Tweaking PIDs

Following from my previous post on PID control, I have integrated my old test PID code into a more usable form in the GUI. I will quickly describe what the old test balancer did, and then the recent updates:

I started with the notes from this useful article as a baseline, but then built a very simple C++ class around it. I have also included some fairly standard modifications to the “vanilla” (ideal form) PID controller:

  • Calculating the derivative of the process variable, rather than of the error, to avoid large and sudden outputs in response to a large increase in error.
  • Prevention of integral windup by constraining the integral term and controller output between a minimum and maximum value.

I then started adding additional features built around the controller, which are:

  • Derived class which works on the standard form of the PID controller
  • Qt “widget” class, which displays a number of spin boxes and sliders for controlling the three gains and the output limits of the PID (as well as the min/max limits of the user controls)
  • A graph to trace the set point (SP) and process variable (PV) against time
  • A procedure which starts/stops the PID loop for balancing, in a separate thread (more on this below)

The aim is to control the forward/backward balance of the robot on a slanting surface. The balancing works by adjusting the ankle motors to match the pitch of the IMU link (see older posts here and here). For example, if the robot is on a board which lowered downwards by 5 degrees, the ankles will also point downwards by 5 degrees. The motor’s position is directly controlled by the IMU pitch, and the PID is actually controlling the moving speed with which the motors will reach that position. Unfortunately it’s not possible to do torque or direct speed control with the AX-12 motors.

With only some limited manual tuning, the initial results are fairly good for small and slow movements of a slopes surface, but it will need some tuning to stop the robot from oscillating at higher speeds and falling over. I will continue improving this method, possibly by using more PIDs to control the knee and hip motors at the same time. For now, here is an image of what it looks like running in the GUI:

Screenshot from 2016-07-20 23-37-37

Some useful links on the basics of PID control

Posted in Bioloid Project, Robotics | Leave a comment

Balancing ideas

I had briefly tried in the past a simple balancing function using a PID controller, which aims to balance the Bioloid using the ankle motors, by trying to keep the IMU (accelerometer/gyro) vertical. The result was mixed success, but was only an early test. I am considering revisiting this balancing test, but this time using a number of PID controllers to control multiple groups of leg motors(e.g. hips, knees and ankles), while also using the GUI to make testing faster and easier.


On another note, I recently came across the Nengo Neural Simulator, which is a framework for creating neural networks of leaky integrate-and-fire (LIF) neurons for creating complex computational models. It has been used to create Spaun, the world’s largest functional brain model which is able to perform a number of functions such as vision, memory, counting, as well as drawing what is sees by controlling a simple arm.

What stands out is how easy it is to use the Nengo GUI to build neural networks. The interface runs in the browser and visualisations of neuron spiking activity and other metrics are easily shown for each graphical object. There is also support for scripting in Python. Installing it and trying it out for yourself is pretty simple, just follow the Getting Started guide here.


It would be really interesting to see if some form of PID controller could be built using Nengo, and then used to control the Bioloid’s balancing!

Posted in Bioloid Project, Robotics | Tagged , , , , , , , | Leave a comment

Qt Style Sheets and C++

This is a quick post to show some more updates to the styling of the GUI.

I have been experimenting with customising the look-and-feel of my GUI using Qt Style Sheets (QSS) which are closely related to HTML Cascading Style Sheets (CSS).

Customised widgets

I have so far customised most of the widgets which appear in the GUI, as shown in the following examples. I have chosen a blue/grey theme throughout, with some exceptions for specialised widgets and items.


Initially the style sheets were embedded in QStrings inside the code, but they were dotted around various classes which meant a lot of code was duplicated as I kept adding content. I then moved the style sheets (QSS) for each widget to a separate text file, and set them via each class individually. Finally I found a way of greatly simplifying this by putting all the code into one single file. This is applied through the program’s main window (QMainWindow). Exception widgets use custom QSS Selector ID, which is set with setObjectName(). This is a nice way of applying specialised styles to specific widgets, such as e.g. a button that needs to stand out.

Here are two QSS examples of the standard and special button shown earlier:

QPushButton {
    background-color: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 lightsteelblue, stop: 1 steelblue);
    border-color: #8F8F91;
    border-style: outset;
    border-width: 4px;
    border-radius: 10px;
QPushButton:flat {
    border: none; /* no border for a flat push button */
QPushButton:default {
    border-color: royalblue; /* make the default button prominent */
QPushButton:pressed {
    background-color: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 royalblue, stop: 1 dodgerblue);
    border-style: inset;
QPushButton#redPushButton {
    color: white;
    background-color: red;
    border: solid lightgrey;
    border-style: outset;
    border-width: 4px;
    border-radius: 4px;
QPushButton#redPushButton:flat {
    border: none;  /* no border for a flat push button */
QPushButton#redPushButton:default {
    border-color: grey;  /* make the default button prominent */
QPushButton#redPushButton:pressed {
    color: red;
    background-color: darkred;
    border: solid red;
    border-style: inset;
    border-width: 4px;
    border-radius: 4px;

As usual, the latest source code is available on GitHub if you want to have a look.

