robo are generally an effective and efficient way to reduce overhead costs, increase consistency in products and services, and perform tasks that may be hazardous to humans. The successful design and building of a two-wheeled balancing robot demonstrates a knowledge of control systems and sensor interfacing that can translate to real world applications. The robo will balance on two wheels and be able to have loads of varying weight and size . An accelerometer and a gyroscope feed information back to a pic microcontroller which feeds a PWM signal to two motors that drive the wheels so they stay under the center of mass of the robo .

Xsimple robo in function at accelerometer and gyroscope ________

Accelerometers measure physical acceleration relative to the free-fall along one dimension. An accelerometer equipped in mobile devices is usually a tri-axial accelerometer unit, in which three linear accelerometers are aligned in x,y,z axis respectively and each measures acceleration along its dimension. Because we know the vertical component of the acceleration value of a stationary object must be 1-g (9.8m/s^2), we can compute the tilt angle of the unit (hence the object to which the unit is attached) by simple trigonometry. Detecting portrait/landscape mode in smartphones is done in this way. On the other hand, you cannot know lateral orientation ("yaw") from accelerometers only because we only have the vertical reference (gravity direction) but not horizontal reference.

Gyroscopes in consumer electronics sense angular velocity (angular rate) along one rotational axis. These does not give you the absolute orientation as they are (they give you velocity, or rate, not the actual degree). In order to obtain the orientation from these sensors, you need to "integrate" the rate values from a known initial orientation. Gyroscopes are usually tri-axial as well, making 6-degrees-of-freedom "inertial measurement unit" with 3-axis accelerometers.

Magnetometers are simply a compass. A linear magnetometer measures direction and strength of magnetic field along one dimension. In the absence of external disturbance, it can be used to detect earth magnetic field, which always points to (magnetic) north. For magnetometers, this "reference" also contains horizontal component ("north"), from which you can read off lateral orientation of the device, unlike accelerometers. However, the earth magnetic field is very weak compared to common disturbances around (steel furniture, car passing by, etc), so not quite an accurate source of yaw orientation .

___________ XProcedure

The accelerometer, gyro and the magnetometer form the momentum inertia . The information obtained from each of these sensors is combined in order to get the best position information since one is more sensitive to other depending on the scenario it is being put into.

1. An accelerometer's primary function is to react to and sense linear acceleration. This gives you the position directly. Also, because the gravitational pull is acceleration, this can give you the position of the device perpendicular to the earth's surface. So, the device motion can be tracked using this.

2. The gyro basically gives you the changes in rotational orientation of the device (from a reference or known orientation of the device).

3. The magnetometer determines the orientation of the device with respect to the Earth's magnetic fields.

___________ Xsimple

1. An accelerometer measures force in each of its axis, if it is a 3 axis accelerometer, then on all the three axis. A gyro measures the angular velocity with respect to the body axis.
And a magnetometer gives you the orientation with respect to magnetic north and magnetic south of the earth's magnetic fields.
2. An accelerometer can tell you your tilt relative to the earth's surface (2 axes) but not your heading. In theory, if you knew where you were starting, you could add up acceleration to give you an estimate of position, but in practice, errors add up very quickly. But until they drift too far away from reality, you can use them for very high frame rate estimates of position.
A magnetometer can tell you your heading if you hold it parallel to the ground. But combined with the tilt readings from a 3-axis accelerometer, you can get your heading regardless of how you're holding your device.Gyros are great at giving rotational velocity, but have no absolute reference. Again, if you knew where you were pointing initially, you could get very high frame rate estimates of orientation. But it, too, drifts quickly. The combination of accels plus gyros gives you an Inertial Navigation System, aka Intertial Measurement unit. But you need very high accuracy instruments for this to stay accurate enough to be useful for very long.
3. Add in a GPS. These give you position, but not orientation. But you only get data once per second, so you couldn't steer a car, plane, or rocket with it. So you fuse the low frequency, absolute, GPS and magnetometer data with the high frame rate gyro and accelerometer using something like a Kalman filter. This is a modern navigation system, and it's pretty damn good. I'll stop before bringing in differential, kinematic, and phase-based GPS, which make it even better using multiple GPS receivers and other improvements to get even higher accuracy.

____________ Xupgrade

The technology is growing at the highest level. These days people want to change their smartphone frequently due to upgradation and invention of technology in the smartphone industry.

Upgradation in specification like RAM, internal storage, camera section and processor are specific and user can easily understand these terms.

But when we come to sensors, which play a key role in the conversion of a normal phone into smart one. The user gets trouble with the certain terms like, accelerometers sensor, Gyroscope, magnetometers and fingerprint sensor etc. I'll try to explain in short.

Accelerometers: The accelerometer is used to sense the changes in the orientation of smartphone with respect to the viewing angle of the operator.

Gyroscope: This is the sensor which is used to maintain and control the position, level or orientation based on the principle of angular momentum.

Fingerprint Sensor: Fingerprint Sensors is an automated method of verifying a match between two human fingerprints. Fingerprints are one of many forms of biometrics used to identify individuals and verify their identity .

___________Xtrust

The magnetometer points to magnetic north not true north. To determine where true north is, you also need to know where on the planet's surface you are (i.e., GPS coordinates). Given your lat/long, it is possible to determine the offset from magnetic north that points to true north and therefore the other relevant compass points for the user. As a thought experiment, imagine you are standing halfway between magnetic north and true north; true north would be 180 degrees from magnetic north. If we are standing on the equator, then the angle difference is much smaller. That's declination.

Roll Pitch and Yaw

Accelerometer
To understand this unit we'll start with the accelerometer. When thinking about accelerometers it is often useful to image a box in shape of a cube with a ball inside it. You may imagine something else like a cookie or a donut , but I'll imagine a ball:

accelerometer model

If we take this box in a place with no gravitation fields or for that matter with no other fields that might affect the ball's position – the ball will simply float in the middle of the box. You can imagine the box is in outer-space far-far away from any cosmic bodies, or if such a place is hard to find imagine at least a space craft orbiting around the planet where everything is in weightless state . From the picture above you can see that we assign to each axis a pair of walls (we removed the wall Y+ so we can look inside the box). Imagine that each wall is pressure sensitive. If we move suddenly the box to the left (we accelerate it with acceleration 1g = 9.8m/s^2), the ball will hit the wall X-. We then measure the pressure force that the ball applies to the wall and output a value of -1g on the X axis.

