Implementation of an autonomous intelligent mobile robot for climate purposes. (30th June 2019)

• Record Type: Journal Article
• Authors: Gharajeh, Mohammad Samadi
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 31:Number 3(2019)
• Page Start: 200
• Page End: 218
• Publication Date: 2019-06-30
• Subjects: autonomous intelligent robot -- weather condition -- utility function -- supervised machine learning -- fuzzy decision system -- sensor -- obstacle detection -- ATmega32 microcontroller -- LM35DZ sensor -- MQ-2 sensor -- photocell -- infrared (IR) sensor -- LCD -- liquid crystal display -- LED -- light-emitting diode -- DC motor; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

This paper proposes an autonomous intelligent mobile robot for climate purposes, called ClimateRobo, to notify the weather condition based on environmental data. An ATmega32 microcontroller is used to measure temperature, gas, light intensity, and distance to obstacles using the LM35DZ, MQ-2, photocell, and infrared (IR) sensors. A utility function is proposed to calculate the weather condition according to the temperature and gas data. Afterwards, the weather condition will be monitored on a liquid crystal display (LCD), an appropriate light-emitting diode (LED) will be illuminated, and an audio alarm would be enabled when weather condition is emergency as well as ambient brightness is high. The ambient brightness is calculated by a proposed supervised machine learning using sensed data of the photocell sensor. A fuzzy decision system is proposed to adjust the speed of DC motors based on weather condition and light intensity. The robot can detect and pass stationary obstacles with the six reflective sensors installed in the left, front, and right sides under six detection scenarios. Simulation results show performance of the proposed supervised machine learning, fuzzy decision system, and obstacle detection mechanism under various simulation parameters. The robot, initially, is simulated in the Proteus simulator and, then, is implemented by electronic circuits and mechanical devices.