Posted in Bioloid Project, Robotics | Tagged , , , , | Leave a comment

Motor dials updated

I have made some updates to the motor dials which control the motor positions. They can now change mode and control motor speed and load. Also, the GUI is regularly updated with some important feedback from each motor: motor voltage and temperature, LED and torque on/off state, and feedback on all the alarm states.

At this point I’m starting to think that an internal model of all the motor control table data would be useful at this point! Rather than classes making direct requests to the motor controller to receive motor information, all the state data could be kept by the controller and updated regularly. Classes would then simply get the latest data from this model when needed. This is however partially the way the controller works already, as it has a model of the ROS-style joint states which hold present positions, present speeds and present torques (loads), as well as goal positions, moving speeds and torque limits. The joint states are published continuously by a ROS publisher. Present and goal positions are the most important data, as the AX-12 by default only performs position control. Moving speed is simply the speed that the motor will use to move between positions, so cannot be used for e.g. a velocity feedback loop. “Torque” is a bit of a misleading term here, as there is no torque sensing in the motors. Torque sensors are only available in much more expensive motors than these. The load values reported by the AX-12 are related to the motor current, but cannot be read while the motor is actually moving. Two notable sources which have more detailed information on this somewhat unclear measurement can be found here on the RoboSavvy forum and here.


I think I’m done with updating these graphical widgets for the time being, as it is detracting from the main goals of exploring MoveIt! and getting the robot walking.

Posted in Bioloid Project, Robotics | Tagged , , , , , , | Leave a comment

Sensor grapher

A sensor plotting window has now been added to the GUI, which shows all data from the IMU and pressure sensors. The accelerometer, magnetometer, gyroscope, heading data and Force Sensing Resistors’ (FSRs) data are all published as ROS messages as shown in this post, so reading them in the Qt GUI is fairly straightforward, in a similar way to how the joint states are being read. The graphs are made using a third party library for Qt called QCustomPlot.

Each graphs show a scrolling 10 second window of buffered data, which can be paused/played. With QCustomPlot it’s easy to enable user interactions with graphs (drag axis ranges with mouse, zoom with mouse wheel, etc.), so I enabled this option whenever the graph is in a paused state.

The y-axis units are currently showing raw data, which I will probably update to show standard values.

Screenshot from 2016-04-03 19-46-07.png

A useful thing I found in Qt with QDockWidget, which is used to create dockable/floating sub-windows, is that these widgets can also be tabbed to save space on the screen. How can this be done in code you may ask? The useful functions I found were: setDockNestingEnabled() (or setDockOptions()), tabifyDockWidget() for QMainWindow, and raise() to select the default tabbed dock widget you want displayed.

That’s pretty much all there is to the sensor grapher. I might add more features to it in the future, but for now it does the basics!

Posted in Bioloid Project, Robotics | Tagged , , , , , , , | Leave a comment

List of ideas

As I have many ideas floating about on what to work on next, I made a list to see try and see which ones are worth prioritising:

  • GUI updates
    • Graph for visualising sensor data
    • Improved motion editing
  • Static balancing
  • Implement MoveIt! trajectory following via GUI
  • Walking routines
  • Advanced movement: Trajectories generate from MoveIt!, combined with active balancing as the robot moves

This is just an initial list, which I hope to expand on in the future.

My immediate plans right now are to continue tidying up some GUI graphical details, implement a simple sensor data plotter (I know rqt_plot does a fairly good job of this already, but I’d like to have sensor data integrated in my GUI) and get back to trajectory generation tests using MoveIt!

Posted in Bioloid Project, Robotics | Tagged , , , , | Leave a comment

More software updates

With the new PC’s development tools up and running, I’ve made a number of updates to the GUI side of things, as well as the background joint controller node. Here are some of the most important from this month.

Joint controller updates

An issue I was having with the joint controller node – a main function of which is to act as a ROS wrapper class around the Dynamixel API – was that I seemed to be getting erroneous values when executing a simple one-off read from the motors. I suspected this was related to the fact that the node is also constantly reading the motors’ feedback values as fast as possible in its main loop. Even though the Dynamixel library seems to have checks for a busy comms bus, there was likely some issue with multiple threads trying to access the bus. This was easily fixed by using ros::spinOnce() in the main loop instead of using an AsyncSpinner which starts up a separate thread.

There is still some problem where positional values for some of the motors seem to be getting constantly corrupted, but only when all the torques are enabled and the motors are struggling to achieve all their goals positions (making the typical whining noise that servos have). I’ve yet to narrow down if this is a software issue or not.

