One day, looking for cheap sensors on ebay, I found this interesting board which contained everything I was looking for. It basically consists of a 3-axis accelerometer (ADXL345), a 3-axis magnetometer (HMC5883L), a 3-axis gyroscope (L3G4200D) and a barometric pressure sensor (BMP085). My plan is to build an Inertial Measurement Unit (IMU) (or maybe I should call it Attitude and heading reference system (AHRS)) and in the process learn how to interact and interpret the information all of this sensors provide. The fact is I have some experience using IMUs since I used one on my master thesis and another one on the Avora AUV, but the fact is they come preprogrammed and there is not much point in working with the raw sensor data unless you want to improve the measurement or give it another use.

For this project I am also using an Arduino Duemilanove, for that reason I wanted to call it ArduIMU, but there is already another project with the same name, so I will have to find another name (suggestions would be appreciated). Connecting the sensor board to the Arduino is pretty straightforward, every sensor has an I²C interface so you can access each of them using the Arduino Wire Library. The drawing was done using fritzing, on which I created the corresponding custom part for this board, although I did something wrong and it does not conform to the fritzing graphic standards.

This will be the first of a series of posts I plan to write about this project, since there are several steps I need to take in order to fully understand each sensor and several more to combine them in order to improve accuracy. In this post I want to talk about the accelerometer and how to obtain the roll and pitch angles from it, which is a process that can also be called tilt sensing.

Accelerometers are devices that are capable of measuring the acceleration they experience relative to free-fall,  the same acceleration living beings feel. As a consequence, accelerometers are incapable of measuring the acceleration of gravity, but can be used to measure the upwards acceleration that counters gravity when at rest. This acceleration is measured as 1g on the z-axis, when both pitch and roll angles are zero, but when the sensor is tilted either the x-axis or the y-axis experiences a component of the upward acceleration, whose magnitude depends on the tilt angle.

Pitch & Roll estimation

Obtaining the pitch and roll angles is then a matter of being able to read the accelerometer, convert these readings to the g unit (1g = 9.8 m/s²), and apply the corresponding equations. The process of obtaining and converting the accelerometer readings depends on the accelerometer you are using, in my case, the ADXL345 in its basic configuration, provides 10-bit resolution for ±2g, but has several other ranges (±2g, ±4g, ±8g, ±16g)  and resolutions (from 10 to 13 bits depending on the range) . Generalizing, the formula used to calculate the acceleration from the accelerometer readings is:

G_{Accel} = Raw_{Accel} \cdot \dfrac{Range}{2^{Resolution - 1}}

Once we have the correct acceleration components, we can proceed to calculate the different angles using the following equations:

pitch = \arctan{\left(\dfrac{G_y}{\sqrt{G_{x}^2 + G_{z}^2}}\right)}     roll =\arctan{\left( \dfrac{-G_x}{ G_{z}}\right)}

For more information about where these equations come from, you can read the documentation I include at the end of this post. As you can see, the denominator of the pitch equation is defined to be always positive, so the equation itself only provides [-90, 90] range, which is exactly what is expected for the pitch angle. In contrast, the roll equation provides [-180, 180] range. It is important to take into account that when the pitch angle is 90º, the surge axis (roll) is directly aligned with the gravity vector, thus we cannot measure the roll angle anymore, this is what is called Gimbal Lock.

Also, be aware that the roll equation is undefined when both G_x and G_z are equal to zero, and that for each possible value of the calculation done inside the arctan function there are two valid solutions, not only on the roll but also on the pitch equation. These problems can be easily solved in code by using the function atan2, which eliminates the angle calculation ambiguity by taking into account the quadrant.

