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If you are a lover of all-things remote-conteolled, it’s likely that you know a thing or two about controllers. You’ll have one or two of the things, both the familiar two-joystick type and the pistol-grip variety. But had you ever considered that there m ight be another means to do it? [Andrei] over at ELECTRONOOBS has posted a guide to a tilt-controlled RC car. It is a good example of how simple parts can be linked together to make something novel and entertaining, and a great starter project for an aspiring hacker.

An Arduino Nano reads from an accelerometer over an I2C bus, and sends commands over a wireless link, courtesy of a pair of HC-12 wireless modules.  Another Nano mounted to the car decodes the commands, and uses a pair of H-bridges, which we’ve covered in detail, to control the motors.

The tutorial is well done, and includes details on the hardware and all the code you need to get rolling.  Check out the build and demo video after the break.

We’d love to see this idea turned up to 11 by using a more capable base vehicle, and finer controls on the steering– A Honda Civic perhaps?

[Thanks to Baldpower for the tip!]

[Carson] didn’t know how to use an accelerometer until he wired one up to a Teensy and put it all in a hat. The result is a joystick that will probably cause you neck problems if you play video games for very long. You can see a video of how the device came to be and how it works, below.

We liked the approach of building up the circuit and testing it before integrating it with the hat. He used a small breadboard with half the Teensy pins hanging off. That seems to work, although we’d be worried about something shorting or floating pins causing issues. Of course, if you drove the disconnected pins as outputs or inputs with pullups that might not be a big deal.

A lot of the video is focused more upon the setup of the custom controller for some specific games, but it did seem to work well. We couldn’t help but be envious of anyone who can move their neck that much without aches and pains.

The controller didn’t seem very practical, if we’re honest, although he did get a little better at using it by the end of the video. It was a fun way to experiment with an accelerometer, however it would probably be nice to add a battery and some wireless communication so that you aren’t trailing a cable.

The code is available via Pastebin. About the biggest takeaway from that was the need to program a dead zone so that tiny movements don’t turn into control inputs.

If you are more interested in how these accelerometers work — which is quite interesting — [Bill Hammack] has just the video for that. If moving your head isn’t really your cup of tea, you can use the same ideas for gesture control, as well.

When you think of world-changing devices, you usually don’t think of the washing machine. However, making laundry manageable changed not only how we dress but how much time people spent getting their clothes clean. So complaining about how laborious our laundry is today would make someone from the 1800s laugh. Still, we all hate the laundry and [Andrew Dupont], in particular, hates having to check on the machine to see if it is done. So he made Laundry Spy.

How do you sense when the machine — either a washer or a dryer — is done? [Andrew] thought about sensing current but didn’t want to mess with house current. His machines don’t have LED indicators, so using a light sensor wasn’t going to work either. However, an accelerometer can detect vibrations in the machine and most washers and dryers vibrate plenty while they are running.

The four-part build log shows how he took an ESP8266 and made it sense when the washer and dryer were done so it could text his cell phone. He’d already done a similar project with an Adafruit HUZZAH. But he wanted to build in some new ideas and currently likes working with NodeMCU. While he was at it he upgraded the motion sensor to an LIS3DH which was cheaper than the original sensor.

[Andrew] already runs Node – RED on a Raspberry Pi, so incorporating this project with his system was a snap. Of course, you could adapt the approach to lots of other things, as well. The device produces MQTT messages and Node – RED subscribes to them. The Pushover handles the text messaging. Node – RED has a graphical workflow that makes integrating all the pieces very intuitive. Here’s the high-level workflow:

You might wonder why he didn’t just have the ESP8266 talk directly to Pushover. That is possible, of course, but in part 2, [Andrew] enumerates some good reasons for his design. He wants to decouple components in the system for easier future upgrades. And MQTT is simple to publish on the sensor side of things compared to API calls which are handled by the Raspberry Pi for now.

Laundry monitoring isn’t a unique idea and everyone has a slightly different take on it, even some Hackaday authors. If phone notification is too subtle for you, you can always go bigger.

