Autonomous Systems
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10 lessons3 levels1 capstone project
Design systems that perceive, estimate state, decide, plan and act safely in the physical world. Cover sensing and sensor fusion, localization/SLAM, perception, planning, control, learning, simulation, safety/verification and edge/fleet operations.
BASIC
- Introduction to Robotics (Lecture 1) — MIT (~50 min)
Establish the robotics/autonomy stack: sensing, models, planning, control and system-level reasoning. - Kalman Filter / Sensor Fusion — Machine Learning TV (~20 min)
Learn state estimation and sensor fusion for localization with GPS, IMU and other noisy sensors. - Motion Planning — Modern Robotics (~10 min)
Understand configuration space, collision-free paths and the planning problem behind autonomous motion.
INTERMEDIATE
- Understanding Autonomous Navigation in ROS2: Mapping, Planning, and Execution
Integrate scans, SLAM, maps, costmaps, global/local planning and navigation execution. - Robot Control — Modern Robotics
Connect feedback control, dynamics and trajectory tracking to autonomous action. - Camera/Radar Sensor Fusion — NVIDIA DRIVE Labs
See a production perception pattern for fusing heterogeneous sensors to improve robustness.
ADVANCED
- Wheeled Mobile Robots — Modern Robotics
Deepen nonholonomic mobile-robot kinematics and planning/control tradeoffs. - Autonomous Mobile / Delivery Robot Simulation — NVIDIA Isaac Sim
Use simulation and digital twins to train, test and validate autonomy before hardware deployment. - NVIDIA Isaac Sim + ROS 2 in Docker
Connect ROS 2, simulation, containers and repeatable autonomous-system development environments.