Removing short-term fluctuations using a Low-Pass filter

At this point we already have a fully functional pitch & roll estimation system, but if we experiment with it we will discover that the readings fluctuate quite a bit and this may be very annoying for some applications. Removing these short-term fluctuations can be achieved by means of what is called a Low-Pass filter. This type of filter attenuates the higher frequencies of the signal, thus providing a smoother reading. The Low-Pass filter is easily implemented by using the following equation:

y_{t} = \alpha \cdot x_{t} + (1 - \alpha) \cdot y_{t - 1}

Where y_t is our filtered signal, y_{t-1} the previous filtered signal, x_t the accelerometer reading and \alpha the smoothing factor. It probably may seem obvious, but filtering should be done to the accelerometer readings before calculating the angles, instead of to the angles themselves. Regarding the smoothing factor, the lower we set it, the more it will take for the angle to stabilize, so we should not set it too low because then we could lose real-time behaviour. With this I mean that the reading will not correspond to the real angle until it stabilizes, and this could take some time.

The source code & the ADXL345 library

I developed a small library to interface with the accelerometer, even though at the moment I have only implemented the basic functionality, I plan on supporting all of the device features. You can find it in my github account, where you can also find the processing code I used for the video example below. Thanks to the library, the code is pretty straightforward. It just reads the sensor accelerations which are already converted into gs by the library, applies the Low-Pass filter and then uses the roll and pitch equations to calculate the angles.

#include <Wire.h>
#include <ADXL345.h>

const float alpha = 0.5;

double fXg = 0;
double fYg = 0;
double fZg = 0;

ADXL345 acc;

void setup()

void loop()
	double pitch, roll, Xg, Yg, Zg;, &Yg, &Zg);

	//Low Pass Filter
	fXg = Xg * alpha + (fXg * (1.0 - alpha));
	fYg = Yg * alpha + (fYg * (1.0 - alpha));
	fZg = Zg * alpha + (fZg * (1.0 - alpha));

	//Roll & Pitch Equations
	roll  = (atan2(-fYg, fZg)*180.0)/M_PI;
	pitch = (atan2(fXg, sqrt(fYg*fYg + fZg*fZg))*180.0)/M_PI;



The result

For a more interactive visualization of the data, I also developed an example using processing, which consists on a rotating 3D cube. You can see the results in the following video.

In the next post about my Arduino IMU, I will talk about how gyroscopes work and how to interpret the information they provide.

As you may already know, I was involved in the construction of an autonomous underwater vehicle (AUV) for participating at the Students AUV Challenge – Europe, which was held in La Spezia (Italy), at the Centre for Maritime Research & Experimentation, from July 6 to 13. It was a great experience being surrounded by top students from all over Europe and Canada, sharing ideas, conceptions and visions about underwater vehicles and robotics.

The sea basin was divided in two equal arenas, this way at most two teams could be working at the same time. The visibility conditions were quite rough and the water currents at the surface were noticeable. The organization provided us with two different workspaces, one on the outside, beside the competition arena, and the other one inside a warehouse. The combination of heat and humidity made it quite complicated to work, even though we were provided with several fans.

The first 5 days were allocated for practice runs, but the truth is some of the teams used this time to finish the construction of their vehicles, including us. On our first few days we did some recordings with an underwater camera, which we used fine grain our detection algorithms. We also finished the construction and did some preliminary tests in the pools. Unfortunately, when everything was ready, the vehicle suffered some leakage  because of an incorrectly sealed connector, which made us lose more than a day cleaning everything, but at least none of the electronic components were damaged.

After repairing the damage, we repeated the tests and verified that everything was working as expected. During these days, the qualification period started, so we were now running against the clock. When everything was ready again, we proceeded to adjust the navigation algorithms directly in the competition arena, something which took longer than expected because one of the arenas was being used for the qualification rounds. The last day of the qualification rounds, we did some simulations of the qualification mission and finished programming it, but at the end, since we had not done enough tests of the mission, we decided not to put at risk the vehicle and gave up our qualification slot.

We all felt a little bit demoralized because of not being able to qualify, but not everything was lost, we still had our chance on the “Impress the judges” category, and we sure came prepared for this one. A while ago, working on our AUV, a member of the team brought a pair of “virtual reality” glasses that he used on his master thesis project. These glasses had attached an external inertial measurement unit, so that the computer could be aware of the operator’s head motion. Since our vehicle was equipped with a pan-tilt camera system, we developed software that combined the camera and the pan-tilt system with the glasses and the gyroscope, so that the user could look around and see the surroundings of the vehicle.

