NaNoBot is a maker-built, four-wheeled RC rover that combines a RPLIDAR A1 and ROS-based mapping with network control and a separate learned-driving workflow. Its 2020 project write-up documents mapping the maker’s house, but the autonomous-driving demonstration shown there ran on an older Raspberry Pi-powered version—not the Jetson Nano configuration. The distinction matters when judging what the build actually demonstrated.
What NaNoBot is—and what it is not
Dhairya Parikh’s Hackster project, published March 16, 2020, describes a small rover designed to map a known environment, accept control over a local network, and support a learned driving model. It is a specific maker project, not a general-purpose commercial surveillance rover or a validated autonomous-navigation platform.
The name “surveillance” should also be understood in the project’s limited context: the rover has a camera and can be remotely controlled, while the documented mapping system produces a 2D map using LiDAR. The project does not establish security-system features, continuous monitoring capability, or a level of autonomy suitable for unattended operation.
How the documented build works
Mapping: RPLIDAR A1, ROS, and Hector SLAM
A SLAMTEC RPLIDAR A1 supplies distance scans. The project connects the scanner to ROS and uses Hector SLAM to build a 2D map; Parikh reports mapping his house. The write-up does not give independently measured map accuracy, operating speed, or reliability figures, so the result should be read as a maker’s demonstration rather than a performance benchmark.
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Driving and remote control: a separate software path
The documented design adapts Donkey Car for camera handling, training, and vehicle control, while the LiDAR mapping runs through ROS. In other words, mapping and driving are not presented as one fully integrated navigation stack. The author lists more extensive ROS integration and LiDAR-based obstacle avoidance as future work, not completed features.
The Jetson-versus-Pi demonstration caveat
The write-up’s autonomous-driving demo used an older Raspberry Pi-powered version of the same bot. Parikh says he did not obtain enough webcam training data in time for the Jetson version. Therefore, that video does not establish that the Jetson Nano build autonomously drove the shown route. The project separately documents Jetson-based hardware and the mapping work.
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Parts in the 2020 project
The author’s documented build calls for the following component categories and parts. This is a historical parts list, not a guarantee that every listed revision remains available or is compatible with current software.
| Component | Documented part or role |
|---|---|
| Onboard computer | Jetson Nano Developer Kit |
| Mapping sensor | SLAMTEC RPLIDAR A1 |
| Camera | Raspberry Pi Camera Module V2 or a suitable USB webcam |
| Servo control | PCA9685 servo shield/driver |
| Vehicle | Exceed RC car, 1/16 scale or larger |
| Mount | Custom mounting plate, such as laser-cut wood or a 3D-printed part |
| Power | Power bank for computing, sensor, and control electronics; separate NiMH or Li-Po battery for the RC car |
Keeping the electronics supply separate from the car battery is part of the documented arrangement. The author also reports that an insufficient-power-supply problem shut down the Nano during attempted model training, so power delivery is a practical design constraint, not an incidental accessory choice.
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Camera and software compatibility
The project was written around a 2020 software environment, including ROS Melodic-era instructions and older dependencies. Treat its setup commands as a historical recipe: before following them, check whether the operating system, Jetson board support, ROS distribution, camera drivers, libraries, and Donkey Car components work together in the versions you intend to use.
Camera support is a particular caveat. The author says the Jetson camera path depends on supported Sony IMX sensor cameras or appropriate USB webcams, and describes difficulty detecting the webcam used during development. The example code was reported as tested with a CSI camera, Pi Camera V2.1, and Logitech C920. Those are the maker’s historical test notes, not a current compatibility guarantee for every camera revision, OS image, or software version.
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What the project demonstrates—and what remains unproven
- Documented: RPLIDAR scanning and 2D mapping with ROS/Hector SLAM, including the author’s report of mapping a house.
- Documented: local-network web control through Donkey Car and a learned-driving workflow.
- Qualified: the autonomous-driving demonstration used the older Raspberry Pi-powered model, not the Jetson configuration.
- Planned rather than established: replacing the Donkey Car driving stack with more ROS integration, LiDAR-based obstacle avoidance, and adding IMU/GPS.
- Not reported: independently measured mapping accuracy, speed, reliability, or field performance.
The project was listed as “Most Practical – US Based Project” in the 2020 China-US Young Maker Competition, as reflected in the competition page and project submissions. That recognition is useful context about the project’s competition participation, but it is not independent technical validation.
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