accelerometer model

Please note that the accelerometer will actually detect a force that is directed in the opposite direction from the acceleration vector. This force is often called Inertial Force or Fictitious Force . One thing you should learn from this is that an accelerometer measures acceleration indirectly through a force that is applied to one of it's walls (according to our model, it might be a spring or something else in real life accelerometers). This force can be caused by the acceleration , but as we'll see in the next example it is not always caused by acceleration.
If we take our model and put it on Earth the ball will fall on the Z- wall and will apply a force of 1g on the bottom wall, as shown in the picture below:

accelerometer model

In this case the box isn't moving but we still get a reading of -1g on the Z axis. The pressure that the ball has applied on the wall was caused by a gravitation force. In theory it could be a different type of force – for example, if you imagine that our ball is metallic, placing a magnet next to the box could move the ball so it hits another wall. This was said just to prove that in essence accelerometer measures force not acceleration. It just happens that acceleration causes an inertial force that is captured by the force detection mechanism of the accelerometer.
While this model is not exactly how a MEMS sensor is constructed it is often useful in solving accelerometer related problems. There are actually similar sensors that have metallic balls inside, they are called tilt switches, however they are more primitive and usually they can only tell if the device is inclined within some range or not, not the extent of inclination.
So far we have analyzed the accelerometer output on a single axis and this is all you'll get with a single axis accelerometers. The real value of triaxial accelerometers comes from the fact that they can detect inertial forces on all three axes. Let's go back to our box model, and let's rotate the box 45 degrees to the right. The ball will touch 2 walls now: Z- and X- as shown in the picture below:

The values of 0.71 are not arbitrary, they are actually an approximation for SQRT(1/2). This will become more clear as we introduce our next model for the accelerometer.
In the previous model we have fixed the gravitation force and rotated our imaginary box. In last 2 examples we have analyzed the output in 2 different box positions, while the force vector remained constant. While this was useful in understanding how the accelerometer interacts with outside forces, it is more practical to perform calculations if we fix the coordinate system to the axes of the accelerometer and imagine that the force vector rotates around us

accelerometer model

Please have a look at the model above, I preserved the colors of the axes so you can make a mental transition from the previous model to the new one. Just imagine that each axis in the new model is perpendicular to the respective faces of the box in the previous model. The vector R is the force vector that the accelerometer is measuring (it could be either the gravitation force or the inertial force from the examples above or a combination of both). Rx, Ry, Rz are projection of the R vector on the X,Y,Z axes. Please notice the following relation:
R^2 = Rx^2 + Ry^2 + Rz^2 (Eq. 1)
which is basically the equivalent of the Pythagorean theorem in 3D.
Remember that a little bit earlier I told you that the values of SQRT(1/2) ~ 0.71 are not random. If you plug them in the formula above, after recalling that our gravitation force was 1 g we can verify that:
1^2 = (-SQRT(1/2) )^2 + 0 ^2 + (-SQRT(1/2))^2
simply by substituting R=1, Rx = -SQRT(1/2), Ry = 0 , Rz = -SQRT(1/2) in Eq.1
After a long preamble of theory we're getting closer to real life accelerometers. The values Rx, Ry, Rz are actually linearly related to the values that your real-life accelerometer will output and that you can use for performing various calculations.
Before we get there let's talk a little about the way accelerometers will deliver this information to us. Most accelerometers will fall in two categories: digital and analog. Digital accelerometers will give you information using a serial protocol like I2C , SPI or USART, while analog accelerometers will output a voltage level within a predefined range that you have to convert to a digital value using an ADC (analog to digital converter) module. I will not go into much detail about how ADC works, partly because it is such an extensive topic and partly because it is different from one platform to another. Some microcontroller will have a built-in ADC modules some of them will need external components in order to perform the ADC conversions. No matter what type of ADC module you use you'll end up with a value in a certain range. For example a 10-bit ADC module will output a value in the range of 0..1023, note that 1023 = 2^10 -1. A 12-bit ADC module will output a value in the range of 0..4095, note that 4095 = 2^12-1.
Let's move on by considering a simple example, suppose our 10bit ADC module gave us the following values for the three accelerometer channels (axes):
AdcRx = 586
AdcRy = 630
AdcRz = 561
Each ADC module will have a reference voltage, let's assume in our example it is 3.3V. To convert a 10bit adc value to voltage we use the following formula:
VoltsRx = AdcRx * Vref / 1023
A quick note here: that for 8bit ADC the last divider would be 255 = 2 ^ 8 -1 , and for 12bit ADC last divider would be 4095 = 2^12 -1.
Applying this formula to all 3 channels we get:
VoltsRx = 586 * 3.3V / 1023 =~ 1.89V (we round all results to 2 decimal points)
VoltsRy = 630 * 3.3V / 1023 =~ 2.03V
VoltsRz = 561 * 3.3V / 1023 =~ 1.81V
Each accelerometer has a zero-g voltage level, you can find it in specs, this is the voltage that corresponds to 0g. To get a signed voltage value we need to calculate the shift from this level. Let's say our 0g voltage level is VzeroG = 1.65V. We calculate the voltage shifts from zero-g voltage as follows::
DeltaVoltsRx = 1.89V – 1.65V = 0.24V
DeltaVoltsRy = 2.03V – 1.65V = 0.38V
DeltaVoltsRz = 1.81V – 1.65V = 0.16V
We now have our accelerometer readings in Volts , it's still not in g (9.8 m/s^2), to do the final conversion we apply the accelerometer sensitivity, usually expressed in mV/g. Lets say our Sensitivity = 478.5mV/g = 0.4785V/g. Sensitivity values can be found in accelerometer specifications. To get the final force values expressed in g we use the following formula:
Rx = DeltaVoltsRx / Sensitivity
Rx = 0.24V / 0.4785V/g =~ 0.5g
Ry = 0.38V / 0.4785V/g =~ 0.79g
Rz = 0.16V / 0.4785V/g =~ 0.33g
We could of course combine all steps in one formula, but I went through all the steps to make it clear how you go from ADC readings to a force vector component expressed in g.
Rx = (AdcRx * Vref / 1023 – VzeroG) / Sensitivity (Eq.2)
Ry = (AdcRy * Vref / 1023 – VzeroG) / Sensitivity
Rz = (AdcRz * Vref / 1023 – VzeroG) / Sensitivity
We now have all 3 components that define our inertial force vector, if the device is not subject to other forces other than gravitation, we can assume this is the direction of our gravitation force vector. If you want to calculate inclination of device relative to the ground you can calculate the angle between this vector and Z axis. If you are also interested in per-axis direction of inclination you can split this result into 2 components: inclination on the X and Y axis that can be calculated as the angle between gravitation vector and X / Y axes. Calculating these angles is more simple than you might think, now that we have calculated the values for Rx,Ry and Rz. Let's go back to our last accelerometer model and do some additional notations:

The angles that we are interested in are the angles between X,Y,Z axes and the force vector R. We'll define these angles as Axr, Ayr, Azr. You can notice from the right-angle triangle formed by R and Rx that:
cos(Axr) = Rx / R , and similarly :
cos(Ayr) = Ry / R
cos(Azr) = Rz / R
We can deduct from Eq.1 that R = SQRT( Rx^2 + Ry^2 + Rz^2).
We can find now our angles by using arccos() function (the inverse cos() function ):
Axr = arccos(Rx/R)
Ayr = arccos(Ry/R)
Azr = arccos(Rz/R)
We've gone a long way to explain the accelerometer model, just to come up to these formulas. Depending on your applications you might want to use any intermediate formulas that we have derived. We'll also introduce the gyroscope model soon, and we'll see how accelerometer and gyroscope data can be combined to provide even more accurate inclination estimations.
But before we do that let's do some more useful notations:
cosX = cos(Axr) = Rx / R
cosY = cos(Ayr) = Ry / R
cosZ = cos(Azr) = Rz / R
This triplet is often called Direction Cosine , and it basically represents the unit vector (vector with length 1) that has same direction as our R vector. You can easily verify that:
SQRT(cosX^2 + cosY^2 + cosZ^2) = 1
This is a nice property since it absolve us from monitoring the modulus(length) of R vector. Often times if we're just interested in direction of our inertial vector, it makes sense to normalize it's modulus in order to simplify other calculations.

