Elkhatib, Mohammed 
ORCID: https://orcid.org/0009-0000-5162-230X
(2025)
Efficient Mapping and Navigation System for Weed Removal Robot in Confined Garden Spaces.
Masters thesis, Concordia University.
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Abstract
Autonomous navigation and mapping technologies are reshaping how robots
interact with their surroundings, enabling a wide range of applications. This the-
sis introduces an autonomous mapping and navigation system for a mobile robot
tailored for weed control in confined, outdoor garden environments. Unlike indoor
robots that often rely on joystick-based manual mapping, the proposed system is
fully automated, delivering a seamless, user-friendly setup experience optimized
for backyard use. The solution leverages Google Cartographer for real-time
SLAM and AMCL for adaptive localization. To optimize exploration coverage,
the robot uses a combination of random exploration for initial mapping and
structured exploration to target unexplored areas effectively. The integrated
A* algorithm ensures efficient path planning and reliable obstacle avoidance
throughout navigation. Extensive simulations and real-world testing demonstrate
the robot’s ability to autonomously map and navigate complex backyard lay-
outs with minimal human intervention. The system shows resilience to dynamic
obstacles, sensor limitations, and uneven terrain, confirming its robustness and
practical utility. A significant contribution of this research is the development of
a fully autonomous, modular navigation framework that removes the need
for manual setup while ensuring high-accuracy mapping. By simplifying navi-
gation in small-scale, unstructured outdoor environments, this work provides a
functional and scalable solution for backyard maintenance and extends the ap-
plicability of autonomous robotics beyond controlled indoor settings. This thesis
highlights how integrating SLAM, adaptive exploration, and planning can provide
effective autonomy for lawn care, contributing to innovation in outdoor robotic
systems.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Elkhatib, Mohammed |
| Institution: | Concordia University |
| Degree Name: | M.A. Sc. |
| Program: | Mechanical Engineering |
| Date: | 10 March 2025 |
| Thesis Supervisor(s): | Xie, wenf fang |
| ID Code: | 995399 |
| Deposited By: | Mohammed Elkhatib |
| Deposited On: | 17 Jun 2025 17:12 |
| Last Modified: | 17 Jun 2025 17:12 |
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