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Drone: new PID with polar coordinates and HowTo improve reactivity and accuracy

Handling polar coordinates for the PID

Defining a new error

Previously1, we have seen how to manage a fair tracking with a PID control loop that uses a traditional Cartesian coordinates system. Picturing its idea seemed however rather less intuitive than by considering polar coordinates.

We are indeed considering a central point and an offset between it and the position of the roundel. The goal of the PID is to make them be about the same. Therefore, we can simply consider that the distance between the middle of our plan and the roundel is a radius, and an angle is formed by the abscissae axis and this radius (look at the figure below to picture the situation).

With using the radius as the only error parameter, a PID controller can be implemented. In such a representation of the system, what does really matter is for the radius to be as close to zero as possible. Having a different angle does not make any difference in measuring the angle: it is as wrong to be at a 3Pi/4 angle as at a -Pi/2 angle (as long as the radius is the same in both cases). The correction applied to the motors will be the same in intensity, and power applied is what is really at stakes while dealing with this kind of system. The angle will serve the only purpose of telling to the motors in what direction they have to rotate in order to move the drone in the right direction – no PID is necessary for that. Our PID is rather here to tell how fast the drone has to move in that direction.

Changing the code

It appears then more natural and even easier to handle one radius parameter instead of the old two x and y error parameters – one for each axis. This change required yet a few tweaks in the code that had to be tested independently:

  • image analysis returns Cartesian coordinates for roundel position. A switch from Cartesian coordinates to polar ones has to be done. The maths behind this change are straightforward:

  • before doing so, it might be nice to perform an axial symmetry using the x-axis, in order to get a more intuitive picture of the plan. Here is the call to the function changing the coordinates – the symmetry is done while passing parameters:
convertToPolarCoordinates(xval - XMIDDLE, -(yval - YMIDDLE), &radius, &theta);

//XMIDDLE is the x-value for which the image is equally split in two parts (same goes with YMIDDLE and the y-axis)
  • creating a new function for the drone is necessary: it has to be possible to tell it to go in a defined direction, at a given speed. Since the API can only handle orders on two Cartesian axis, to pitch and roll (not mentioning yaw to turn and gaz to change altitude), some basic conversion (converting a movement on one axis to a movement on two perpendicular axis)  has also to be taken care of here.


The core of the algorithm kept unchanged: we merely apply a PID control loop that take the radius as an error parameter that should be close to zero. The results were therefore as good as the previous one (not better). A simpler Proportional Derivative (PD) is being considered, insofar as the Integral term main purpose is to help remove small errors to help being exactly on top of the target, which is not essential for us, as long as the drone does not describe huge circles around it. We will go back later on this precise matter.

Responsiveness tests: which detection is really efficient ?

A need for faster loops

We recently introduced image analysis to deal with tag detection on the PC side. This was done with the idea of taking advantage of a greater computing power and the possibility to choose the kind of tag we want to track – hence getting ride of the limitation induced by the drone firmware. We have experimented that our detection roughly provided the same results, even better on average than the one given by the drone.

Well, this conclusion proved to be partly right. We were indeed a little more efficient than the embedded program in terms of frames received and analyzed: for a new frame received by the computer, our OpenCV algorithm performs a little better than the one embedded on the drone for the same frame. Since the PC sends orders to the drone only when a new frame was received, no matter if we are considering the OpenCV analysis or the embedded one, the PID results were almost the same.

The problem lies in the fact that the computer does not received all the frames got by the 60 fps vertical camera. Whereas this is due to a loss of data happening during the WiFi communication, a problem of bandwidth, or a slow processing time of new frames on the drone or computer side, we don’t really know. Since we have no access to the drone’s firmware yet, we cannot do anything about it. Anyway, our loop were therefore quite slow, running at an average speed of 62ms, meaning less than 10 frames per second (without image analysis, which would decrease again this speed). So as much new orders per second sent to the drone. And this is without taking into account some big slowdown on the computer side, entailing in delays of sometimes more than a second. Which is huge while considering such a reactive system: if the power applied to the motors at a given time is someway high due to a PID correction, a delay even as small as 3/4 of a second can have the drone overshoot its target so it will lose it for good.


To see how much useful data were lost and hence unanalysable by our algorithm, we kept running our OpenCV image analysis and the embedded roundel recognition at the same time, comparing the number of matches. However, the waiting time we used to have in each loop was deleted, so the program could run a new iteration even if the frame received was still the same. Because not getting any new frame does not mean not getting new navigation data, the program had then access to those navigation data send by the drone faster. And among those navigation data are kept the coordinates of tags detected by the algorithm embedded on the drone.

The next figure pictures the experiment process in a chart. Note: the results would have been even more obvious if we  were to split the OpenCV analysis and the navdata handling in two different threads.

The results are listed in the graph below. On average, the embedded algorithm records 1.45 times more different coordinates of the ground tag than the OpenCV algorithm running on the computer.

Our recent discoveries with the speed acquisition of navigation made us test it without any video display on our computer, not to mention video analysis. Our running loop went therefore faster, multiplying its speed by about 300 times. Even if it does not multiply the navigation data like this, we still receive some more, and are sure to get all of them, without losing them while the image analysis is being processed.


Gained responsiveness

The main comment that can be done about those results is that the embedded detection is obviously much more efficient (about 45% more)  once we consider all the useful frames. And the reason for that is that the drone has more frame at its disposal on which it can run the analysis than the WiFi connected computer. The actual drone’s navigation data keep changing even while no new frame is received, which confirms that some frames are lost in the process (otherwise coordinates sent by the drone would come at the same pace than those got by our OpenCV algorithm). Add this to the fact that the image is converted from raw data to an actual image that can be displayed on the computer’s screen during the transmission process, and you can start having a better idea of the benefits in dealing with algorithms on board rather than with a second device, no matter how powerful it is.

One path we could follow in order to get improved results without changing our way of doing things lies in using the newly release ARDrone’s firmware, that allegedly improves the video decoding time process thanks to an other codec. The problem is that this firmware does not seem stable enough at the moment, and it really messes things up with our code.

We could however implement the image analysis in a separate thread, without slowing down the PID algorithm. Since we will gain speed in receiving navigation data as we saw it, we might want to not check twice the same package (i.e. filtering data), and therefore send only once the same order to the drone, so as to avoid jamming the bandwidth.

What to do with those performance conclusions ?

One legitimate question one may ask is whether we really need all those frames for fulfilling our tracking purpose. Our early tests showed us a much more responsive and accurate drone, that kept its target in sight longer. The PID (or PD at least) needs to be tuned again, since the drone has still a tendency to wander around the roundel, and not hover perfectly on top of it.

As for our flock of robot tracking purpose, we may have now a major issue. We will need to do image analysis to detect different robots while following them at the same time. But since we actually need to be quickly responsive for the task that helps follow the leader, we can contemplate doing the following:

  • use the really efficient embedded detection for hovering on top of the leader.
  • take advantage of our own OpenCV image analysis in a separate, slower thread, for reporting coordinates and orientation of the other robots in the flock. We do not need a speed as high as for the hovering task to do so, so it should be just fine for keeping the formation on the ground.
  • since we plan one being able to change our leader at any time, and tags recognized by the drone are limited, we will have to use whatever is made available by the engineers at Parrot. For the moment, roundels, oriented roundels and stripes of different colors can be tracked. This should be just enough for our task.

This can all be summed up in the chart below.


  1. As stated in a former article : http://www.ludep.com/performing-simple-image-analysis-and-full-pid-controller-with-the-drone/ []

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