Part 2. Gyroscope

We're not going to introduce any equivalent box model for the gyroscope like we did for accelerometer, instead we're going to jump straight to the second accelerometer model and we'll show what does the gyroscope measure according to this model.

Each gyroscope channel measures the rotation around one of the axes. For instance a 2-axes gyroscope will measure the rotation around (or some may say "about") the X and Y axes. To express this rotation in numbers let's do some notations. First let's define:
Rxz – is the projection of the inertial force vector R on the XZ plane
Ryz – is the projection of the inertial force vector R on the YZ plane
From the right-angle triangle formed by Rxz and Rz, using Pythagorean theorem we get:
Rxz^2 = Rx^2 + Rz^2 , and similarly:
Ryz^2 = Ry^2 + Rz^2
also note that:
R^2 = Rxz^2 + Ry^2 , this can be derived from Eq.1 and above equations, or it can be derived from right-angle triangle formed by R and Ryz
R^2 = Ryz^2 + Rx^2
We're not going to use these formulas in this article but it is useful to note the relation between all the values in our model.
Instead we're going to define the angle between the Z axis and Rxz, Ryz vectors as follows:
Axz – is the angle between the Rxz (projection of R on XZ plane) and Z axis
Ayz – is the angle between the Ryz (projection of R on YZ plane) and Z axis
Now we're getting closer to what the gyroscope measures. Gyroscope measures the rate of changes of the angles defined above. In other words it will output a value that is linearly related to the rate of change of these angles. To explain this let's assume that we have measured the rotation angle around axis Y (that would be Axz angle) at time t0, and we define it as Axz0, next we measured this angle at a later time t1 and it was Axz1. The rate of change will be calculated as follows:
RateAxz = (Axz1 – Axz0) / (t1 – t0).
If we express Axz in degrees, and time in seconds , then this value will be expressed in deg/s . This is what a gyroscope measures.
In practice a gyroscope(unless it is a special digital gyroscope) will rarely give you a value expressed in deg/s. Same as for accelerometer you'll get an ADC value that you'll need to convert to deg/s using a formula similar to Eq. 2 that we have defined for accelerometer. Let's introduce the ADC to deg/s conversion formula for gyroscope (we assume we're using a 10bit ADC module , for 8bit ADC replace 1023 with 255, for 12bit ADC replace 1023 with 4095).
RateAxz = (AdcGyroXZ * Vref / 1023 – VzeroRate) / Sensitivity Eq.3
RateAyz = (AdcGyroYZ * Vref / 1023 – VzeroRate) / Sensitivity
AdcGyroXZ, AdcGyroYZ – are obtained from our adc module and they represent the channels that measure the rotation of projection of R vector in XZ respectively in YZ planes, which is the equivalent to saying rotation was done around Y and X axes respectively.
Vref – is the ADC reference voltage we'll use 3.3V in the example below
VzeroRate – is the zero-rate voltage, in other words the voltage that the gyroscope outputs when it is not subject to any rotation, for the Acc_Gyro board it is for example 1.23V (you can find this values in the specs – but don't trust the specs most gyros will suffer slight offset after being soldered so measure VzeroRate for each axis output using a voltmeter, usually this value will not change over time once the gyro was soldered, if it variates – write a calibration routine to measure it before device start-up, user must be instructed to keep device in still position upon start-up for gyros to calibrate).
Sensitivity – is the sensitivity of your gyroscope it is expressed in mV / (deg / s) often written as mV/deg/s , it basically tells you how many mV will the gyroscope output increase , if you increase the rotation speed by one deg/s. The sensitivity of Acc_Gyro board is for example 2mV/deg/s or 0.002V/deg/s
Let's take an example, suppose our ADC module returned following values:
AdcGyroXZ = 571
AdcGyroXZ = 323
Using the above formula, and using the specs parameters of Acc_Gyro board we'll get:
RateAxz = (571 * 3.3V / 1023 – 1.23V) / ( 0.002V/deg/s) =~ 306 deg/s
RateAyz = (323 * 3.3V / 1023 – 1.23V) / ( 0.002V/deg/s) =~ -94 deg/s
In other words the device rotates around the Y axis (or we can say it rotates in XZ plane) with a speed of 306 deg/s and around the X axis (or we can say it rotates in YZ plane) with a speed of -94 deg/s. Please note that the negative sign means that the device rotates in the opposite direction from the conventional positive direction. By convention one direction of rotation is positive. A good gyroscope specification sheet will show you which direction is positive, otherwise you'll have to find it by experimenting with the device and noting which direction of rotation results in increasing voltage on the output pin. This is best done using an oscilloscope since as soon as you stop the rotation the voltage will drop back to the zero-rate level. If you're using a multimeter you'd have to maintain a constant rotation rate for at least few seconds and note the voltage during this rotation, then compare it with the zero-rate voltage. If it is greater than the zero-rate voltage it means that direction of rotation is positive.

Putting it all together. Combining accelerometer and gyroscope data.

The first step in using a combination IMU device that combines an accelerometer and a gyroscope is to align their coordinate systems. The easiest way to do it is to choose the coordinate system of accelerometer as your reference coordinate system. Most accelerometer data sheets will display the direction of X,Y,Z axes relative to the image of the physical chip or device. For example here are the directions of X,Y,Z axes as shown in specifications for the Acc_Gyro board:

