Autonomous Robotic System Based Environmental Assessment and Dengue Hot-Spot Identification

Abstract

Robotic systems have a tremendous influence on real-world applications as well as the actions and decisions of humans. It is one of the key driving forces towards advancements in the field of technology. With an everlasting desire to master this, humans have made one major compromise: unmonitored depletion of Earth's invaluable resources and uncontrolled accumulation of waste. Furthermore, this so-called progress towards a technologically advanced world has paved the way to create a complex environment that poses several risks to human health. One such risk is caused due to the stagnation of contaminated water which serves as a breeding ground for insects such as mosquitoes and houseflies and causes a plethora of diseases. As a result, mosquito-borne diseases are on the rise in countries such as India, Sri Lanka, Thailand, Cambodia, and Brazil. These diseases are detrimental to the health of the country's population. Dengue, a mosquito-borne disease, is the fastest spreading and most critical. Therefore, the challenge at hand is to develop a robotic system which is efficient in locating “dengue hot-spots” while being complementary to the environment, i.e. fewer emissions, zero greenhouse gases. Current methods of dealing with this challenge require a lot of resources and do not yield expected results. Our design methodology, referred to as EMBED-X, offers a systematic approach that is backed up by protocols which support intelligent and pro-active monitoring of the environment. The robotic system is to be semiautonomous and remotely connected to a control station for constant data analysis. The aim is to identify objects such as coconut shells, rubber tires, and plastic containers that have the potential for retaining stagnant water. To identify these objects, image analysis algorithms are applied on the images captured by the autonomous robot during the surveillance of a given area. Our image analysis algorithm has an accuracy of 66.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and is continually being improved using the EMBED-X methodology. There are limitations to this solution and methodology. However, this project would serve as a stepping stone towards better design of robotic systems applied specifically to environmental monitoring and improving assessment capabilities through advanced image processing methods.