An Economically Efficient Agent-based Autonomous Road Cleaner for Asian Cities

Abstract

In the last couple of decades, autonomous road cleaners have been enormously attracting the world. For this purpose, numerous researchers have contributed to designing efficient, robust, and human-friendly cleaning mechanisms. However, their proposed approaches do not provide a cost-effective solution due to the deployment of exorbitant sensors and sophisticated infrastructure deployment. Hence, we propose to develop a cost-effective self-driving road that has the capability to clean the roads of major cities of Asia. The developed road cleaner efficiently detects road lanes using advanced machine vision techniques and estimates the angle to stay in the lane. Moreover, to control the speed of the road cleaner we have used a proportional–integral–derivative (PID) controller. The smart cleaning robot detects objects in its surroundings to prevent accidents in congested roads; thus it will have a function of anticollision and automatic avoidance. It also measures turning angle by performing computations on the position of road lanes and takes turns accordingly. To validate the efficiency of the proposed solution, we have performed a series of experiments considering different test cases.