Next steps are:
– identify the gyroscope outputs that correspond to RateAxz , RateAyz values discussed above.
– determine if these outputs need to be inverted due to physical position of gyroscope relative to the accelerometer
Do not assume that if a gyroscope has an output marked X or Y, it will correspond to any axis in the accelerometer coordinate system, even if this output is part of an IMU unit. The best way is to test it.
Here is a sample sequence to determine which output of gyroscope corresponds to RateAxz value discussed above.
– start from placing the device in horizontal position. Both X and Y outputs of accelerometer would output the zero-g voltage (for example for Acc_Gyro board this is 1.65V)
– next start rotating the device around the Y axis, another way to say it is that you rotate the device in XZ plane, so that X and Z accelerometer outputs change and Y output remains constant.
– while rotating the device at a constant speed note which gyroscope output changes, the other gyroscope outputs should remain constant
– the gyroscope output that changed during the rotation around Y axis (rotation in XZ plane) will provide the input value for AdcGyroXZ, from which we calculate RateAxz
– the final step is to ensure the rotation direction corresponds to our model, in some cases you may have to invert the RateAxz value due to physical position of gyroscope relative to the accelerometer
– perform again the above test, rotating the device around the Y axis, this time monitor the X output of accelerometer (AdcRx in our model). If AdcRx grows (the first 90 degrees of rotation from horizontal position), then AdcGyroXZ should decrease. This is due to the fact that we are monitoring the gravitation vector and when device rotates in one direction the vector will rotate in oposite direction (relative to the device coordonate system, which we are using). So, otherwise you need to invert RateAxz , you can achieve this by introducing a sign factor in Eq.3, as follows:
RateAxz = InvertAxz * (AdcGyroXZ * Vref / 1023 – VzeroRate) / Sensitivity , where InvertAxz is 1 or -1
same test can be done for RateAyz , by rotating the device around the X axis, and you can identify which gyroscope output corresponds to RateAyz, and if it needs to be inverted. Once you have the value for InvertAyz, you should use the following formula to calculate RateAyz:
RateAyz = InvertAyz * (AdcGyroYZ * Vref / 1023 – VzeroRate) / Sensitivity
If you would do these tests on Acc_Gyro board you would get following results:
– the output pin for RateAxz is GX4 and InvertAxz = 1
– the output pin for RateAyz is GY4 and InvertAyz = 1
From this point on we'll consider that you have setup your IMU in such a way that you can calculate correct values for Axr, Ayr, Azr (as defined Part 1. Accelerometer) and RateAxz, RateAyz (as defined in Part 2. Gyroscope). Next we'll analyze the relations between these values that turn out useful in obtaining more accurate estimation of the inclination of the device relative to the ground plane.
You might be asking yourself by this point, if accelerometer model already gave us inclination angles of Axr,Ayr,Azr why would we want to bother with the gyroscope data ? The answer is simple: accelerometer data can't always be trusted 100%. There are several reason, remember that accelerometer measures inertial force, such a force can be caused by gravitation (and ideally only by gravitation), but it might also be caused by acceleration (movement) of the device. As a result even if accelerometer is in a relatively stable state, it is still very sensitive to vibration and mechanical noise in general. This is the main reason why most IMU systems use a gyroscope to smooth out any accelerometer errors. But how is this done ? And is the gyroscope free from noise ?
The gyroscope is not free from noise however because it measures rotation it is less sensitive to linear mechanical movements, the type of noise that accelerometer suffers from, however gyroscopes have other types of problems like for example drift (not coming back to zero-rate value when rotation stops). Nevertheless by averaging data that comes from accelerometer and gyroscope we can obtain a relatively better estimate of current device inclination than we would obtain by using the accelerometer data alone.
In the next steps I will introduce an algorithm that was inspired by some ideas used in Kalman filter, however it is by far more simple and easier to implement on embedded devices. Before that let's see first what we want our algorithm to calculate. Well , it is the direction of gravitation force vector R = [Rx,Ry,Rz] from which we can derive other values like Axr,Ayr,Azr or cosX,cosY,cosZ that will give us an idea about the inclination of our device relative to the ground plane, we discuss the relation between these values in Part 1. One might say – don't we already have these values Rx, Ry , Rz from Eq.2 in Part 1 ? Well yes, but remember that these values are derived from accelerometer data only, so if you would be to use them directly in your application you might get more noise than your application can tolerate. To avoid further confusion let's re-define the accelerometer measurements as follows:
Racc – is the inertial force vector as measured by accelerometer, that consists of following components (projections on X,Y,Z axes):
RxAcc = (AdcRx * Vref / 1023 – VzeroG) / Sensitivity
RyAcc = (AdcRy * Vref / 1023 – VzeroG) / Sensitivity
RzAcc = (AdcRz * Vref / 1023 – VzeroG) / Sensitivity
So far we have a set of measured values that we can obtain purely from accelerometer ADC values. We'll call this set of data a "vector" and we'll use the following notation.
Racc = [RxAcc,RyAcc,RzAcc]
Because these components of Racc can be obtained from accelerometer data , we can consider it an input to our algorithm.
Please note that because Racc measures the gravitation force you'll be correct if you assume that the length of this vector defined as follows is equal or close to 1g.
|Racc| = SQRT(RxAcc^2 +RyAcc^2 + RzAcc^2),
However to be sure it makes sense to update this vector as follows:
Racc(normalized) = [RxAcc/|Racc| , RyAcc/|Racc| , RzAcc/|Racc|].
This will ensure the length of your normalized Racc vector is always 1.
Next we'll introduce a new vector and we'll call it
Rest = [RxEst,RyEst,RzEst]
This will be the output of our algorithm , these are corrected values based on gyroscope data and based on past estimated data.
Here is what our algorithm will do:
– accelerometer tells us: "You are now at position Racc"
– we say "Thank you, but let me check",
– then correct this information with gyroscope data as well as with past Rest data and we output a new estimated vector Rest.
– we consider Rest to be our "best bet" as to the current position of the device.
Let's see how we can make it work.
We'll start our sequence by trusting our accelerometer and assigning:
Rest(0) = Racc(0)
By the way remember Rest and Racc are vectors , so the above equation is just a simple way to write 3 sets of equations, and avoid repetition:
RxEst(0) = RxAcc(0)
RyEst(0) = RyAcc(0)
RzEst(0) = RzAcc(0)
Next we'll do regular measurements at equal time intervals of T seconds, and we'll obtain new measurements that we'll define as Racc(1), Racc(2) , Racc(3) and so on. We'll also issue new estimates at each time intervals Rest(1), Rest(2), Rest(3) and so on.
Suppose we're at step n. We have two known sets of values that we'd like to use:
Rest(n-1) – our previous estimate, with Rest(0) = Racc(0)
Racc(n) – our current accelerometer measurement
Before we can calculate Rest(n) , let's introduce a new measured value, that we can obtain from our gyroscope and a previous estimate.
We'll call it Rgyro , and it is also a vector consisting of 3 components:
Rgyro = [RxGyro,RyGyro,RzGyro]
We'll calculate this vector one component at a time. We'll start with RxGyro.

