"Robotics Software Engineer with Mechanical Engineering background"

Developing autonomous mobile robots with ROS2, focusing on navigation, localization, and sensor integration. Building robotic systems that navigate and interact with real-world environments.

Focus Areas: Autonomous Mobile Robots • ROS2 Navigation • SLAM & Localization • Sensor Integration (LiDAR, IMU, Camera) • Computer Vision for Robotics

Patrol Navigation Controller (C++ with ROS2 Nav2)

• Waypoint patrol with cyclical behavior
• Pose orientation handling with quaternions
• AMCL localization integration
• Callback-based asynchronous goal management

Random Explorer Bot (C++ with ROS2 Nav2)

• Autonomous exploration implementation
• Dynamic path planning
• SLAM-based environment mapping

Waypoint Navigation (Python with ROS2 Nav2)

• Waypoint management system
• Real-time navigation feedback
• Nav2 action server integration

Autonomous Navigation with Obstacle Avoidance (Python)

• Custom navigation implementation
• Real-time obstacle detection and response
• Path replanning algorithms

Wall Following Controller (Python with PID Control)

• LiDAR scan data processing and filtering
• PID control implementation
• Multi-zone collision detection
• State machine logic: search → find wall → follow → escape
• Adaptive speed based on obstacles

Wall Following Controller (C++ implementation)

• High-performance variant with optimized sensor processing
• Real-time control loop implementation

MoveIt Pick & Place Demo (Python)

• Pick-and-place operations with MoveIt
• Computer vision for object detection
• Arm trajectory planning and execution
• Coordinated motion control

Colored Object Picker (Python with OpenCV)

• Real-time object detection and tracking
• Vision-based positioning and manipulation
• HSV color space filtering

Automatic Water Pumping System (MATLAB + Arduino)

• Sensor integration fundamentals
• Arduino hardware control
• Real-time monitoring system

ROS2 Development → Navigation systems with Nav2, action servers, and lifecycle management
SLAM & Localization → AMCL localization, working with GMapping/Cartographer
Sensor Processing → LiDAR and camera data processing, filtering techniques
Control Systems → PID controllers for mobile robot navigation
Computer Vision → Object detection and tracking with OpenCV, basic PyTorch models
System Integration → ROS2 launch files, parameter configuration, node communication
Hardware Basics → Arduino sensor integration and embedded control

Learning Next: Robot simulation (Gazebo, Isaac Sim) • ML model optimization • Advanced navigation patterns

Building Skills In:

• ROS2 Advanced Features → Nav2 behaviors, custom plugins, lifecycle nodes
• Simulation Tools → Gazebo environment setup and robot modeling
• Machine Learning → Deploying PyTorch models for robot perception
• Production Code → Clean code practices, testing, and version control workflows
• Hardware Projects → Arduino-based sensor integration experiments