“Techno-Social Excellence”
Marathwada Mitra Mandal’s Institute of Technology Lohgaon, Pune-47
Department of Mechatronics Engineering
B.E. Project Stage I
On
AUTONOMOUS MOBILE ROBOT
By
1. BMX29
VRUSHABH JAIN
2. BMX30
ABHISHEK GAIKWAD
3. BMX35
ANAND BARAPATRE
4. BMX07
BHAGYASHRI GADHE
Under the Guidance of
PROF. S. V .GOLANDE
MMIT Lohgaon
B.E. Project Stage I
AY 2023-24
1
CONTENTS
1. INTRODUCTION
2. LITERATURE REVIEW
3. PROBLEM DEFINITION
4. OBJECTIVES
5. METHODOLOGY
6. CAD MODEL
7. SIMULATION/ANALYSIS
8. REFERENCES
MMIT Lohgaon
B.E. Project Stage I
AY 2023-24
2
INTRODUCTION
Autonomous Mobile Robots are machine which are capable of :
• Self-contained machines.
• Equipped with sensors.
• Capable of perceiving their environment.
• Make autonomous decisions based on sensor input.
• Execute movements and actions independently.
• Operate without human intervention.
MMIT Lohgaon
B.E. Project Stage I
AY 2023-24
3
LITERATURE REVIEW
Sr.No
Title of Papers
Author
Journal
/conference name with
year of publication
1
Navigation of
Autonomous
Mobile Robots in
Unstructured
Environments
John A. Smith,
Mary L. Johnson
International Conference on
Robotics and Automation
(ICRA) 2019
2
A systematic
review on
recent advances
in autonomous
mobile robot
navigation
Chen, Y.,
Wang H., Liu, J
Robotics and ComputerIntegrated Manufacturing
(2023)
3
A novel
approach to
autonomous
mobile robot
navigation
using
reinforcement
learning
Liu, Y., Wang,
Z., & Zhang, Y.
IEEE Transaction on
Robotics (2023)
MMIT Lohgaon
B.E. Project Stage I
Description
This research paper presents a comprehensive study on
the navigation of autonomous mobile robots operating in
unstructured environments. The authors address the
significant challenges associated with enabling robots to
autonomously navigate through environments that lack
predefined paths or have dynamic obstacles.
This paper presents a systematic review on recent
advances in autonomous mobile robot navigation. The
authors focus on path planning algorithms, which are
essential for enabling robots to navigate safely and
efficiently in complex environments. They review a
variety of path planning algorithms, including traditional
algorithms such as A* and Dijkstra's algorithm.
This paper presents a novel approach to autonomous
mobile robot navigation using reinforcement learning.
They proposed approach does not require any prior
knowledge of the environment or the robot's dynamics.
Instead, the robot learns to navigate by trial and error,
using reinforcement learning to reward itself for reaching
its goal and penalize itself for colliding with obstacles.
AY 2023-24
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PROBLEM DEFINITION
Design and development of an Autonomous mobile robot for real-world applications
Challenges with autonomous mobile robots:
•Executing specific tasks autonomously, such as delivery, inspection, or manipulation
•Navigating in complex, dynamic, and unstructured environments while avoiding
obstacles.
•Efficiently planning collision-free paths, especially in real-time
•Accurately determining the robot's position within a map of its environment
(localization).
MMIT Lohgaon
B.E. Project Stage I
AY 2023-24
5
OBJECTIVES
•
Navigation: One of the primary objectives of autonomous mobile robots is to navigate their environment
safely and efficiently. This involves obstacle avoidance, path planning, and localization.
• Task Execution: Autonomous mobile robots are often designed to perform specific tasks, such as delivering
items, cleaning floors, or inspecting infrastructure.
