BRNE Social Navigation

Author: Katie Hughes

This set of ROS 2 packages implements Bayes Rule Nash Equilibrium (BRNE) crowd navigation as well as pedestrian tracking and odometry updates using a ZED2i camera. It can be deployed on a Unitree Go1 quadruped, but nothing about this package is specific to this robot.

Clone Packages

To set up the necessary packages:

mkdir -p ${ws}/src
cd ${ws}/src
git clone https://github.com/katie-hughes/brne_social_nav.git
cd ..
vcs import --recursive < src/brne_social_nav/repos/crowdnav.repos

Build Packages

To build all packages:

colcon build

If you want to build this project to only test in simulation, or for use on an external computer that doesn’t have/need the ZED SDK, you can build the project with the following command instead:

colcon build --packages-select crowd_nav brnelib crowd_nav_interfaces pedestrian_tracking brne_py zed_interfaces

Finally, if you are building only the perception related portions of this project (for example, to deploy on an Orin Nano), you can build with the following command:

colcon build --packages-select crowd_nav_interfaces pedestrian_tracking zed_components zed_ros2 zed_wrapper zed_interfaces

Running In Simulation

To run the system in simulation, without being connected to the robot, run:

ros2 launch crowd_nav sim.launch.xml

This will bring up an RVIZ window which represents the scene. If you select a goal pose in RVIZ, you will see the robot move to it, avoiding the static pedestrian obstacles. The number of pedestrians can be adjusted with the launch argument n_peds:=0, 1, 2, or 3. You can also simulate a single moving pedestrian with the launch argument sim_moving:=true (it is false by default). To move the pedestrian, call the service move_ped.

Parameters for these simulations – such as pedestrian locations and the moving pedestrian’s speed – can be set in pedestrian_tracking/config/sim_ped.yaml.

Static Pedestrians

Example launch command:

ros2 launch crowd_nav sim.launch.xml n_peds:=1

sim_static.webm

Moving Pedestrian

Example launch command:

ros2 launch crowd_nav sim.launch.xml sim_moving:=true

To replicate this test, you can run a script with these following service calls:

ros2 service call /set_goal_pose crowd_nav_interfaces/srv/GoalReq "x: 6.0
y: 0.0"
ros2 service call /move_ped std_srvs/srv/Empty

sim_moving.webm

Languages

This project contains both Python and C++ implementations of the BRNE algorithm code. The behavior should be identical. To specify which nodes get run, use the launch argument lang:=C++ or lang:=PYTHON (C++ is default). If you are interested in the time it takes for each iteration of the algorithm to execute, you can run with the launch argument debug_level:=debug.

Parameters

The parameters of the BRNE algorithm, such as the number of samples and the cost parameters, can be tuned by editing brne.yaml.

Running On a Unitree Go1

Connecting to the robot

The Unitree Go1 has an onboard WLAN hotspot that is bridged to the ethernet connections between the boards. When the robot is powered on, check on your compter to see if the UnitreeWifi network is visible. In the event that it doesn’t come up after a few minutes, the robot will need to be rebooted.

Once you connect to the wifi, you will need to configure a static IP on your own computer that matches the subnet 192.168.123.xxx, which is what the boards on the robot are also set up with. (I usually set my IP to 192.168.123.100). Then, you should be able to directly ssh into and see ros topics from all boards inside the dog.

If at any point you stop being able to ping these boards or your ssh session freezes, the hotspot has probably died. The only way around this issue that I have found is to turn the robot off for a few minutes, then power it back on.

Go1 Xavier Setup

The result of the BRNE algorithm is a motion plan represented as a series of cmd_vel commands. In order to process these commands, the unitree_nav package should be installed and running onboard the Go1 Xavier. To process the cmd_vel, run:

ros2 launch unitree_nav control.launch.py use_rviz:=false

Using an onboard laptop

If using a laptop strapped to the back of the robot, this computer will be responsible for pedestrian tracking with the ZED, odometry updates from the ZED, and the BRNE algorithm itself. To set all of this up, run

ros2 launch crowd_nav onboard.launch.xml

As in simulation, you can choose the C++ or Python implementation of the BRNE nodes with lang:=C++ or lang:=PYTHON (C++ is default), and you can see the time for each algorithm iteration to execute with the launch argument debug_level:=debug.

File truncated at 100 lines see the full file