gyro model

Let's start by observing the following relation in our gyroscope model, from the right-angle triangle formed by Rz and Rxz we can derive that:
tan(Axz) = Rx/Rz => Axz = atan2(Rx,Rz)
Atan2 might be a function you never used before, it is similar to atan, except it returns values in range of (-PI,PI) as opposed to (-PI/2,PI/2) as returned by atan, and it takes 2 arguments instead of one. It allows us to convert the two values of Rx,Rz to angles in the full range of 360 degrees (-PI to PI). You can read more about atan2 here.
So knowing RxEst(n-1) , and RzEst(n-1) we can find:
Axz(n-1) = atan2( RxEst(n-1) , RzEst(n-1) ).
Remember that gyroscope measures the rate of change of the Axz angle. So we can estimate the new angle Axz(n) as follows:
Axz(n) = Axz(n-1) + RateAxz(n) * T
Remember that RateAxz can be obtained from our gyroscope ADC readings. A more precise formula can use an average rotation rate calculated as follows:
RateAxzAvg = ( RateAxz(n) + RateAxz(n-1) ) / 2
Axz(n) = Axz(n-1) + RateAxzAvg * T
The same way we can find:
Ayz(n) = Ayz(n-1) + RateAyz(n) * T
Ok so now we have Axz(n) and Ayz(n). Where do we go from here to deduct RxGyro/RyGyro ? From Eq. 1 we can write the length of vector Rgyro as follows:
|Rgyro| = SQRT(RxGyro^2 + RyGyro^2 + RzGyro^2)
Also because we normalized our Racc vector, we may assume that it's length is 1 and it hasn't changed after the rotation, so it is relatively safe to write:
|Rgyro| = 1
Let's adopt a temporary shorter notation for the calculations below:
x =RxGyro , y=RyGyro, z=RzGyro
Using the relations above we can write:
x = x / 1 = x / SQRT(x^2+y^2+z^2)
Let's divide numerator and denominator of fraction by SQRT(x^2 + z^2)
x = ( x / SQRT(x^2 + z^2) ) / SQRT( (x^2 + y^2 + z^2) / (x^2 + z^2) )
Note that x / SQRT(x^2 + z^2) = sin(Axz), so:
x = sin(Axz) / SQRT (1 + y^2 / (x^2 + z^2) )
Now multiply numerator and denominator of fraction inside SQRT by z^2
x = sin(Axz) / SQRT (1 + y^2 * z ^2 / (z^2 * (x^2 + z^2)) )
Note that z / SQRT(x^2 + z^2) = cos(Axz) and y / z = tan(Ayz), so finally:
x = sin(Axz) / SQRT (1 + cos(Axz)^2 * tan(Ayz)^2 )
Going back to our notation we get:
RxGyro = sin(Axz(n)) / SQRT (1 + cos(Axz(n))^2 * tan(Ayz(n))^2 )
same way we find that
RyGyro = sin(Ayz(n)) / SQRT (1 + cos(Ayz(n))^2 * tan(Axz(n))^2 )
Side Note: it is possible to further simplify this formula. By dividing both parts of the fraction by sin(Axz(n)) you get:
RxGyro = 1 / SQRT (1/ sin(Axz(n))^2 + cos(Axz(n))^2 / sin(Axz(n))^2 * tan(Ayz(n))^2 )
RxGyro = 1 / SQRT (1/ sin(Axz(n))^2 + cot(Axz(n))^2 * sin(Ayz(n))^2 / cos(Ayz(n))^2 )
now add and substract     cos(Axz(n))^2/sin(Axz(n))^2   = cot(Axz(n))^2
RxGyro = 1 / SQRT (1/ sin(Axz(n))^2 – cos(Axz(n))^2/sin(Axz(n))^2 + cot(Axz(n))^2 * sin(Ayz(n))^2 / cos(Ayz(n))^2 + cot(Axz(n))^2 )
and by grouping terms 1&2 and then 3&4 we get
RxGyro = 1 / SQRT (1 + cot(Axz(n))^2 * sec(Ayz(n))^2 ),    where cot(x) = 1 / tan(x) and sec(x) = 1 / cos(x)
This formula uses only 2 trigonometric functions and can be computationally less expensive. If you have Mathematica program you can verify it
by evaluating    FullSimplify [Sin[A]^2/ ( 1 + Cos[A]^2 * Tan[B]^2)]

Now, finally we can find:
RzGyro = Sign(RzGyro)*SQRT(1 – RxGyro^2 – RyGyro^2).
Where Sign(RzGyro) = 1 when RzGyro>=0 , and Sign(RzGyro) = -1 when RzGyro<0.
One simple way to estimate this is to take:
Sign(RzGyro) = Sign(RzEst(n-1))
In practice be careful when RzEst(n-1) is close to 0. You may skip the gyro phase altogether in this case and assign: Rgyro = Rest(n-1). Rz is used as a reference for calculating Axz and Ayz angles and when it's close to 0, values may overflow and trigger bad results. You'll be in domain of large floating point numbers where tan() / atan() function implementations may lack precision.

So let's recap what we have so far, we are at step n of our algorithm and we have calculated the following values:
Racc – current readings from our accelerometer
Rgyro – obtained from Rest(n-1) and current gyroscope readings
Which values do we use to calculate the updated estimate Rest(n) ? You probably guessed that we'll use both. We'll use a weighted average, so that:
Rest(n) = (Racc * w1 + Rgyro * w2 ) / (w1 + w2)
We can simplify this formula by dividing both numerator and denominator of the fraction by w1.
Rest(n) = (Racc * w1/w1 + Rgyro * w2/w1 ) / (w1/w1 + w2/w1)
and after substituting w2/w1 = wGyro we get:
Rest(n) = (Racc + Rgyro * wGyro ) / (1 + wGyro)
In the above formula wGyro tells us how much we trust our gyro compared to our accelerometer. This value can be chosen experimentally usually values between 5..20 will trigger good results.
The main difference of this algorithm from Kalman filter is that this weight is relatively fixed , whereas in Kalman filter the weights are permanently updated based on the measured noise of the accelerometer readings. Kalman filter is focused at giving you "the best" theoretical results, whereas this algorithm can give you results "good enough" for your practical application. You can implement an algorithm that adjusts wGyro depending on some noise factors that you measure, but fixed values will work well for most applications.
We are one step away from getting our updated estimated values:
RxEst(n) = (RxAcc + RxGyro * wGyro ) / (1 + wGyro)
RyEst(n) = (RyAcc + RyGyro * wGyro ) / (1 + wGyro)
RzEst(n) = (RzAcc + RzGyro * wGyro ) / (1 + wGyro)
Now let's normalize this vector again:
R = SQRT(RxEst(n) ^2 + RyEst(n)^2 + RzEst(n)^2 )
RxEst(n) = RxEst(n)/R
RyEst(n) = RyEst(n)/R
RzEst(n) = RzEst(n)/R
And we're ready to repeat our loop again.

____________ X applicate

Desk mount tablet arm for YouTube videos

Desk mount tablet arm for YouTube videos

This makes it easy to adjust the position of the camera while recording, while simultaneously being able to see if the subject is properly framed. Because the arm takes up little space, it doesn’t interfere with the project or my movement. I’ve even considered mounting it from overhead for additional freedom. The mounting bracket screws on and off the table quickly.
Room lighting can pour in from all directions. Because the camera is in the far corner of the iPad, the tablet body does not block three-quarters of the overhead lighting.
The tablet can be tilted to take a video of the computer monitor, or it can be used for its intended purpose as an ordinary tablet when I’m not recording.
I looked at many other swing arms / desk mounts, but I was disappointed in how flimsy they looked. For those products, reviewers complained about sagging or difficulty adjusting the position. The LapWorks mount is solidly built and the motions are smooth.

