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From robotics engineer to systems architect: SLAM at depth, manipulation, multi robot fleets, edge AI at scale, and an autonomous fleet system.
8 modules · 48 lessons
Advanced perception and SLAM, robotic manipulation and control theory, multi robot systems and swarm coordination, edge AI and embodied intelligence, industrial IoT and digital twins, a minor project on manipulation and multi robot coordination, safety certification and deployment engineering, and a major project architecting an autonomous fleet system.
This is the final stage of the Ucanly IoT and Robotics track, where robotics engineers become systems architects. Built for practitioners with hands on experience deploying ROS 2 robots with SLAM, edge AI, and secure connectivity, this program goes deep into advanced SLAM with visual and LiDAR sensor fusion, robotic manipulation with inverse kinematics and grasp planning, and multi robot systems with fleet coordination and swarm algorithms. You will build embodied AI with vision language action models and reinforcement learning for control, and architect industrial IoT systems with digital twins and predictive maintenance. In your week six minor project, you will build a multi robot coordination system with manipulation capability, tested in simulation and defended technically. You will also cover functional safety, ISO and IEC standards, and production deployment engineering for fleets of robots. You will graduate having architected a complete autonomous fleet system in your week twelve major project, with multi robot coordination, advanced manipulation, embodied AI decision making, a digital twin, safety certification documentation, and a live technical and safety defense.
Complete this course to earn a verified Ucanly certificate you can add to your profile, share on LinkedIn, and showcase to employers as proof of the skills you've built.
It is recommended, but hands on experience deploying ROS 2 robots with SLAM, edge AI, and secure connectivity is sufficient to start.
Yes, you will design manipulation systems with inverse kinematics, trajectory planning, and grasp planning, applied in your minor project alongside multi robot coordination.
Vision language action models for robot decision making, reinforcement learning for control, and how these techniques combine with classical robotics for more capable autonomous systems.
A complete autonomous fleet system with multi robot coordination, advanced manipulation, embodied AI decision making, a digital twin, safety certification documentation, and a live technical and safety defense to Ucanly mentors.