I am making an automated quadcopter: no radio transmitter-receiver and the quacopter flies on its own with pre-programmed orders.
All most all quadcopter implement PID on throttle/yaw/pitch/roll as these 4 axes are directly on an remote controller. However this is a bit inconvenient for an automated one without a controller. As an automated quadcopter without user input, velocities along x/y/z axis are of more concern, because:
keeping balance(yaw/pitch/roll=0) doesn't mean keeping still, as first, there will be some error in manufacture so it might still have some acceleration. Second, even there's no acceleration, it can have speed, causing it to drift in space. And as there's no user input, it can not fix the drift on it's own. Besides, if there's wind, it might got blow away, even it thinks it's balanced.
Orders are mostly given in "go to position(x,y)" or "keeping velocity x, fly above position(x,y) and start camera video capture." or so. These orders can't be translated into yaw/pitch/roll directly.
So basically I have two ideas:
Implement PID on yaw/pitch/roll/height and use a second PID loop to control velocity. The second PID loop take desired velocity and current velocity as input and output desired yaw/pitch/roll for first loop.
Implement PID directly on velocity. The pid loop take desired velocity and current velocity(by integrating acceleration from accelerometer) as input and output PWM width for 4 motors.
Have anyone tried idea 2? Will this work?
PID maps a measured value to a controlled value. If you can sense velocity reliably, you can use it to drive a PID. However, integrating an accelerometer won't give you a reliable enough velocity. Any sensing errors will compound through the integration and could grow your velocity estimate unbounded.
The R/P/Y PIDs on a quadcopter don't control the PWM to the motors directly, they control the roll, pitch, and yaw rates, then convert the rates to thrusts, then convert the thrust components for the various motors, then convert the thrusts into PWMs. See ArduPilor MotorMatrix code for hints.
You can put PIDs anywhere you like in the process between whatever is choosing the velocity and the motors, but you likely need some intermediate control/state variables between 'velocity' and 'PWM[1-4]' to balance things and have coordinated flight.
I want to develop an app that detects how far the user/device is from points on a map.
Calculating the distance is easy, but when you get close to about 30meters I would need it to be as precise as possible.
Basically I want some lights on the UI to get brighter the closer you get to the target/point.
How do I achieve this if the gps position sometimes bounces around for 5-10 meters or more?
Any ideas on how to approach this?
Thanks!
In general there is the inaccuracy with the position, and indeed its meters, thus the bouncing will be there and its rather impossible to get rid of it, anyways, one suggestion would be to collect the last few (3-10 up to you and your logic really) locations and calculate average from them. Then with fast movements your position would be lagging of course, but when doing slow movements the position shown should be more stable.. Of course you could also have additional logic on determining the movement direction, and accepting the location change towards that faster etc.
You will not get a better precision than 3m to the target.
At low, speed, like walking, you will no make it better than 8-10m.
Count the distance sicne last used fix, If it exceeds 12m then use the fix, and mark it as last used.
This is a simple filter which works well for walking speeds.
At speeds higher (> 10km/h) switch off the filter.
GPS should not jump at that speed.
I have created a very simple numerical simulation that models an object being thrown off a building at some angle, and when the object hits the ground, the simulation stops. Now I want to add in collision detection. How would I go about doing this?
I know I need to find the exact time that the object (a ball) hits the ground, as well as the velocity in the x and y direction, and position of the object when it hits the ground, and I have to add in parameters that say how much the ball will bounce on impact. But I don't know how to go about doing this. I know that there are various ways of detecting collision but since I am new to this, the most comprehensible method would be best.
Make a coordinate system, with the ground at y=0. Track the coordinates of the ball as it flies and then check when it has y=0, and that's where it hits the ground. You can also keep track of the x and y velocity as the ball is moving.
Use Physics skillz. This is a good tutorial. If you have it, I recommend Fundamentals of Physics by Halliday, Resnick and Walker. They have a very good chapter on this.
If you are just looking for the math, that you could write C code for. I found this one helpful. Math Models
Collision detection simply involves determining the distance between 2 objects.
If you are only interested in collisions between objects and the ground, you can use:
if(object.y <= ground.y) {
//collision occurred
}
To do collisions between objects, you can loop through all objects and compare them to each other in the same way.
The sensor module in my project consists of a rotating camera, that collects noisy information about moving objects in the surrounding environment.
The information consists of distance, angle and relative change of the moving objects..
The limiting view range of the camera makes it essential to rotate the camera periodically to update environment information...
I was looking for algorithms / ways to model these information, in order to be able to guess / predict / learn motion properties of these object..
My current proposed idea is to store last n snapshots of each object in a queue. I take weighted average of positions and velocities of moving object, but I think it is a poor method...
