NaNoBot is a four-wheeled maker-built RC rover designed to map a known indoor environment, accept control over a local network, and support a learned-driving workflow. Its 2020 build write-up centers mapping on a SLAMTEC RPLIDAR A1 and ROS with Hector SLAM, while a separate Donkey Car-based stack handles vehicle control and training. The published autonomous-driving demo needs an important qualification: it ran on an older Raspberry Pi-powered version of the rover, not the documented Jetson Nano configuration.
What NaNoBot is—and what it can do
Dhairya Parikh’s Hackster project, published March 16, 2020, describes a small four-wheel RC platform built to combine indoor mapping, local-network operation and experiments with learned driving. The author reports mapping his house with the rover. It is a specific maker project, not a commercial or general-purpose autonomous rover.
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The project’s documented capabilities have different levels of evidence. The write-up describes 2D LiDAR mapping using ROS and Hector SLAM, web-based rover control through Donkey Car, and a learned-driving workflow. It also shows an obstacle-response driving demo, but says that demo used an older Raspberry Pi-powered model because adequate webcam training data was not obtained in time. It therefore does not establish that the Jetson Nano build autonomously drove the demonstrated route.
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How the mapping and control systems fit together
LiDAR mapping
The RPLIDAR A1 scans the surroundings to provide the range data used for 2D mapping. The project pairs that sensor with ROS and Hector SLAM. In practical terms, the mapping stack and the driving stack are distinct parts of the build: the LiDAR/ROS setup creates a map, while the RC control and learned-driving workflow rely on Donkey Car adaptations.
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Rover control and learned driving
The write-up describes local-network web control and a process for collecting camera input and training a driving model. This is a workflow, not proof of a fully autonomous Jetson-based rover. The author’s future plans included deeper ROS integration, LiDAR-based obstacle avoidance, and adding an IMU and GPS; those are planned or work-in-progress items, not established completed features.
Parts in the 2020 build
The project’s listed components form a practical checklist for understanding the architecture. They describe the documented build, not a guarantee that current versions remain compatible or readily available.
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- 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 driver/shield.
- Vehicle: Exceed RC car, 1/16 scale or larger.
- Mounting: A custom plate, described as laser-cut wood or 3D-printed.
- Power: A power bank for the compute, sensor and control electronics, plus a separate NiMH or Li-Po battery for the car.
The author identifies the RPLIDAR A1 as the mapping sensor and Jetson Nano as the documented compute board. The listed power arrangement matters: the rover’s electronics and RC car have separate power sources in the build description. The author also reports that an insufficient power supply shut down the Nano during an attempted model-training run.
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The article includes setup directions and commands from a ROS Melodic-era software stack, along with older dependencies. They are useful for understanding how the 2020 project was assembled, but should not be treated as a current installation recipe without checking the operating system, ROS distribution, board support, camera drivers and library versions first.
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Camera compatibility is a particular consideration. Parikh says the Jetson camera path depends on supported Sony IMX sensor cameras or suitable USB webcams. He reports trouble detecting the webcam used during development and says the example code was tested with a CSI camera, Pi Camera V2.1 and Logitech C920. Those are the maker’s observations about the project at the time, not a present-day compatibility guarantee. Confirm support for the exact camera and software versions you plan to use before building around them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the project does—and does not—show
- Documented: a RPLIDAR A1-based 2D mapping setup using ROS/Hector SLAM, local-network web control through Donkey Car, and a learned-driving workflow.
- Reported demonstration: mapping the author’s house and obstacle response in a driving demo, with the demo using an older Raspberry Pi-powered rover.
- Not established: a measured mapping-accuracy figure, speed, reliability or field-performance result. The project pages provide no named, independently measured values for these.
- Future work in the write-up: more ROS integration, LiDAR-based obstacle avoidance, and IMU/GPS additions.
The project was listed as “Most Practical – US Based Project” in the 2020 China-US Young Maker Competition. That is recognition context, not independent validation of its technical performance.
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