The judges were quite impressed with our telepresence system and it was kind of fun to see them taking turns to try the glasses. They were also quite interested in some of our innovations, such as our pan-tilt camera system or the use of a bend sensor for water velocity measurement. The award ceremony was kind of a surprise, we won the first prize at the “Impress the judges” category, which was much more than we expected after four months work, competing against teams with years of experience and very mature vehicles. After the award ceremony we had a small good-bye party at Lerici, which was shorter than expected, for some of us at least, because of transportation issues.

During these days I had the opportunity to meet some of the most incredible vehicles I have ever seen, not only because of their design, but because of the fact that they were built by students. The vehicle I liked the most was the Canadian one, from the Team SONIA, with a robust and flexible design and an impressive software. The team was very prepared and it felt like they had every situation under control, which is a demonstration of their years of experience participating at the RoboSub Competition. Suffice it to say they won this year’s SAUC-E and got third place at RoboSub, quite a feat!

I was also impressed by the design of the vehicle SMART-E, from the University of Luebeck, even though I think it might present a painful challenge for autonomous navigation. This vehicle was shaped like a UFO, and was equipped with 3 thrusters each of which had an additional rotational axis so as to achieve vertical motion. The main hull was transparent, so they took advantage of this to build a strobe light, which was a requirement of the competition, using LEDs all around it. This combined with its shape, made it look like a real UFO, or should I say UCO? (Unidentified Cruising Object).

Overall, it was a worthwhile experience, not only competing but also building an autonomous underwater vehicle from scratch, and I surely recommend it to any student. It is an opportunity to gain more knowledge and to test the knowledge you already have, but more importantly to achieve experience in a real life project.

You can read more about our vehicle on our Journal Paper, or visit my youtube channel or the team’s youtube channel, or visit the team’s facebook page.

Recently, I have been working on a very interesting project, consisting in the design and development of an Autonomous Underwater Vehicle (AUV) for participating in the Students AUV Challenge – Europe held in La Spezia (Italy) at NATO Undersea Research Centre (NURC). We are a team composed of 8 students from different areas (Computer Science, Telecommunications, Electronics, Naval Engineering), in which I have the great honor of being the team leader and lead developer. The name of the AUV (and the team) is AVORA, which means Autonomous Vehicle for Operation and Research in Aquatic Environments, it was intentionally picked as a reference to an ancient deity from the Canary Islands.

The goal of the competition is to perform a series of tasks autonomously, without external information sources and within a fixed time frame, although this time frame is sufficiently large. Taking into account the broad spectrum of missions an AUV can accomplish, we can see that each of the tasks tries to emulate situations that arise in real life, in a limited fashion. The tasks are:

  1. Passing through a validation gate constructed of 2 orange buoys on a rope, 4 meters apart.
  2. Performing an underwater structure inspection. This underwater structure is basically a pipeline of cylinders.
  3. Searching and informing another autonomous vehicle about a mid-water target.
  4. Surveying a wall.
  5. Tracking and following a moving ASV.
  6. Surfacing in the surface zone.
  7. Impressing the judges! In this task, the teams are encouraged to be creative and demonstrate interesting features about their vehicles.

As one can see, completing all of these tasks requires certain type of sensors such as a sonar, cameras, depth and pressure sensors, inertial measurement units, etc, and also a great amount of hard work and time. Our AUV is on its way, since it is our first time, the vehicle has to be constructed from the ground up and it is not an easy job preventing water from getting inside everything.

Another problem with not having the vehicle constructed from the beginning is that most of the work has to be done with each sensor alone and that artificial datasets have to be created in order to fine tune the algorithms. Some of the algorithms require large amounts of data so as to validate them, in such cases we are trying to use datasets provided by others, non-related to the competition. But as I say, being the first time it’s difficult to know what to expect.

From now on I will try to post regularly about our progress. Wish us luck!