Longsen Gao

Full-stack in Robotics ∩ Control ∩ Deep Learning

5 minute read

Three-Arm System Build up for Hardware Notebook

This blog will record some cool stuff that I recently worked on, on how to configure the three robot arm in our lab (UR5e x 1, UR10e x 1 and WAM x 1.) Note that this instruction has lots of changes compared with the instruction in my

. Don’t use that one for this project!

-Last update: 2026.4.17

Section 1: Environment Preparation

Step 1: Install anaconda

Anaconda3-2024.06-1-Linux-x86_64.sh (you can install any version later than 2021 based on my tests.)

sudo chmod +x Anaconda3-2024.06-1-Linux-x86_64.sh

./Anaconda3-2024.06-1-Linux-x86_64.sh

Step 2 : install git

sudo apt install git

Step 3 : install the MuJoCo library

  1. Download the MuJoCo library from

    https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz

(Update: now you don’t need to do this, just follow the operations below.)

  1. create a hidden folder :
sudo mkdir /home/agman/.mujoco

sudo chmod a+rwx /home/agman/.mujoco

(Note: if the folder you created without any permission, use “sudo chmod a+rwx /path/to/file” to give the folder permission)

cd /home/agman/.mujoco

git clone https://github.com/gaolongsen/mujoco210.git

  1. include these lines in .bashrc file(terminal command: gedit ~/.bashrc):

    gedit ~/.bashrc

  2. Then add the following four lines on the bottom of you .bashrc file:

export LD_LIBRARY_PATH=/home/agman/.mujoco/mujoco210/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia
export PATH="$LD_LIBRARY_PATH:$PATH"
export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libGLEW.so

(:point_up_2:Note that don’t forget to replace the wam with your local PC name.)

Note that for ubuntu 22.04, you should also install the following package:

sudo apt install libglew-dev

  1. source ~/.bashrc
  2. Test that the library is installed by going into:
cd ~/.mujoco/mujoco210/bin

./simulate ../model/humanoid.xml

If you are successful, you will see like this:

Step 4 Install mujoco-py:

conda create --name mujoco_py python=3.10

conda activate mujoco_py

sudo apt update

sudo apt-get install patchelf

sudo apt-get install python3-dev build-essential libssl-dev libffi-dev libxml2-dev

Note that below command is different than the instructions for Ubuntu 20.04 with Python 3.8, be care!

sudo apt-get install libxslt1-dev zlib1g-dev libglew2.2 libglew-dev python3-pip

git clone https://github.com/openai/mujoco-py

cd mujoco-py

pip install -r requirements.txt

pip install -r requirements.dev.txt

pip3 install -e . --no-cache

Step 5 reboot your machine 

sudo reboot

Step 6 run these commands 

conda activate mujoco_py

sudo apt install libosmesa6-dev libgl1-mesa-glx libglfw3

sudo ln -s /usr/lib/x86_64-linux-gnu/libGL.so.1 

sudo ln -s /usr/lib/x86_64-linux-gnu/libGL.so

pip3 install -U 'mujoco-py<2.2,>=2.1'

pip install "cython<3"

(PS: above command you don’t have to do if you run the following example test without any error happened. But based on our test, you probably need to downgrade the cython to avoid any potential problem.)

cd examples

python3 setting_state.py

If you successfully setup your environment, you will see the demo run like this:

If you’re getting a Cython error, try:

pip install "cython<3"

Note: if you use Pycharm as your compiler, don’t forget to add this line on Run -> Edit Configuration -> Environment variables:

LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/wam/.mujoco/mujoco210/bin:/usr/lib/nvidia;LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libGLEW.so

(:point_up_2:Note that don’t forget to replace the wam with your local PC name.)

Step 7 Install ROS2 Humble

Please follow the official website here: https://docs.ros.org/en/humble/Installation/Ubuntu-Install-Debs.html

Section 2: Prepare UR robotics

Step 1 **Set up UR Driver for ** UR10e 

sudo apt-get install ros-humble-ur

ros2 launch ur_calibration calibration_correction.launch.py \
    robot_ip:=192.168.1.5 \
    target_filename:="${HOME}/my_ur10e_calibration.yaml"

Note that when you connect with UR10e, be careful!!! In Ubuntu 22.04, if you use PyCharm, PyCharm will occupy port 50001! Here are the workflow between your PC and the robot arm.

Your PC (192.168.1.22)        UR10e Robot (192.168.1.5)
  │                                    │
  │  ROS driver startup                │
  │  → Listen on port 50001 ───────────┤
  │                                    │
  │            Pannel Button ▶         │
  │            Run External Control    │
  │                                      │
  │  ← ─ Robot reverse connection 50001 ─┤
  │  （That's why we call "reverse"）     │

So, 50001 is a port on your PC, not on the robot. The notion of ‘changing 50001 on the robot controller’ is not really correct.

In the External Control URCap, the only thing you can set is which port on your PC the robot should connect to, and that port must match the one the UR driver is listening on.