A delightful version of a clever one-dimensional game has been made by [Critters] which he calls TWANG! because the joystick is made from a spring doorstop with an accelerometer in the tip. The game itself is played out on an RGB LED strip. As a result, the game world, the player, goal, and enemies are all represented on a single line of LEDs.

How can a dungeon crawler game be represented in 1D, and how is this unusual game played? The goal is for the player (a green dot) to reach the goal (a blue dot) to advance to the next level. Making this more difficult are enemies (red dots) which move in different ways. The joystick is moved left or right to advance the player’s blue dot left or right, and the player can attack with a “twang” motion of the joystick, which eliminates nearby enemies. By playing with brightness and color, a surprising amount of gameplay can be jammed into a one-dimensional display!

Code for TWANG! is on github and models for 3D printing the physical pieces are on Thingiverse. The video (embedded below) focuses mainly on the development process, but does have the gameplay elements explained as well and demonstrates some slick animations and sharp feedback.

Using a spring doorstop as a controller is neat as heck as well as intuitive, but possibly not quite as intuitive as using an actual car as a video game controller.

What do you get when you put an ultra-bright LED in the palm of a glove, and strobe it controlled by an accelerometer? A Time Control Glove! In creator [MadGyver]’s own words, it’s “just a stroboscope with frequency adjustment” but the effect is where all the fun is.

The Time Control Glove uses the stroboscopic effect, which many of us have seen used in timeless water drop fountains where the strobe rate makes drops appear to change speed, freeze in place, and even change direction. [MadGyver] made the entire assembly portable by putting it into a glove. An on-board accelerometer toggles the strobe in response to a shake, and the frequency is changed by twisting the glove left or right. The immediate visual feedback to the physical motions is great. The whole effect is really striking on the video, which is embedded below.

Schematics and bill of materials are available on GitHub. Brilliant work! And while we’re discussing the stroboscopic effect, find out how it can be used to tune guitar strings.

[via Arduino Blog]


Filed under: Arduino Hacks, how-to, led hacks

Who doesn’t like the user interface in the movie Minority Report where [Tom Cruise] manipulates a giant computer screen by just waving his hands in front of it? [AdhamN] wanted to unlock his door with hand gestures. While it isn’t as seamless as [Tom’s] Hollywood interface, it manages to do the job. You just have to hold on to your smartphone while you gesture.

The project uses an Arduino and a servo motor to move a bolt back and forth. The gesture part requires a 1sheeld board. This is a board that interfaces to a phone and allows you to use its capabilities (in this case, the accelerometer) from your Arduino program.

The rest should be obvious. The 1sheeld reads the accelerometer data and when it sees the right gesture, it operates the servo. It would be interesting to do this with a smart watch, which would perhaps look a little less obvious.

We covered the 1sheeld board awhile back. Of course, you could also use NFC or some other sensor technology to trigger the mechanism. You can find a video that describes the 1sheeld below.


Filed under: Arduino Hacks

Last fall, I grabbed a robot arm from Robot Geeks when they were on sale at Thanksgiving. The arm uses servos to rotate the base and move the joints and gripper. These work well enough but I found one aspect of the arm frustrating. When you apply power, the software commands the servos to move to home position. The movement is sufficiently violent it can cause the entire arm to jump.

This jump occurs because there is no position feedback to the Arduino controller leaving it unable to know the positions of the arm’s servos and move them slowly to home. I pondered how to add this feedback using sensors, imposing the limitation that they couldn’t be large or require replacing existing parts. I decided to try adding accelerometers on each arm section.

Accelerometers, being affected by gravity when on a planet, provide an absolute reference because they always report the direction of down. With an accelerometer I can calculate the angle of an arm section with respect to the direction of gravitational acceleration.

