Volume 14 Issue 2 November - January
2019
Research Paper
Vikas Kumar*, Arun Kumar Sah**,
Prases Kumar Mohanty***
*-** B.Tech Graduate, Department of
Mechanical Engineering, National Institute of Technology, Arunachal Pradesh,
India.
***Assistant Professor, Department of
Mechanical Engineering, National Institute of Technology, Arunachal Pradesh,
India.
Kumar, V., Sah, A. K., Mohanty, P. K.
(2019). Design and Implementation of an Autonomous Surveillance Mobile Robot
with Obstacle Avoidance Capabilities using Raspberry PI. i-manager’s
Journal on Future Engineering and Technology , 14 (2), 42-54.https://doi.org/10.26634/jfet.14.2.14856
Abstract
This paper focuses on
exploring and analyzing the process of robot design and hardware implementation
of the studies made on the autonomous mobile robot navigation reported in the
paper, “Application of Deep Q-Learning for Wheel Mobile Robot Navigation” (Mohanty,
Sah, Kumar, & Kundu, 2017). Incorporating autonomous robots into daily life
for serving humanity has been a long-term goal for the robotics plethora. An
autonomous mobile robot has tremendous application in various environments
since they work without human intervention. The robot is defined as a device
that is composed of the electronic, electrical, and mechanical systems with a
brain imported from computer science. In this paper, a mobile robot is
introduced which was fabricated using Raspberry Pi 3 B as a processing chip,
range sensors, and camera, which are used for extracting raw sensory data from
the environment and feeding it to the robot. The composed mobile robot can be
remotely accessed from anywhere around the globe without being in the vicinity
of the robot and can be controlled by the means of any gadget, regardless of
whether a portable workstation, a versatile, or a tablet, which makes it
perfectly suitable for surveillance, exploration, and military applications.
For training the robot in the virtual environment, a simulation model was
developed in python from scratch. The pre-trained model from the simulation was
deployed for further training of the robot in the actual environment.
Algorithms like obstacle detection and image recognition were merged together
to equip the mobile robot with necessary controls. In the end, the progress of
the robot was analyzed in different real environments and the performance
accuracy of the obstacle avoidance ability of the mobile robot was calculated based
on hit-rate matrices and tabulated.
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