Update to read/write services

The ROS read/write services have been simplified now, and two of the most useful commands are the ones that perform a sync read and write across a number of motors. As these are ROS services they can also be called from a terminal command line. For example, to receive the current position, current speed and current torque of motors 1, 3 and 5 all at once, you can run:

rosservice call /ReceiveSyncFromAX '[1, 3, 5]' 36 3

36 is the start address for the low byte of the present position, and 3 indicates the number of control table values, including the start address, to read for each motor. This is calling the sync_read function that the USB2AX offers, via a ROS service.

In a similar way you can write goal position (100), goal speed (300) and max torque (512) to motors 1, 3 and 5 all at once by running:

rosservice call /SendSyncToAX '[1, 3, 5]' 30 '[100, 300, 512, 100, 300, 512, 100, 300, 512]'

One restriction is that the sync read and write functions can only read/write consecutive control table addresses, as explained in the USB2AX link above, as well as here in the Robotis documentation.

New motor control table value editor

On the GUI side of things, I’ve made a new “motor address editor” as an improvement on the previous “motor value editor”. The new editor allows reading/writing of all control table addresses of the AX-12. You don’t have to worry about writing low/high bytes to the two-byte values, as that is handled by the editor; simply write any valid value to the parameter of interest.

I have kept the old editor as two useful features it has is let me easily write the same value to all motors, as well as send position/speed/torque in “standard” units (rad, rad/s and torque %) instead of raw values.

Recording and executing poses

I added a new test function which moves the robot through a sequence of queued poses that are saved in the GUI. The robot pauses between each sequence based on each pose’s specified delay time.

Because the function’s execution has to pause and wait for the specified dwell time time, I added the code into a separate thread. Originally I did this by subclassing QThread, but then updated it and created a “worker object” which is moved to a thread using moveToThread(), as this seems to be the better way in Qt.

Although this is very simplistic motion editor functionality which has been done many times before by other tools, I thought it would be useful to add to my GUI, as it could prove useful in the future as a complement or even a replacement of the MoveIt! ideas if they don’t work out. This GUI is now essentially a one-stop shop for easily testing various ROS functionality and other ideas as I keep on developing along the way!

Posted in Bioloid Project, Robotics | Tagged , , , | Leave a comment

New development PC

Progress has been slow as my laptop is having overheating problems, so running ROS for any extended periods was getting difficult. For this reason I have almost finished setting up a Linux development environment on my main PC which is more than capable of running ROS and all the graphical tools. I will keep using the laptop for less intensive tasks and for development away from home.

I am using Linux Mint 17.3 Rosa 64-bit on the PC vs Linux Mint 17 Qiana 32-bit on the laptop (laptop had Wi-Fi driver issues with 64-bit version), but still sticking with ROS Indigo instead of upgrading to the more recent Jade, to avoid any surprises.

Installation of ROS was straightforward as with the laptop, and follows the Ubuntu installation instructions, as Mint is built on Ubuntu. The only edit I had to make was change the repository name to use the Ubuntu Trusty codename instead of the Mint Qiana codename, so the repository name looks like this:

deb trusty main

I use Dropbox to keep everything in sync and share the same catkin workspace between the two machines. The only minor annoyance is having to delete the build and devel folders and rebuilding my packages when switching between machines, because of the 32-bit/64-bit difference.

Posted in Bioloid Project, Robotics | Tagged , , , , , | Leave a comment

2015 in review

The stats helper monkeys prepared a 2015 annual report for this blog.

Here’s an excerpt:

A New York City subway train holds 1,200 people. This blog was viewed about 7,100 times in 2015. If it were a NYC subway train, it would take about 6 trips to carry that many people.

Click here to see the complete report.

Posted in Site updates | Tagged , , , , , | Leave a comment

Fitting the foot force sensors

I finally got round to installing the force sensing resistors to the underside of the Bioloid’s feet. I am using the FSR 400 Short model, with male end connectors, which are small and flat and didn’t fit in the square female header pins which often come at the end of breadboard wires. I didn’t want to solder directly as I’ve damaged these sensors in the past, so I followed a suggestion on the Adafruit website and ended up using these PCB terminal blocks.

The force sensing element of the FSR fits nicely in the pre-existing cut-out on the underside of the Bioloid foot plate, and the pins are fed through a hole in the plate so they can be connected on the topside. However, in the standard Bioloid humanoid configuration, the foot plates are attached at an offset from the ankle angle brackets, which means there is not enough space between the footplate and the bracket to connect the pins. To fix this, I moved the footplates so that the ankle brackets sit along their centres. There is still some free space between the footplates when the Bioloid sits in its resting position, so hopefully the feet will not collide with each other after this change.

For completeness, I also made changes to the CAD model and also added FSR CAD models to the feet, although that is just a cosmetic addition to the model (on a related side-note, I also found a CAD model of the USB2AX which adds to the realism)! The robot’s URDF files and CAD models have been updated on GitHub.

Here are some pictures of the results:

Posted in Bioloid Project, Robotics | Tagged , , , , , , | Leave a comment