Before discussing the accelerometers, take a look at the picture of the arm. An accelerometer would be added to each section of the arm between the controlling servos.

arm flat extended with text

Accelerometers

Gravity tugs everything toward the center of the mass of the Earth. It is a force that creates an acceleration exactly just like what you feel when a vehicle begins to move or stop. The force of gravity creates an acceleration of 1 g which is 9.8 m/s2 or 32.15 ft/s2. An accelerometer measures this force.

Integrated circuit accelerometers are inexpensive and small devices readily usable by hackers. One reason they are inexpensive is the high demand for them in smart phones. These small devices are based on etching mechanical structures within the integrated circuit using a technology called MEMS (Microelectromechanical systems).

One design for a MEMS accelerometer is basically a variable capacitor. One plate is fixed and the other mounted some distance away on a spring suspension. When the device is accelerated the suspended plate moves closer or further away from the fixed plate, changing the capacitance. Another uses piezo-resistive material to measure the stress on an arm caused by acceleration.

one axis measurementA single axis accelerometer measures acceleration in only one direction. If positioned so the direction is up and down it will measure the force of gravity but will not detect horizontal acceleration. When the device is tilted between horizontal and vertical the force of gravity is only partially affecting the measurement. This provides the ability to measure the angle of the device with the direction of gravity. The acceleration felt along the tilted axis, for a tilt angle can be calculated by:

A_{X} = g \sin\left ( \theta \right )

Knowing the output of the accelerometer we can determine the angle by taking the inverse sine, the arc sine, of the output:

\theta = \arcsin \left ( \frac{A_{X}}{g} \right )

If you rotate a single axis device through 360° the output is a sine wave. Start with the device outputting zero and consider that 0°. As it rotates, the output is 1 when the angle is 90° and back to zero at 180°. Continuing the rotation, the output becomes -1 at 270°, or -90°, degrees and back to zero at 360°, or 0°.

Notice on the chart that between -60° and 60° the output is nearly linear. This is the best orientation for measuring inclination. Increases in inclination are not as accurate on the other portions of the curve. Also notice that the same output is generated for 45° and 135° (90° + 45°) creating an ambiguity. With a single axis you cannot determine which of those angles is measured.

angle of inclination dual axis angle of inclination

Putting two accelerometers at a right angle to one another creates a 2-axis device which solves the ambiguity problem. As the device is rotated through 360° the outputs are 90° out of phase, the same relationship as the sine and cosine. By combining the measurements there is a unique solution for every angle throughout 360°. The acceleration due to gravity at each angle is given by:

\frac{A_{X}}{A_{Y}}  = \frac{\sin\left ( \theta \right )}{\cos\left ( \theta \right )} = \tan\left ( \theta \right )

which leads to calculating the angle by:

\theta = \arctan \left ( \frac{A_{X}}{A_{Y}} \right )

Actually, one more step is needed to determine the sign of the angle. This requires examining the sign of the values for the X and Y axis. It isn’t necessary to go into this here because a standard programming function handles this automatically.

The orientation of a quadcopter requires a 3-axis accelerometer. The calculations for the three spherical angles combine all three inputs for their results. You’ll need to study this carefully because the standard trigonometric equations can cause anomalies when the quadcopter flips.

First Pass Solution

Accelerometers are easily obtained and relatively cheap. You can find them mounted on breakout boards with voltage regulators and all the supporting circuits from the usual vendors. They are available for 1 to 3 axis, various amounts of g force, and providing either analog or digital outputs. Analog devices need an analog input for each axis being measured. Digital outputs use I2C or SPI buses for communications. I decided to use analog devices because digital units typically only allow two addresses and the arm needs three devices, one for each section.

The robot arm uses an Arduino board so there are at least 6 analog inputs. The original board was a Robot Geek Geekduino, their version of the Arduino Duemilanove, with 8 analog inputs. Unfortunately, when working with the arm I broke the USB connector so switched to a Uno equivalent having only 6 inputs.

accelerometerMy choice for accelerometer is a 3-axis, ±3 g accelerometer breakout from Adafruit, their ADXL335. It has one analog output for each axis. Since I’m measuring three joints that means three boards which adds up to 9 analog outputs.

