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System Identification

Modified 6 days ago by gibernas

This document provides instructions for performing an offline system identification procedure. The identification procedure is completed in three stages.

The first stage is the data acquisition stage where we send certain input patterns to our duckiebot and record the camera images along with the input commands sent into a rosbag.

In the second stage, we post-process the recorded rosbag to translate camera images to local pose estimations.

In the third stage, post-processed rosbag is passed to the optimization script which performs nonlinear-optimization to estimate the parameters of duckiebot’s model. The resulting optimal model parameters are saved into a YAML file. We also generate parameter convergence plots, cost convergence plot as well as a plot demonstrating one-step-ahead predictions of the initial and the identified model.

Finally, we run a test to see if the duckiebot can drive straight with the identified model.

A duckiebot version DB-18.

Camera calibration completed.

USB drive.

Calibration pattern, see below.

Video of expected results

Modified 6 days ago by gibernas

The video is at https://vimeo.com/322489840.

Outcome of a successful SysId procedure

Pre-flight checklist

Modified 6 days ago by gibernas

Check: the USB plugged in.

Check: the duckiebot has sufficient battery

Abbreviations

Modified 6 days ago by gibernas

DOCKER_CONTAINER = duckietown/rpi-duckiebot-base:suitability-suite-v1

SOFTWARE_ROOT = /home/software

PACKAGE_ROOT = SOFTWARE_ROOT/catkin_ws/src/05_teleop/calibration

Demo instructions

Modified 6 days ago by gibernas

Step 0: Download the AprilTag that is used in the system identification procedure from here.

Check that side length of the AprilTag matches the value specified in the settings (0.1735 meter), see this. Note that we are using the AprilTag with ID 0. If it is not possible to print the AprilTag with with the specified dimension you can manually adjust its value. Note that this needs to be done before starting the post-processing.

Step 1: SSH into your duckiebot and create the directory for logging.

duckiebot $ sudo mkdir /data/logs

Step 2: Then mount your USB

duckiebot $ sudo mount -t vfat /dev/sda1 /data/logs -o umask=000

Step 3: Now we will run the docker container on our duckiebot that contains the data acquisition script. Open a new terminal on your computer. Make sure that DOCKER_HOST variable is set by checking

laptop $ echo $DOCKER_HOST

if the output is empty then DOCKER_HOST is not set. You can set it with

laptop $ export DOCKER_HOST=ROBOT_NAME.local

Now, we will run the docker container. Be sure to replace the DOCKER_CONTAINER with the name provided under Abbreviations section.

laptop $ docker -H HOST_NAME.local run -it --net host --privileged -v /data/logs:/logs -v /data:/data --memory="800m" --memory-swap="2.8g" --name sysid-pi DOCKER_CONTAINER /bin/bash

Depending on you network speed it might take some time until the duckiebot downloads the container.

Stage 4: Now Place your duckiebot at a distance of approximately 1 meter in front of the AprilTag as shown in the image (Figure 2.4).

Make sure that the AprilTag is oriented exactly the same way as you see in the picture__.

Correct AprilTag orientation

Having the experimental setup ready, we can start collecting some data, enter into the running sysid-pi container if your are not already in, and launch the data-acquisition interface.

laptop $ export DOCKER_HOST=ROBOT_NAME.local

laptop $ docker exec -it sysid-pi /bin/bash

duckiebot $ roslaunch calibration data_collector.launch veh:=HOST_NAME output_rosbag_dir:=/logs use_for:=calibration

Note that, if output_rosbag_dir is not specified the program attempts to save the results to user´s home folder. This will fail it you don’t have enough space in your device.

With data-acquisition interface you can specify

  • the type of the experiment you would like to conduct by choosing amongst the presented options,

  • the parameters of the experiment you chose,

  • whether to save the collected experiment data by replying to the question after the experiment has been completed,

  • whether to do another experiment.

You might want to start with a simple step experiment to get a feeling of your duckiebot. Once you run it, you will probably observe that duckiebot drifting to one side. This is expected! We strongly recommend running one ramp-up and one sine experiment as well to provide sufficiently rich data to the optimization procedure.

It is necessary to have enough data for system identification procedure to work well. Thus we need to make sure that the duckiebot sees the Apriltag during its travel. This can be achieved by adjusting the initial heading of the duckiebot and/or placing the duckiebot off-centered towards the opposite direction. Note that you can also adjust the duration of the input commands. Typically experiments of length 1.5-2.0 seconds are seen to be sufficient.

Step 5: Currently, the rosbags we recorded only contain compressed images. We have to convert them to local poses before we can feed them into the optimization script. In this section we will see how to do it. Note that, we will execute these steps directly on our duckiebot inside the sysid-pi container. Remember that you can always enter into a running container as described at Step 4. Also note that if you want to adjust the default value for the AprilTag, you should do it now. Inside the container, head to SOFTWARE_ROOT/catkin_ws/src/20-indefinite-navigation/apriltags2_ros/apriltags2_ros/config/tags.yaml and adjust the value of Tag0 to what you measure. Note that you have to execute catkin_make after making the change.

