Low-Cost Obstacle Detection and Avoidance Robots with Swarm Intelligence

Abstract

In robotics, it is very important to adapt to the real-time variations taking place in the surrounding for executing a task. It is not only required for the robots designed to be robust but also they must be easy to implement, power-efficient and affordable for wider adaptability and application. Such robots can be used with ease by anybody and anywhere for detecting obstacles where it is difficult for a person to reach. Swarm robots can have a wide variety of uses in agriculture, surveillance, mining, etc. due to their cheap manufacturing. Swarm intelligence can be made use of in the case of multi-robotic practices where coordination among a number of mobile robots is required which perform tasks autonomously without human intervention. In recent years, it has been observed that multi-robotic systems are substituting single robots because of the factors that multi-robotic systems are very time-efficient and have become much more affordable than before. This paper delivers a framework of swarm intelligence implemented into robot clusters for detecting obstacles and avoiding them in a master–slave configuration. Swarm robots are based on a progression strategy that relates to social insects. Swarm intelligence-based robots are a rapidly emerging field. This work highlights the approach of making swarm robotic systems easy to implement, economical and power-efficient, making them a possible objective for further advancement.