Follow-me with ADBSCAN on Aaeon Robot¶
This tutorial tells you how to run the ADBScan-based Follow-me algorithm from Robotics SDK using Intel® RealSense™ camera input. We used a custom Aaeon Robot for validation of the algorithm. The Intel® RealSense™ camera publishes to “/camera/depth/color/points” topic. The adbscan_follow_me node subscribes to the corresponding topic, detects the obstacle array, and outputs to the “/cmd_vel” topic of type geometry_msg/msg/Twist. This twist message consists of updated angular and linear velocity of the robot to follow the target, which can be subsequently subscribed by a robot-driver.
Getting Started¶
Install Deb package¶
Install the ros-humble-follow-me-tutorial Deb package from the Intel® Robotics SDK APT repository.
sudo apt update sudo apt install ros-humble-follow-me-tutorial
Run Demo¶
Run the following script to launch the Follow Me Application tutorial on the Aaeon robot.
source /opt/ros/humble/setup.bash /opt/ros/humble/share/tutorial-follow-me/scripts/follow-me.sh
After running above command, you can observe that the robot detects the target if the target is within a tracking radius (~0.5 - 0.7 m) and follows when target person is moving
Note
There are reconfigurable parameters in /opt/ros/humble/share/follow_me_tutorial_aaeon/followme_adbscan_RS_params.yaml file. The user can modify parameters depending on the respective robot, sensor configuration and environments (if required) before running the tutorial. Find a brief description of the parameters in the following table.
Lidar_type |
Type of the pointcloud sensor. For Intel® RealSense™ camera inputs, the default value is set to RS |
Lidar_topic |
Name of the topic publishing pointcloud data |
Verbose |
If this flag is set to True, detected target object locations will be printed as screen log output. |
subsample_ratio |
This is the downsampling rate of the original pointcloud data. Default value = 15 (i.e. every 15-th data in original pointcloud is sampled and passed to the core adbscan algorithm) |
x_filter_back |
Pointcloud data with x-coordinate > x_filter_back are filtered out (positive x direction lies in front of the robot) |
y_filter_left, y_filter_right |
Pointcloud data with y-coordinate > y_filter_left and y-coordinate < y_filter_right are filtered out (positive y-direction is to the left of robot and vice versa) |
Z_based_ground_removal |
Pointcloud data with z-coordinate > Z_based_ground_removal will be filtered out. |
base, coeff_1, coeff_2, scale_factor |
These are the coefficients used to calculate adaptive parameters of the adbscan algorithm. These values are pre-computed and recommended to keep unchanged. |
init_tgt_loc |
This value describes the initial target location. The person needs to be at a distance of init_tgt_loc in front of the robot to initiate the motor. |
max_dist |
This is the maximum distance that the robot can follow. If the person moves at a distance > max_dist, the robot will stop following. |
min_dist |
This value describes the safe distance the robot will always maintain with the target. If the person moves closer than min_dist, the robot stops following. |
max_linear |
Maximum linear velocity of the robot |
max_angular |
Maximum angular velocity of the robot |
max_frame_blocked |
The robot will keep following the target for max_frame_blocked number of frames in the event of a temporary occlusion. |
Troubleshooting¶
Failed to install Deb package: Please make sure to run
sudo apt updatebefore installing the necessary Deb packages.You may stop the demo anytime by pressing
ctrl-C.If the robot rotates more than intended at each step, try reducing the parameter max_angular in the parameter file.
For general robot issues, go to: Troubleshooting for Robot Tutorials.
If the motor controller board does not start, restart the robot.