We start by creating a two folders to store the post-processed bags: training folder, validation folder. Training folder will contain the rosbags that we will pass to the optimization script in the next step. Validation folder will contain a rosbag that will be used for validating the model by generated by the optimization script. Further create a folder to place recorded rosbags for post-processing, we will refer to this particular folder while generating rosbags containing local pose estimation in the next step. Create these folders with,

duckiebot $ mkdir /data/logs/train_folder /data/logs/validation_folder /data/logs/raw_data

Place the the rosbags you want to process under /data/logs/raw_data,

duckiebot $ mv /logs/MY_ROSBAG.bag /data/logs/raw_data

Now you can launch the script that converts the images to local poses. Note that the folder must contain ONLY the rosbags you want to process. Then

duckiebot $ roslaunch calibration calculate_poses.launch veh:=HOST_NAME input_path:=/data/logs/raw_data lane_filter:=false operation_mode:=1

This process will take couple of minutes depending on the number of images contained in your rosbags. Once you see the message finished processing all N bags, where N is the number of rosbags placed under /data/logs/raw_data, you can exit the program with keyboard command CTRL + C. You can find the processed rosbags under /data/logs/raw_data/post_processed. Verify that the processed bags contain _pp suffix. This indicates that the process has been completed successfully.

Now copy the files you would like to use for training into /data/logs/train_folder/,

duckiebot $ mv /logs/raw_data/post_processed/MY_POST_PROCESSED_ROSBAG.bag /data/logs/train_folder/

Also copy the files you would like to use for verification into /data/logs/validation_folder,

duckiebot $ mv /logs/raw_data/post_processed/MY_POST_PROCESSED_ROSBAG.bag /data/logs/validation_folder

Step 6: Now we are ready to launch our optimization script.

duckiebot $ roslaunch calibration calibration.launch veh:=HOST_NAME train_path:=/data/logs/train_folder validation_path:=/data/logs/validation_folder

Lets look into what each option that we pass to roslaunch means:

  • train_path: the folder under which the program expects to find the rosbags to be used during the optimization routine.

  • validation_path: the folder under which the program expects to find the rosbags to be used for validation.

During the optimization routine you will be prompted some information regarding the status of the optimization. In particular, notice success and x sections. The success option states whether the optimization has converged, whereas x contains the optimal values for the parameters. Here the values are in order: dr, dl, L. Parameter dr is the drive factor for the right wheel, dl is the drive factor for the left wheel and L is the distance from the center of the baseline to the wheels. At the end of the optimization routine, you can head to results folder at

duckiebot $ cd /home/software/catkin_ws/src/05-teleop/calibration/results

Here you will find the results folder and the zipped version of the results folder named according to the program execution date and time. Note that every time the optimization routine is run, a new folder named according to new execution date and time will be automatically generated. Inside this folder you will find

1- ‘optimization’ folder containing; convergence of the model parameters, evolution cost during the optimization,

2- ‘data’ folder containing; rosbags used for training and validation,

3- ‘preprocessing’ folder containing; various stages of data processing,

4- a config file including the parameters values used for various sub-tasks of the whole pipeline,

5- a report file providing a high-level outlook of the optimization process,

6- plots showing the vehicle trajectory and the state evolutions for both model one-step-ahead and n-step-ahead prediction schemes,

7- a copy of the generated yaml file which will be used by the vehicle.

The program also generates a YAML file named HOST_NAME_kinematic_drive.yaml and saves it under /data/config/calibrations/kinematics.

It is worth noting we provide a package level config file to give user control over some of the internal procedures. For details please see PACKAGE_ROOT/configs/system_identification/.

Step 8: Now lets test whether the parameters returned by the optimization script improved the system performance, i.e. whether the duckiebot can drive perform well for certain tasks such as driving straight or following a circle.

Place your duckiebot on the ground, and execute,

duckiebot $ roslaunch calibration data_collector.launch veh:=ROBOT_NAME use_for:=verification model:=kinematic_drive

The test script will provide you with a list of verification paths, select the one you wish to execute.

Troubleshooting

Modified 6 days ago by gibernas

No log have been recorded.

Make sure you mounted USB drive. Please note that you have to should first mount it correctly before you can start data collection.

Logs are created but they are empty.

This might be because the Raspberry-Pi did not have enough time to save the data. Please increase wait_start_rosbag and wait_write_rosbag inside this script.

The duckiebot deviates from the trajectory, so that the AprilTag goes out of the camera’s field of view.

You can adjust the parameters of each command to maximize the duration

There are large discontinuities in the recordings despite the fact that the duckiebot does see the AprilTag most of the time.

One possible cause of this problem is insufficient memory. Please make sure to execute docker run command with --memory="800m" --memory-swap="2.8g" flags which would tell docker to utilize the swap space. Swap space is created and allocated during the initialization process. The swap space allocation is done by default since 5 October 2018. If you had flashed your SD card prior to that, please reflash your SD card. You can verify that you have swap space by executing top command in your duckiebot and inspecting KiB Swap section.

My problem is not listed here. How do I get help?

Though we tested the system identification procedure multiple times on different duckiebots, it is possible that something did not work for you. Please file an issue on GitHub, here.

Demo failure demonstration

Modified 6 days ago by gibernas

The video is at https://vimeo.com/322489800.

Before SysId
Because of mathjax bug

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