Autonomous bot with ML-based reactive navigation for indoor environment

TL;DR

This paper presents an affordable indoor navigation system for robots using machine learning to predict obstacle avoidance maneuvers based on ultrasonic sensor data, balancing cost and accuracy effectively.

Contribution

It introduces a ML-based reactive navigation method utilizing ultrasonic sensors and a dual-processor setup for efficient obstacle avoidance in indoor environments.

Findings

System successfully navigates cluttered indoor spaces

Machine learning model improves obstacle avoidance accuracy

Cost-effective hardware setup demonstrated

Abstract

Local or reactive navigation is essential for autonomous mobile robots which operate in an indoor environment. Techniques such as SLAM, computer vision require significant computational power which increases cost. Similarly, using rudimentary methods makes the robot susceptible to inconsistent behavior. This paper aims to develop a robot that balances cost and accuracy by using machine learning to predict the best obstacle avoidance move based on distance inputs from four ultrasonic sensors that are strategically mounted on the front, front-left, front-right, and back of the robot. The underlying hardware consists of an Arduino Uno and a Raspberry Pi 3B. The machine learning model is first trained on the data collected by the robot. Then the Arduino continuously polls the sensors and calculates the distance values, and in case of critical need for avoidance, a suitable maneuver is made by the Arduino. In other scenarios, sensor data is sent to the Raspberry Pi using a USB connection and the machine learning model generates the best move for navigation, which is sent to the Arduino for driving motors accordingly. The system is mounted on a 2-WD robot chassis and tested in a cluttered indoor setting with most impressive results.