MMIT Lohgaon
B.E. Project Stage I
AY 2023-24
6
METHODOLOGY
Creation of the Robot in CAD
Selection of Hardware Components
Creating the Robot for
Simulation (Gazebo) URDF FILE
Developing a ROS package to control
Motors
Integrating Navigation Stack
Calibration and Testing
MMIT Lohgaon
B.E. Project Stage I
Chassis
Lidar, IMU sensor
Encoders
Motors, Wheels
Motor driver
Raspberry Pi
Battery
Implement algorithms
localization, and
mapping (SLAM).
Path Planning: Creating
algorithms for route
planning and obstacle
avoidance.
AY 2023-24
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CAD Model (Rough)
MMIT Lohgaon
B.E. Project Stage I
AY 2023-24
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SIMULATION/ ANALYSIS
ROS and Gazebo Integration:
✓ Developed ROS packages for encapsulating robot functionalities.
✓ Utilized ROS nodes for sensor data processing, motion planning, and communication.
✓ Integrated Gazebo as the simulation platform for a realistic 3D environment.
✓ Used Gazebo plugins to synchronize ROS nodes with the simulated robot model
Simulation Results:
➢ Validated behavioral algorithms, including obstacle avoidance and path planning.
➢ Assessed sensor performance, especially LiDAR and various conditions.
MMIT Lohgaon
B.E. Project Stage I
AY 2023-24
9
SIMULATION
Mapping:
✓ Implemented mapping algorithms to generate
accurate representations of the environment.
✓ Utilized SLAM techniques to create maps while the
robot explored the simulated space.
Fig - Visualization of Mapping results during
simulation
MMIT Lohgaon
B.E. Project Stage I
AY 2023-24
10
Guessing Robot's
estimated pose
using AMCL
SIMULATION
Localization:
✓ Developed and tested localization algorithms to
accurately position the robot within the simulated
environment.
Fig - Visualization of localization results during
simulation
MMIT Lohgaon
B.E. Project Stage I
AY 2023-24
11
SIMULATION
Navigation:
✓ Implemented navigation algorithms to plan
and execute the robot's movements.
✓ Validated the robot's ability to follow
predefined paths and adapt to dynamic
changes in the environment.
Fig - Visualization of Navigation results during
simulation
MMIT Lohgaon
B.E. Project Stage I
AY 2023-24
12
STAGE I WORK:
In the initial phase of our Autonomous Mobile Robot project, we've focused on foundational aspects such as
Preliminary Design:
✓ Developed a basic CAD model to visualize the structural layout
Defined Hardware specifications:
✓ Collaboratively outlined the robot's sensory requirements, considering accuracy and range criteria.
✓ Determined power system specifications, including anticipated voltage and battery type for seamless
integration.
ROS and Gazebo Integration:
✓ Utilized ROS and gazebo for simulation and experimentation purposes.
Budget Allocation:
✓ Allocated a preliminary budget for materials and components.
MMIT Lohgaon
B.E. Project Stage I
AY 2023-24
13
REFERENCES
[1] [ROS Topics]," in IEEE Robotics & Automation Magazine, vol. 17, no. 1, pp. 13-14, March
2010, doi: 10.1109/MRA.2010.935808.
[2] N. DeMarinis, S. Tellex, V. P. Kemerlis, G. Konidaris and R. Fonseca, "Scanning the Internet
for ROS: A View of Security in Robotics Research," 2019 International Conference on
Robotics and Automation (ICRA), Montreal, QC, Canada, 2019, pp. 8514-8521, doi:
10.1109/ICRA.2019.8794451.
[3] Bruno Steux and Oussama El Hamzaoui. tinySLAM: A SLAM algorithm in less than 200
lines C-language program. In ICARCV, pages 1975--1979. IEEE, 2010.
[4] M. Köseoğlu, O. M. Çelik and Ö. Pektaş, "Design of an autonomous mobile robot based on
ROS," 2017 International Artificial Intelligence and Data Processing Symposium (IDAP),
Malatya, 2017, pp. 1-5, doi: 10.1109/IDAP.2017.8090199
MMIT Lohgaon
B.E. Project Stage I
AY 2023-24
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