The UR driver uses four ports in total:

Parameter Default Value Function
reverse_port 50001 Port used for the robot’s reverse connection back to the PC; this is the one most likely to conflict with PyCharm.
script_sender_port 50002 Port used to send URScript to the robot.
trajectory_port 50003 Port used to send trajectory data.
script_command_port 50004 Port used to send control commands.

To avoid that, we need to reset up the port for UR and here is my example below:

ros2 launch ur_robot_driver ur_control.launch.py \
    ur_type:=ur10e \
    robot_ip:=192.168.1.5 \
    kinematics_params_file:="${HOME}/my_ur10e_calibration.yaml" \
    reverse_port:=50010 \
    script_sender_port:=50020 \
    trajectory_port:=50030 \
    script_command_port:=50040 \
    launch_rviz:=true

Then on your UR panel, you should set up as follows:

Note that here the “Custom Port” should not be the same as the script_sender_port you set in your PC.

Step 2 **Set up UR Driver for **UR5e

This is the most challenging part in this project! We know that any UR robotics use the same lib in just one PC whatever you use ROS1 or ROS2. In this context, we need to use the orignal tool from ROS2: namespace and tf_prefix

  • namespace： Add a prefix to each topic/service/node of every robot, e.g., /ur10e/joint_states、/ur5e/joint_states
  • tf_prefix：Add prefixes to all URDF links for each robot，e.g., ur10e_base_link、ur5e_base_link

Because UR driver support these two parameters, so we don’t need to change the original lib from UR.

Port Planning

Assign a separate port range to each machine, spaced far enough apart to avoid future conflicts with PyCharm:

Parameter UR10e UR5e
reverse_port 50110 50210
script_sender_port 50120 50220
trajectory_port 50130 50230
script_command_port 50140 50240

Then we need to create a workspace structure in local for two robot arm as

~/workspace/ur_dual_ws/
└── src/
    └── ur_dual_bringup/
        ├── package.xml
        ├── CMakeLists.txt
        ├── launch/
        │   ├── dual_ur.launch.py        # 主入口
        │   ├── single_ur.launch.py      # 单台 UR 的可复用 launch
        │   └── dual_ur_moveit.launch.py # 可选：MoveIt 部分
        └── config/
            └── dual_ur_controllers.yaml

For dual_ur.launch.py file, we should write:

from launch import LaunchDescription
from launch.actions import IncludeLaunchDescription, GroupAction
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch_ros.actions import PushRosNamespace
from ament_index_python.packages import get_package_share_directory
import os


def generate_launch_description():
    ur_driver_launch = os.path.join(
        get_package_share_directory('ur_robot_driver'),
        'launch',
        'ur_control.launch.py'
    )

    # ---- UR10e ----
    ur10e_group = GroupAction([
        PushRosNamespace('ur10e'),
        IncludeLaunchDescription(
            PythonLaunchDescriptionSource(ur_driver_launch),
            launch_arguments={
                'ur_type':              'ur10e',
                'robot_ip':             '192.168.1.5',
                'kinematics_params_file': os.path.expanduser('~/my_ur10e_calibration.yaml'),
                'tf_prefix':            'ur10e_',
                'reverse_port':         '50110',
                'script_sender_port':   '50120',
                'trajectory_port':      '50130',
                'script_command_port':  '50140',
                'launch_rviz':          'false',
                'use_tool_communication': 'false',
            }.items(),
        ),
    ])

    # ---- UR5e ----
    ur5e_group = GroupAction([
        PushRosNamespace('ur5e'),
        IncludeLaunchDescription(
            PythonLaunchDescriptionSource(ur_driver_launch),
            launch_arguments={
                'ur_type':              'ur5e',
                'robot_ip':             '192.168.1.6',
                'kinematics_params_file': os.path.expanduser('~/my_ur5e_calibration.yaml'),
                'tf_prefix':            'ur5e_',
                'reverse_port':         '50210',
                'script_sender_port':   '50220',
                'trajectory_port':      '50230',
                'script_command_port':  '50240',
                'launch_rviz':          'false',
                'use_tool_communication': 'false',
            }.items(),
        ),
    ])

    return LaunchDescription([ur10e_group, ur5e_group])

In package.xml file, we should write as

<?xml version="1.0"?>
<package format="3">
  <name>ur_dual_bringup</name>
  <version>0.0.1</version>
  <description>Dual UR10e + UR5e bringup</description>
  <maintainer email="you@example.com">you</maintainer>
  <license>Apache-2.0</license>

  <exec_depend>ur_robot_driver</exec_depend>
  <exec_depend>ur_description</exec_depend>
  <exec_depend>ur_moveit_config</exec_depend>

  <export>
    <build_type>ament_cmake</build_type>
  </export>
</package>

In CMakeLists.txt file, we should write as

cmake_minimum_required(VERSION 3.8)
project(ur_dual_bringup)
find_package(ament_cmake REQUIRED)

install(DIRECTORY launch config
  DESTINATION share/${PROJECT_NAME}
)

ament_package()

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