Designed and developed a two-wheeled Autonomous Mobile Robot (AMR) engineered to act as a home service robot within a simulated indoor building environment. The system autonomously executes pick-and-place tasks on virtual objects. These autonomous systems mirror industrial logistics workflows deployed in automated fulfillment centers. such as Amazon warehouses, where they transport goods and organize shelving, while highlighting the broad applicability of mobile service robotics across diverse commercial fields.

The primary objective of this project was to simulate an autonomous pick-and-place task, providing a scalable testing ground to build and deploy applications for physical robotic systems. To achieve robust autonomous operation, core robotics algorithms, including localization, mapping, SLAM, and navigation were integrated into a unified software pipeline.

Key Features

• Autonomous Mobile Robot (AMR) kinematics model custom-designed using URDF and Xacro
• 3D physical simulation and sensor visualization built with Gazebo and RViz
• Integrated sensor suites including simulated Lidar sensor units and RGB-D depth cameras
• Probabilistic robot localization implemented via the AMCL (Adaptive Monte Carlo Localization) ROS package
• Simultaneous Localization and Mapping (SLAM) powered by the RTAB-Map mapping package
• Autonomous path planning and obstacle avoidance utilizing the ROS Navigation Stack
• Modular ROS node development engineered using C++ and Python in ROS Noetic

System	Ubuntu 20.04 LTS, Windows 10, Interl i7, 32GB RAM, Nvidia GTX1080, 1TB HDD
IDE & Tools	VS Code, Gazebo, RViz, RQT Graph
Programming Languages	C++, Python
Robotics Framework	ROS 1 (Noetic)
Simulation	Gazebo Classic
ROS Packages	RTAB-Map, map_server, AMCL, Map Creator, Navigation Stack, ROS Teleop
Repository