NaovaCodeK1 Installation

NaovaCodeK1 Installation#

This page explains how to install, build, and run NaovaCodeK1.

Note

Target platform: Ubuntu 22.04 (Jammy).

Prerequisites#

You need:

  • A Linux machine with Ubuntu 22.04

  • A user account with sudo permissions

  • Internet access (for package downloads)

  • Around 20+ GB free disk space (more if installing vision dependencies)

  • ROS2 Humble already installed

Open a terminal and move to the project root:

cd /path/to/NaovaCodeK1

If you do not have the repository yet:

git clone git@github.com:Naova/NaovaCodeK1.git
cd NaovaCodeK1

Install Booster Robotics SDK#

Follow the official Booster Robotics SDK installation guide :

cd /path/to/booster_sdk
git clone https://github.com/BoosterRobotics/booster_robotics_sdk.git
cd booster_robotics_sdk
sudo ./install.sh

Install extra dependencies#

Install backward-ros:

sudo apt-get install ros-humble-backward-ros

For build without CUDA (ONNX inference), install ONNX Runtime first:

# aarch64
./third_party_aarch64/install_onnxruntime.sh

# x86_64
./third_party/install_onnxruntime.sh

Build#

Important

Most users should use the without CUDA flow unless they have a GPU that supports CUDA Compute Capability 5.0 or higher.

Build without CUDA (requires ONNX Runtime):

./scripts/build_no_cuda.sh

Build with CUDA (real robot):

./scripts/build.sh

Run#

Simulation (virtual robot):

./scripts/sim_start.sh

Local runs use the committed local override files directly:

  • src/brain/config/config_local.yaml

  • src/vision/config/vision_local.yaml

The launch files load the base config first, then the local override file, and finally the machine-specific files in ~/agents/booster_soccer/ if they exist. This keeps the team-shared local defaults in git while still allowing robot-specific values on the hardware.

Real robot:

./scripts/start.sh

Real robot runs use the same launch flow, but the local override files contain the real-robot values instead of the simulation values.

Configuration notes#

JetPack 6.2 note:

This repository supports JetPack 6.2 and is adapted to the default TensorRT model configured in src/vision/config/vision.yaml.

detection_model:
        model_path: ./src/vision/model/best_digua_second_10.3.engine
        confidence_threshold: 0.2