Intelligent Motion Planning for a Cost-effective Object Follower Mobile Robotic System with Obstacle Avoidance

TL;DR

This paper presents an intelligent, vision-based motion planning system for a mobile robot that follows a human holding a unique colored object, effectively avoiding obstacles and accurately predicting velocities using deep learning and neural networks.

Contribution

It introduces a novel vision-based approach combining deep learning and neural networks for accurate object detection and velocity prediction in obstacle-rich environments.

Findings

High accuracy in detecting the unique colored object under various lighting conditions

Low error rates in linear and angular velocity predictions

Outperforms existing methodologies in obstacle avoidance and following accuracy

Abstract

There are few industries which use manually controlled robots for carrying material and this cannot be used all the time in all the places. So, it is very tranquil to have robots which can follow a specific human by following the unique coloured object held by that person. So, we propose a robotic system which uses robot vision and deep learning to get the required linear and angular velocities which are {\nu} and {\omega}, respectively. Which in turn makes the robot to avoid obstacles when following the unique coloured object held by the human. The novel methodology that we are proposing is accurate in detecting the position of the unique coloured object in any kind of lighting and tells us the horizontal pixel value where the robot is present and also tells if the object is close to or far from the robot. Moreover, the artificial neural networks that we have used in this problem gave us a meagre error in linear and angular velocity prediction and the PI controller which was used to control the linear and angular velocities, which in turn controls the position of the robot gave us impressive results and this methodology outperforms all other methodologies.