Automation Department

Autonomous Navigation & Path Planning

Algorithms that let robots decide where to go and how to get there — route planning, obstacle avoidance and autonomous positioning in dynamic competition arenas.

Control Systems & Feedback Loops

PID loops, state machines and closed-loop control that turn perception into precise motion — keeping every actuator, motor and mechanism on target in real time.

Sensor Integration & Fusion

Cameras, LiDAR, encoders and IMUs combined into one coherent picture of the world — calibrated, synchronized and fused into reliable state estimates.

Perception & Decision Making

Computer vision for object detection and tracking, feeding on-board decision systems that choose the robot's next move — lines, markers, targets and terrain, interpreted in milliseconds.

Programming & Tools

• Python
• C++ for Real-Time Systems
• MATLAB & Simulink
• ROS / ROS 2

Perception & Vision

• OpenCV
• YOLO & Object Detection Models
• Kalman Filters & Tracking
• TensorFlow / PyTorch

Robotics Algorithms

• PID & State-Machine Control
• Path Planning (A*, RRT)
• SLAM & Localization
• Sensor Fusion

Vision-Guided Autonomous Navigation

Camera-driven navigation stack that fuses visual detection of lines and markers with path planning and obstacle avoidance for fully autonomous arena runs.

Multi-Sensor State Estimation

Kalman-filter pipelines that merge encoders, IMU and vision into a single trusted position estimate — the difference between thinking you know where you are and knowing.

Robocon Task Automation

End-to-end autonomous task sequences for competition rounds — perception triggers, decision logic and actuator control choreographed into reliable mission execution.

Control Loop Design & Simulation

MATLAB/Simulink modeling of motor controllers and mechanism dynamics — simulated, tuned and validated before a single line runs on hardware.