A terrain robo platform must be able to handle different environments and be able to go over obstacles.

_________ XBook

practical questions, such as:
– given a certain electrical setup that can generate a certain power, what is the maximum thrust that we can achieve ?
– can a human-powered aircraft be built ?
– can I tell the expected thrust generated by a copter simply by knowing the power it consumes and vice-versa ?
– are larger propellers really more “efficient” ?
– which multicopter configuration is more efficient: tricopter , quadcopter , hexacopter or octacopter ?
– I heard that for every gram of mass I add to my multicopter, the flight time will decrease by 1 second, can you put any sense in this ?
– and many more…
When selecting motors for an RC model you will often come across the thrust tables that tell you how much thrust (or “pull”) a specific motor can generate. This usually depends on few factors: the propeller used and the voltage applied. Next to these two parameters you will usually see the current and power consumed and the resulting thrust generated, usually expressed in grams. If you want your model to hover, you must ensure that your motors can generate a thrust equal to or greater than the mass of the model.
The power that your motor consumes is calculated based on the simple formula:
P = V * I , where V is the voltage and I is the current.
Power represents the amount of energy we’re consuming per second, which is expressed as follows:
P = E / t, where E is the energy.
The efficiency of a specific setup consisting of a propeller and a motor is often calculated as:
Eff = Thr / P , where Thr is the thrust force generated expressed in grams and P is the power consumed expressed in Watts.
However this efficiency is not expressed in percentage as you would usually expect so it does not tell us if our motor-propeller setup is consuming energy exclusively to generate lift or wasting it somewhere else. To compute an efficiency value in percentage we would need to know the minimum or “ideal” power necessary to generate a specific thrust given our setup and environment.
We have to mention from the start that such an “ideal” model, taking into account all the possible factors would probably be too complex to use for practical purposes. So instead our goal is to first deduct and then define a “theoretical” or “reference” power formula. Finally this formula will be applied to a series of practical experiments and the resulting “theoretical” power will be compared to the real-world observed power. To avoid confusion with the other efficiency let’s call this ratio “Theoretical Thrust Ratio” or TTR:
TTR = P(real) / P(theoretical)
So let’s dive into our calculations. But before we do that, for the impatient reader, here are the formulas that we’ll be deducting and using for computing our “theoretical” power (or thrust – if we flip the formula around):

(Formula 1.1)

(Formula 1.2)

(Formula 1.3)

Here P is the power expressed in Watts., F is the thrust expressed in Newtons, r is the radius of the propeller expressed in meters and K is a air density dependent coefficient, with a nominal value of 0.3636 assuming air pressure of 1atm and air temperature of 20C.
The formulas above are expressed in international physical units, however for those doing RC I am also providing the following convenient formula for computing thrust in grams Tgram, based on a propeller diameter expressed in inches Dinch:

(Formula 2.1)

(Formula 2.2)

(Formula 2.3)

Here C is an air density dependent coefficient with a nominal value of 0.0278 assuming air pressure of 1atm and air temperature of 20C.
Below is a lookup table for K and C coefficients at various temperatures and air pressure of 1atm.

Temperature (degrees C)	Air Density (kg/m^3) @ 1atm	K	C
35	1.1455	0.372745594	0.02850295
30	1.1644	0.369708101	0.02827068
25	1.1839	0.366650731	0.028036891
20	1.2041	0.363562254	0.027800722
15	1.225	0.360447503	0.027562545
10	1.2466	0.357311097	0.027322712
5	1.269	0.354143483	0.027080492
0	1.2922	0.35094996	0.026836291
−5	1.3163	0.347722365	0.026589484
−10	1.3413	0.344466588	0.026340523
−15	1.3673	0.341175753	0.026088881
−20	1.3943	0.337856246	0.025835046
−25	1.4224	0.334502366	0.025578583

The Deduction

Let’s get back to the main question of this article – “how much power is needed to hover an object ?” , or in other words how much energy we need to consume per second to maintain this object “in the air”. Well, as it turns out “it all depends…”. For example we do not need to consume any energy to keep a book on a shelf or a tire suspended on a rope. Clearly we did not formulated our question correctly! So let’s try again and this time be more specific. “How much power does a craft powered by a spinning propeller needs in order to hover in the air ?” Here you go – much better ! Let’s see if we can answer this question.
You might recall from school physics a rule called “preservation of energy”, it simply states that the energy does not like to go away or disappear, all it does – it transforms from one form to another. We certainly know that our craft consumes energy in the form of electricity or burned fuel, but where does it go ? Didn’t we say that we don’t need energy to hover an object such as a book on a shelf or a tire hanging on a rope ? The reality is that not every object is as lucky as a book on a shelf or a tire suspended on a rope. In both these cases – the book and the tire have the benefit of a “reaction force” of the shelf and rope respectively. The book pushes on the shelf with its weight and the shelf will always push back with an equal force in the opposite direction. If you’re confused it’s probably because of a common misconception that an existence of a “force” implies that energy is consumed. The later is not true – a “force” can exist simply because of the setup of the nature, for example the gravitational force, the magnetic force and the reactive force they all simply exists without consuming energy. It’s their action that might or might not lead to the transfer of energy.
Back to our model of a propeller-powered craft, since our craft is suspended in the air, there must clearly exist an opposing force F that is directed in a opposite direction:
F = m * g , where m is the mass of our craft and g is the acceleration of gravity ( approximately 9.8 m / s^2 ).
In fact this force is not much different from the “reaction force” that a shelf exerts on the book, except our medium “the air” is less dense than the shelf and as a result of this force, as we push “against” it with the propeller, the air starts moving. Here you go! We found were our energy goes. The electrical or fuel energy consumed by an aircraft is transferred to the kinetic energy of the moving air.
Let’s imagine a spinning rotor that generates a thrust F (in case of hover it is equal to mg) and a cylinder of air of infinite height with the rotor placed in the middle. The rotor is sucking air from the top and then pushing it down from the bottom. The rotor will have very little influence on the air on the top (since it’s very-very far away) so we can assume the speed of the air starts at 0, then slowly increases as it passes the rotor to w/2 (due to symmetry of our model from top and bottom) and finally as it reaches the bottom of the cylinder it stabilizes at a speed w, again rotor has very small influence on the air from the bottom of cylinder since it’s far-far away.

Let’s examine our model for a period of time t that it takes for a particle of air to travel from the top to bottom of cylinder . Let’s note the area of the cross-section of our cylinder as A and since it’s defined by the radius of our rotor we have:

The volume of air Vol that passes through the rotor during the time t, can be calculated by monitoring how much “height” of air passes through the rotor at the speed v, h=v*t and multiplying by the cross section area A.