ADXL335-fbl

Because of the geometry of the arm, however, I only need 5 inputs for these three joints. The shoulder joint only moves from 0° to 180°. This can be handled by a single axis accelerometer by mounting it to read acceleration of 1 g for 0° and -1g for 180°. That provides a unique output for the necessary angles. The elbow and wrist joints each require two inputs. The third input is not needed because their motion is constrained to moving within the vertical plane of the arm.

Frame of Reference

The next issue is the frame of reference. This is a standard problem in robotics work. Early in a project, a global frame of reference is decided upon. This sets the origin for the coordinate system that the robot will follow and the direction of the three axes, usually specified as X, Y, and Z. For the arm, X is straight forward, Y is to the left, and Z is straight up. The zero point is the base of the shoulder. This also defines a global frame for rotation of the arms limbs with zero degrees also toward the front.
elbow anglesshoulder angles

Sensors and controllers each have their own frame of reference. Any differences among these devices and the global frame need to be resolved in software. The shoulder servo’s frame of reference is 0° at the back of the arm and 180° at the front, a clockwise rotation. This is the reverse of the global frame. The elbow servo worked the opposite with a counter-clockwise rotation putting 180° straight up and 90° straight out when the shoulder was vertical. It is 90° off from the global frame of reference.

Sensors also have their own frame of reference. The Y-axis accelerometer measuring the shoulder orientation worked counter-clockwise. Both axis on the accelerometers measuring the elbow worked in the clock-wise direction. This may seem strange but it’s because of the different mounting orientations of the sensors.

Software

The actual code is straightforward once the frame of references are sorted out. A single axis is read from the analog input and its angle calculated with:

const int shouldery = shoulderAnalogY.read();
float shoulder_angle = degrees(asin(shouldery_value / 100.0));

The read() method scales the raw analog input values so ±1g is represented as ±100. The input to asin() is divided by 100.0 to convert to the actual g value. That suffices for the shoulder angle.

The elbow and wrist angles use values from two axis and the calculation is:

const int elbowx = elbowAnalogX.read();
const int elbowy = elbowAnalogY.read();
float elbow_angle = degrees(atan2(-elbowy, elbowx));

The atan2() function is a special version of the arc tangent calculation. It examines the signs of the input value to determine the quadrant of the angle to set the appropriate sign on its result. The negative sign on the elbowy is needed to set the appropriate quadrant. There’s the frame of reference issue, again.

Wrap Up

Adding the accelerometers solved the startup lurching problem well enough. Whether the accelerometers can be used for other purposes remains to be seen.

The accuracy of the angle measurements is not good. In part this is due to using a device that with a +/- 3 g range to measure 1/3 of the devices range, 1 g. The device outputs 0 to 3.3 volts while the Arduino is sampling for 5 volts, again losing accuracy. This might be improved by using an Arduino based on 3.3 volts. I have a couple Dues on hand so might try them. The Uno also provides for adjusting the reference voltage for analog inputs so setting it to 3.3 volts might help.

The analog values need to be calibrated with some care. Each accelerometer outputs slightly different values. Calibration requires measuring the outputs for 1 and -1 g for each axis, recording the values, and using them to scale the voltage input to acceleration. This calibration is not accurate given the other problems with the analog inputs.

Another problem is the mounting of the accelerometers on the arm’s sections. The alignment of the boards with the sections of the arm is not perfect. When the servo is positioned at 90° the accelerometer doesn’t necessarily sit at 90° with respect to the center of the earth. Of course, the servos are not that precise, either. They do not always arrive at the same position, especially when approaching from different directions. Another goal for this project was to use the accelerometer information to more precisely position the servos.

I guess I have to think about this project a bit more, including deciding what I actually want to accomplish with the arm. But then, just saying you have a robotic arm is a terrific hacking cred.


