Applied Robotics
Build intelligent machines using sensors, actuators, control systems, ROS, and vision - from prototype to deployment.
From Parts to Autonomous Systems
The Applied Robotics program takes you from fundamentals to real robot behavior: sensing, perception, control, and autonomy.
You'll build with hardware + simulation workflows to create robotics pipelines that survive real-world noise and edge cases.
Program Outcomes:
- ->Integrate sensors (IMU/camera) and actuators with stable control loops
- ->Build ROS-based architecture and debugging workflow
- ->Implement vision pipelines for detection and tracking
- ->Tune navigation behaviors with obstacle avoidance
- ->Ship a capstone robot demo with a complete autonomy stack
Robotics Tool Stack
ROS / ROS2
Robotics OS
Python
Control & Logic
OpenCV
Computer Vision
Arduino
Microcontrollers
Raspberry Pi
Edge Compute
Gazebo / Sim
Simulation
Sensors & IMU
Hardware I/O
Docker
Deployment
Structured Learning Path
Electronics & I/O Basics
GPIO, PWM, motor drivers, sensor reads, noise & filtering.
Control Systems (PID & Tuning)
Stable feedback loops, PID tuning, smoothing, trajectory control.
Kinematics & Motion Planning
Forward/inverse kinematics, constraints, calibration workflows.
ROS Foundations
Nodes, topics, services, TF frames, bags, debugging tools.
Vision & Perception
OpenCV pipelines, detection, tracking, depth basics.
Localization & Mapping
Odometry, IMU fusion, mapping concepts, localization strategies.
Navigation & Obstacle Avoidance
Path planning, costmaps, avoidance tuning, behaviors.
Simulation Workflow
Gazebo/Sim testing, scenario iteration, regression checks.
Edge Deployment
Dockerized builds, device profiling, logs and field fixes.
Capstone Robot Build
A complete autonomous robot demo: build -> test -> refine -> ship.
FROM CODE TO CONTROL.
Engineering intelligent machines for the real world.
Wall of Fame
Frequently Asked Questions
Everything you need to know
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