Given the air density Qair we can calculate the mass of air that passes through the rotor in the time period t:

Let’s note that since the air starts at zero speed so its starting momentum is zero, thus it’s momentum change will be equal to final momentum Mair * w, where w is the final speed. From momentum conservation theory we can recall that the force that we apply on a mass is equal to the rate of change of momentum:
clip_image002[19]

Finally combining the last two formulas we get:
clip_image002[21]

From here we can get the speed v at the rotor:

clip_image002[23]

Next let’s note that the power consumed is equal to the work done by the force F at the rotor (work and power are both defined by the rate of change of energy), which is given by another classical formula W=F*v:

clip_image002[31]

Note that we substituted the circle area A = PI*r^2 formula. Finally, by extracting some coefficients we get:

clip_image002[35]

and the inverse formula for thrust is:
clip_image002[51]

The formulas above work with international physics units, thrust F is measured in Newtons, and radius r is measured in meters. For practical purposes in RC we work with propeller diameter in inches and thrust is calculated in grams (which is equivalent to mass this thrust force can hover from F = mg formula, where g is standard gravity ~ 9.8m/s^2). So let’s define another equivalent set of formulas:

and the inverse formula is

clip_image002[53]

Experimental Results

To verify these theoretical results I conducted a series of thrust tests using different motors and propellers commonly used in RC. Also I verified thrust tables randomly found on the internet (forums or motor specs):

Motor: DYS 1306 3100KV BX :

Prop Diam "

	Prop Ptich	V	A	Thrust(g)	W	Eff (g/W)	Theoretical Thrust (g)	TTR=Thrust /Theoretical Thrust
5	3	7.4	1.4	70	10.36	6.8	151	46.2%
5	3	7.4	3.3	140	24.42	5.7	268	52.2%
5	3	7.4	5.7	220	42.18	5.2	386	57.0%
5	3	11.1	2.1	100	23.31	4.3	260	38.5%
5	3	11.1	5.5	210	61.05	3.4	494	42.5%
5	3	11.1	9.4	350	104.34	3.4	706	49.6%
6	2	7.4	1.3	70	9.62	7.3	163	43.0%
6	2	7.4	3.7	150	27.38	5.5	327	45.9%
6	2	7.4	7.9	230	58.46	3.9	542	42.4%

Motor: EMAX MT1804 2480KV

Prop Diam "

	Prop Ptich	V	A	Thrust(g)	W	Eff (g/W)	Theoretical Thrust (g)	TTR=Thrust /Theoretical Thrust
5	3	12.1	7.3	345	88.33	3.9	632	54.6%
5	3	12.2	6.2	330	75.64	4.4	570	57.9%
5	4	12.1	9	370	108.9	3.4	727	50.9%
6	3	12.2	8.6	410	104.92	3.9	800	51.2%

Motor: DYS BE1806 2300KV

Prop Diam "

	Prop Ptich	V	A	Thrust(g)	W	Eff (g/W)	Theoretical Thrust (g)	TTR=Thrust /Theoretical Thrust
5	3	12	9	415	108	3.8	723	57.4%
5	3	12.2	3.6	230	43.92	5.2	397	58.0%
5	3	12.1	7.5	360	90.75	4.0	643	55.9%
5	3	12.2	3.3	205	40.26	5.1	374	54.8%
5	4	12	10.6	440	127.2	3.5	806	54.6%
5	4	12.1	4.2	225	50.82	4.4	437	51.5%
6	3	12	10.2	485	122.4	4.0	887	54.7%
6	3	12.1	4	240	48.4	5.0	478	50.2%
6	4.5	11.8	16.1	580	189.98	3.1	1189	48.8%
6	4.5	12	5.1	235	61.2	3.8	559	42.1%
5	3	16.3	11.5	570	187.45	3.0	1044	54.6%
5	3	16.4	5.1	280	83.64	3.3	609	45.9%

Example Uses

Q: Can I build a human powered helicopter ?

A: A trained cyclist can generate 500W peak power (or even 1500W according to some reports), let’s a assume the cyclist weights 70Kg and the weight of the copter is 30Kg, thus the thrust needed for lift off is 100Kg. From this formula:

we can compute the radius of the propeller that this human-powered helicopter will need to have :

r = K * (mg) ^ (3/2) / P = 0.3636 * (100*9.8) ^ (3/2) / 500 = 22.3 (meters)
In fact human powered machines have been built. Let’s analyze for example the Gamera II built by University of Maryland:
Weight: ~ 37kg + Pilot ~ 90kg
Rotors Radius: ~ 7.2m ( x 4)
Since this craft has 4 rotors , each rotor would need to produce at least 1/4 of the thrust or 90Kg / 4 = 22.5Kg . Using our formula we can calculate power needed for each rotor:

P = K * (mg) ^ (3/2) / r = 0.3636 * (22.5*9.8) ^ (3/2) / 7.2 = 165.35

Thus the total power required is estimated at 165.35W*4 = 661.4 W , which is quite manageable for a trained cyclist. In reality no motor is 100% efficient. Still, an advantageous effect for take-off is the “ground effect”. As air is pushed into the ground its density increases, as a result our K coefficient will decrease, meaning there’s less power required to generate same thrust compared to less dense air.

Q: I am buying a motor that is rated at 200W, assuming I will use a battery that has a voltage high enough to result in all 200W being drawn from the motor and a 10” prop, how much thrust can I expect from this motor and what will be the gram/Watt efficiency ?

A: Simply apply this formula to get the thrust in grams:

Tgrams = ( P * Dinch / C) ^ (2/3) = ( 200 * 10 / 0.0278) ^ (2/3) =~ 1730 gram, gram/Watt efficiency immediately follows: Eff = 1730 / 200 = 8.65. From practice, knowing that most brushless motors setup used in RC will deliver 50-60% of theoretical thrust and efficiency you can divide the above theoretical values by 2 to get a real-life estimate , so P ~ 865g, Eff ~ 4.3 g/ W.

below is the picture of the machine that I called “QuadHybrid”:

the conclusion was that a suitable flying robo platform must not rely too much (or at all) on its sensors to be stable while hovering. It must be mechanically symmetric, preferably with it’s center of mass in the center. At the same time it must have the maneuverability capabilities of a quadcopter – and namely I wanted it to fly sideways, forward & back and rotate around its axis. Although the solution I came up with might seem to some (including myself) a little strange and out of the ordinary – as turned out it flew surprisingly well , was very stable and maneuverable, and best of all –

QuadHybrid_Origins

A 3 channel helicopter as we know it – only has 3 controls (throttle, yaw and pitch). The problem with a co-axial 4CH helicopter for instance is that it relies on a swash plate and servos to achieve the pitch/roll control. Basically it is a servo mechanism for tilting the axis of rotation of the bottom rotor – it is not easy to fine tune and it needs frequent maintenance.

eh-ek-e303ra-7_3

the actually used all electronics in this 4CH helicopter, except the servos (as will become clearer in the next diagram):

lama_v3_electronics

the swashplate on the bottom blade was replaced with a fixed blade mount from a 3CH helicopter. This made the bottom blade of the 4CH fixed. Normally on a 4CH heli it is being tilted by servos that are connected to the swashplate using 2 links.