Filed under: Arduino Hacks, robots hacks

Gyroscopes and accelerometers are the primary sensors at the heart of an IMU, also known as an internal measurement unit — an electronic sensor device that measures the orientation, gravitational forces and velocity of a multicopter, and help you keep it in the air using Arduino.

Two videos made by Joop Brokking, a Maker with passion for RC model ‘copters, clearly explain how to program your own IMU so that it can be used for self-balancing your drone without Kalman filters,  libraries, or complex calculations.

Auto leveling a multicopter is pretty challenging. It means that when you release the pitch and roll controls on your transmitter the multicopter levels itself. To get this to work the flight controller of the multicopter needs to know exactly which way is down. Like a spirit level that is on top of the multicopter for the pitch and roll axis.

Very often people ask me how to make an auto level feature for their multicopter. The answer to a question like this is pretty involved and cannot be explained in one email. And that is why I made this video series.

You can find the bill of materials and code here.

mellis-aday

At Arduino Day, I talked about a project I and my collaborators have been working on to bring machine learning to the maker community. Machine learning is a technique for teaching software to recognize patterns using data, e.g. for recognizing spam emails or recommending related products. Our ESP (Example-based Sensor Predictions) software recognizes patterns in real-time sensor data, like gestures made with an accelerometer or sounds recorded by a microphone. The machine learning algorithms that power this pattern recognition are specified in Arduino-like code, while the recording and tuning of example sensor data is done in an interactive graphical interface. We’re working on building up a library of code examples for different applications so that Arduino users can easily apply machine learning to a broad range of problems.

The project is a part of my research at the University of California, Berkeley and is being done in collaboration with Ben Zhang, Audrey Leung, and my advisor Björn Hartmann. We’re building on the Gesture Recognition Toolkit (GRT) and openFrameworks. The software is still rough (and Mac only for now) but we’d welcome your feedback. Installations instructions are on our GitHub project page. Please report issues on GitHub.

Our project is part of a broader wave of projects aimed at helping electronics hobbyists make more sophisticated use of sensors in their interactive projects. Also building on the GRT is ml-lib, a machine learning toolkit for Max and Pure Data. Another project in a similar vein is the Wekinator, which is featured in a free online course on machine learning for musicians and artists. Rebecca Fiebrink, the creator of Wekinator, recently participated in a panel on machine learning in the arts and taught a workshop (with Phoenix Perry) at Resonate ’16. For non-real time applications, many people use scikit-learn, a set of Python tools. There’s also a wide range of related research from the academic community, which we survey on our project wiki.

For a high-level overview, check out this visual introduction to machine learning. For a thorough introduction, there are courses on machine learning from coursera and from udacity, among others. If you’re interested in a more arts- and design-focused approach, check out alt-AI, happening in NYC next month.

If you’d like to start experimenting with machine learning and sensors, an excellent place to get started is the built-in accelerometer and gyroscope on the Arduino or Genuino 101. With our ESP system, you can use these sensors to detect gestures and incorporate them into your interactive projects!

Giant wristwatches are so hot right now. This is a good thing, because it means they’re available at many price points. Aim just low enough on the scale and you can have a pre-constructed chassis for building your own smartwatch. That’s exactly what [benhur] did, combining a GY-87 10-DOF module, an I²C OLED display, and an Arduino Pro Mini.

The watch uses one button to cycle through its different modes. Date and time are up first, naturally. The next screen shows the current temperature, altitude, and barometric pressure. Compass mode is after that, and then a readout showing your step count and kilocalories burned.

In previous iterations, the watch communicated over Bluetooth to Windows Phone, but it drew too much power. With each new hardware rev, [benhur] made significant strides in battery life, going from one hour to fourteen to a full twenty-fours.

Take the full tour of [benhur]’s smartwatch after the break. He’s open to ideas for the next generation, so share your insight with him in the comments. We’d like to see some kind of feedback system that tells us when we’ve been pounding away at the Model M for too long. 

[via Embedded Lab]


Filed under: Arduino Hacks, wearable hacks


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