Machine vision system for robotic navigation in a residential environment

Abstract

This article delves into the integration between CNN-based artificial vision and robotic navigation algorithms with the aim of efficient autonomous driving of a tracked mobile robot in residential environments. The development is based on a machine vision system, through a camera mounted on the robot, capturing scenes from different environments within a residential home to identify its current location.

PROBLEM:

Robotic navigation’s kinematics are usually implemented in spatial coordinates of an unknown environment, thus limiting the human-robot interaction to a naive completion of commands by ignoring the potential behind the environmental context in which the robot behaves. The integration of artificial vision into robotic navigation is expected to enhance a robot’s performance in supporting domestic environment tasks.

METHODOLOGY:

To achieve the identification of the robot’s location and its direction of movement, a convolutional neural network is employed, which has two branches that identify different aspects of the environment from the robot’s perspective. Once a destination is set within the environment, a branched exploration algorithm is implemented, allowing the robot to navigate while knowing its location.

RESULTS:

Mobile robotic algorithms for path planning and obstacle avoidance were implemented along with a 98.33% accuracy CNN measured on its capacity to identify residential rooms from the robot’s first-person perspective. These algorithms’ incorporation resulted in the successful guidance of a tracked differential mobile robot through the rooms of a virtual residential environment, avoiding obstacles in the process and identifying locations through which the robot crosses.