Main Rotor Head

This is a swashplate assembly on a 4Ch helicopter it is the black disk under the blades mount. It is connected to the blade mount with two links. Two servos that are controlled by the RC receiver are able to tilt the swashplate and thus alter the axis of rotation of the bottom blade. This is how the roll and pitch is controlled in a 4 channel helicopter. It is far from perfect and requires frequent tune-ups.

A gyro-stabilized co-axial 3 channel toy helicopter is fairly stable, as opposed to a 4CH helicopter- it uses the tail rotor to control the pitch (flying forward or backward). Note the bottom blade is fixed , just like on the QuadHybrid.

At the heart of the QuadHybrid V1 controller board is a PIC16F1825 microcontroller – it’s main task is to capture the servo signals received on ports RA5(ROLL) and RA4(PITCH) and convert them to 4 PWM signals that control the 4 rotors mounted on arms. The motors are driven using 4 NPN transistors – these can be any NPN transistors as long as they have a current rating of at least 1A (or the maximum current of your arm-mounted motors) and a low Collector-Emitter resistance (< 0.3Ohm or so). MOSFET could be used instead of NPN transistors, with slight schematic change – however I found hard to source them in an easy to solder and light-weight TO-92 package so I used NPNs instead.
QuadHybrid V1 controller board operates by default in “+” configuration. If you want to operate it in “X” configuration then connect the X_CONF jumper – this will give you better control with smaller motors , since for example flying forward will activate two motors at once (3 & 2). The only thing to keep in mind when flying in “X” configuration is that the “Front” is a imaginary line between arm #0 and #1 .

PCB was created as a all through-hole design so that anyone can assemble it and covert a toy RC helicopter into a QuadHybrid:

http://uploads.oshpark.com/uploads/project/top_image/BaiOVPXQ/thumb_i.png

Mechanical Construction

On top of QuadHybrid prototype is basically a stripped down frame of a 3ch helicopter

Under the frame are 2 aluminum tubing pieces that form the 4 arms. At the end of each arm I attached a tail rotor from the same helicopter model:

Then, to house all the electronics and battery I just sandwiched two perforated boards with nylon spacers (Tip: most bolts,nuts and spacers used in this design are nylon-made to lower the weight) :

For landing gear I just used a RC helicopter training kit that consist of a round bracket attached to the bottom deck, four carbon fiber rods and four (orange) ping-pong balls:

although all the mechanical parts are from a 3Channel helicopter , you will need electronics from a 4Ch helicopter! The main reason for using a 3CH helicopter for mechanical part is because the bottom blade bracket was already fixed (3Ch helicopters have no swashplate):

It might be more economical to get a 4Ch helicopter (mainly for its electronics that support pitch & roll not just pitch as a 3Ch helicopter) and then just remove the servos and the swashplate and then fix the bottom blade bracket using screws and/or glue.

As an alternative to fixing the bottom blade bracket, you can also get a fixed blade bracket from a 3CH helicopter – it must be same inner diameter as the one from your 4CH helicopter. The diameter of the outer drive shafts are typically 4mm ( and 5mm , 6mm and even 8mm for larger helicopters).

What are The Advantages and Disadvantages of the QuadHybrid Design over a traditional QuadCopter or a RC Helicopter

While building the QuadHybrid I selected the best features of a quadcopter and a helicopter and put them together, this resulted in a machine that took the best of the two worlds:
– QuadHybrid is mechanically symmetrical and stable. As opposed to a QuadCopter it does not rely on sensor to achieve this, the main hovering stability comes from the co-axial design and the centrifugal momentum of the fly-bar (the black bar on top of the blades with two weights on each side) – this is basically the same principle at work as in a mechanical gyroscope.
– QuadHybrid only requires two lifting “powerful” motors, the other 4 small motors are used for steering and are not crucial for device stability. There’s no need for motors to be brushless, and no need for brushless ESC controllers, they can be cheap RC-type brushed motors that can be driven by MOSFETs or transistors, there’s no need to reverse rotation of any motors, however it will be a feature in V2 of the controller board. Also there’s no need for CW and CCW propellers for the motors mounted on arms. All this vastly reduces the complexity and cost of the design.
– Partial or complete failure of any motor will still keep device in the air and give the operator/program a good chance to land it safely. In the worst case scenario – if a driving motor fails , the device will loose lifting power and will spin in the air, but will still at least stay on it’s vertical axis. In case of a quadcopter – failure of any motor is fatal – it will basically flip the quadcopter, or make it fly sideways with great chance to hit something. This is of course debatable, but in any case statistics tells us that the probability of one of the 4 motors to fail is twice of that of one of the two motors to fail (and economics tells us it’s also cheaper to get two motors than four). Failure of any arm-mounted motor on a QuadHybrid is not critical, the device will still hover in the air and can be landed safely.
– Safety (we all know it should be first). A QuadCopter without shielded high-powered rotors is a pretty dangerous device, its propellers are spinning with an incredible force and are ready to cut into anything that falls in their way – they can do a lot of damage to humans, pets or property. QuadHybrid only has four low-powered motors on its arms since they are only used for steering, not lifting. The center driving propellers on a QuadHybrid are shielded away by its arms, also they have a folding design, so if they hit something it will somewhat make the impact less destructive. Overall QuadHybrid is more suitable for indoor applications such as schools , universities or research facilities. Make no mistake though – QuadHybrid can still do damage and safety procedures must he observed at all times !
Few advantages of Quadcopters over QuadHybrid worth mentioning may be:
– because they have four powerful motors, Quadcopters have greater lifting power. However this is more of a “brute-force” advantage – one could always get more powerful motors and battery for a QuadHybrid to achieve the desired lift power.
– Quadcopters can do some serious aerobatics (if you’re a good pilot) including fly upside down or do 3D stunts. This is not really an advantage for some people, specifically when you are actually trying to build a nice and stable robotic platform – on the contrary it’s more something you try to avoid.
In conclusion: although this first version of QuadHybrid was not designed to do 3D stunts or fly with high speeds – it has a great hovering and maneuvering stability. I think it will find most of its use as a highly stable and controllable indoor flying robotic platform. However similar spin-offs could be expected to be developed for flying outdoors and/or with higher speeds.

the name of Sky "O" Droner ___ Xsimple

Teleradio is a global technology leader that specializes in the developing and manufacturing of advanced on blocking for bringing systems.

Sky "O" Droner is a fast and effective blocking for bringing systems. surveillance system designed to Detect, Distract and Disable any anonymous drone from flying into a protected area. It consists of multiple sensors to monitor the range of radio signals and identify the different characteristics of a drone's signature. Sky " O " Droner can distract the drone by taking over the command and control frequencies. During an emergency, it is capable of immobilizing the drone to prevent it from further intrusion.