial Reactive approaches such as Bee Colony (ABC), Cuckoo Search (CS), Shuffled Frog Leap Algorithm (SFLA), and other miscellaneous algorithms (OMA) are under study.
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