Navigational Robot – Car-5 (ROS2 Autonomy Stack + Dedicated Controller Architecture)
Learn ROS2 the industry way—modular autonomy pipelines, perception stacks, and SLAM-ready navigation workflows.
Car-5 is the fifth robot in the ZAS Navigational Robotics Series, built for students moving from “robot programming” to real robotics engineering—using the same layered architecture used in modern autonomous systems.
Request Platform Details
Product Description
The Navigational Robot – Car-5 (ROS2 Autonomy Stack + Dedicated Controller Architecture) is designed around a professional, industry-style robotics stack.
A dedicated controller board manages low-level motion control, encoder feedback, and sensor interfacing with reliable real-time performance. Meanwhile, the Raspberry Pi runs ROS2 for higher-level intelligence such as localisation, autonomy logic, perception, and planning.
This clean separation between control and compute mirrors how real robots are engineered for stability, scalability, and long-term autonomy.
With ROS2 at the core, students learn modular development: building nodes, publishing sensor streams, and composing reusable navigation pipelines. Optional camera and LiDAR support unlock perception-driven autonomy and SLAM-ready workflows.
What’s Included
A complete ROS2-ready platform with professional architecture.
- Fully assembled ROS2-ready navigational robot car
- Dedicated robot controller board (low-level motion + sensor interface)
- Raspberry Pi interface (40-pin connector for Zero 2W / 3 / 4 / 5)
- High-precision encoder motor drive system (closed-loop motion)
- Multi-sensor autonomy stack support (IMU + encoders + compass + GPS ready)
- ROS2-ready power architecture (stable compute + motor supply separation)
- Expansion interfaces: LiDAR support, camera support (mono/stereo)
- I2C expansion for external microcontrollers and modules
- Real-time debugging interface (OLED / telemetry-ready)
- Integrated power system, wiring, and clean pre-mounted electronics
Optional Add-ons
- Raspberry Pi Zero 2W / 3 / 4 / 5
- LiDAR (TF-Mini-Plus / C1 / A1)
- Monocular camera (OV5647 / IMX219)
- Stereo camera (IMX219-83 Binocular)
- MicroSD (32 / 64 GB)
What Can Students Do?
- Build ROS2 systems using nodes, topics, publishers/subscribers
- Use services, actions, parameters and launch files professionally
- Publish real robot streams: odometry, IMU, compass, GPS
- Implement modular autonomy pipelines aligned with industry workflows
- Understand robust compute + control separation for stability
- Build localisation and navigation pipelines aligned with ROS2
- Develop SLAM-ready autonomy using LiDAR and/or vision pipelines
- Run vision and deep learning algorithms as ROS2 nodes on Raspberry Pi
- Debug and optimise systems using structured telemetry and feedback
- Prepare for university projects and industry-grade robotics development
Computer Vision Techniques
Run perception as ROS2 nodes for SLAM-ready autonomy.
- Feature Detection & Description: SIFT, SURF, ORB, AKAZE, BRISK, FREAK, FAST+BRIEF, SuperPoint
- Sparse Matching: Brute-force, FLANN, KNN, Cross-Check, Lowe’s Ratio
- Dense Matching: Optical Flow (RAFT, DeepFlow), Block Matching, LoFTR, Direct Methods (DSO)
- Geometry Consistency: RANSAC, MAGSAC, PROSAC, LMedS, epipolar constraints
- Motion / Pose / Depth: Essential matrix, Homography, PnP, Triangulation, Disparity, Bundle Adjustment, Loop Closure
- SLAM / Visual Odometry Foundations
Who Is This For?
- University students building industry-grade ROS2 robotics skills
- Robotics clubs and research teams working on SLAM-ready autonomy
- Programs teaching professional robotics architecture and pipelines
- Learners preparing for robotics roles in industry and labs
Where This Fits in the Learning Journey
Car-5 represents professional ROS2 engineering—where autonomy becomes modular, scalable, and industry-ready with perception pipelines and SLAM workflows.
Build Industry-Ready ROS2 Autonomy
Teach real robotics engineering: modular ROS2 systems, perception pipelines, and SLAM-ready navigation.
Contact Us for Pricing & Curriculum