Can you state some titles that suit this case?
Thanks
Kalman {Extended, unscented, ... } filters and particle filters only after reading about Kalman filters.
Kalman filters learn and predict the correct data from noisy data with a Gaussian assumption, so it may be of use to you. If you need non-Gaussian methods, look at the particle filter.
Not unlike a clap detector ("Clap on! clap clap Clap off! clap clap Clap on, clap off, the Clapper! clap clap ") I need to detect when a door closes. This is in a vehicle, which is easier than a room or household door:
Listen: http://ubasics.com/so/van_driver_door_closing.wav
Look:
It's sampling at 16bits 4khz, and I'd like to avoid lots of processing or storage of samples.
When you look at it in audacity or another waveform tool it's quite distinctive, and almost always clips due to the increase in sound pressure in the vehicle - even when the windows and other doors are open:
Listen: http://ubasics.com/so/van_driverdoorclosing_slidingdoorsopen_windowsopen_engineon.wav
Look:
I expect there's a relatively simple algorithm that would take readings at 4kHz, 8 bits, and keep track of the 'steady state'. When the algorithm detects a significant increase in the sound level it would mark the spot.
What are your thoughts?
How would you detect this event?
Are there code examples of sound pressure level calculations that might help?
Can I get away with less frequent sampling (1kHz or even slower?)
Update: Playing with Octave (open source numerical analysis - similar to Matlab) and seeing if the root mean square will give me what I need (which results in something very similar to the SPL)
Update2: Computing the RMS finds the door close easily in the simple case:
Now I just need to look at the difficult cases (radio on, heat/air on high, etc). The CFAR looks really interesting - I know I'm going to have to use an adaptive algorithm, and CFAR certainly fits the bill.
-Adam
Looking at the screenshots of the source audio files, one simple way to detect a change in sound level would be to do a numerical integration of the samples to find out the "energy" of the wave at a specific time.
A rough algorithm would be:
Divide the samples up into sections
Calculate the energy of each section
Take the ratio of the energies between the previous window and the current window
If the ratio exceeds some threshold, determine that there was a sudden loud noise.
Pseudocode
samples = load_audio_samples() // Array containing audio samples
WINDOW_SIZE = 1000 // Sample window of 1000 samples (example)
for (i = 0; i < samples.length; i += WINDOW_SIZE):
// Perform a numerical integration of the current window using simple
// addition of current sample to a sum.
for (j = 0; j < WINDOW_SIZE; j++):
energy += samples[i+j]
// Take ratio of energies of last window and current window, and see
// if there is a big difference in the energies. If so, there is a
// sudden loud noise.
if (energy / last_energy > THRESHOLD):
sudden_sound_detected()
last_energy = energy
energy = 0;
I should add a disclaimer that I haven't tried this.
This way should be possible to be performed without having the samples all recorded first. As long as there is buffer of some length (WINDOW_SIZE in the example), a numerical integration can be performed to calculate the energy of the section of sound. This does mean however, that there will be a delay in the processing, dependent on the length of the WINDOW_SIZE. Determining a good length for a section of sound is another concern.
How to Split into Sections
In the first audio file, it appears that the duration of the sound of the door closing is 0.25 seconds, so the window used for numerical integration should probably be at most half of that, or even more like a tenth, so the difference between the silence and sudden sound can be noticed, even if the window is overlapping between the silent section and the noise section.
For example, if the integration window was 0.5 seconds, and the first window was covering the 0.25 seconds of silence and 0.25 seconds of door closing, and the second window was covering 0.25 seconds of door closing and 0.25 seconds of silence, it may appear that the two sections of sound has the same level of noise, therefore, not triggering the sound detection. I imagine having a short window would alleviate this problem somewhat.
However, having a window that is too short will mean that the rise in the sound may not fully fit into one window, and it may apppear that there is little difference in energy between the adjacent sections, which can cause the sound to be missed.
I believe the WINDOW_SIZE and THRESHOLD are both going to have to be determined empirically for the sound which is going to be detected.
For the sake of determining how many samples that this algorithm will need to keep in memory, let's say, the WINDOW_SIZE is 1/10 of the sound of the door closing, which is about 0.025 second. At a sampling rate of 4 kHz, that is 100 samples. That seems to be not too much of a memory requirement. Using 16-bit samples that's 200 bytes.
Advantages / Disadvantages
The advantage of this method is that processing can be performed with simple integer arithmetic if the source audio is fed in as integers. The catch is, as mentioned already, that real-time processing will have a delay, depending on the size of the section that is integrated.
There are a couple of problems that I can think of to this approach:
If the background noise is too loud, the difference in energy between the background noise and the door closing will not be easily distinguished, and it may not be able to detect the door closing.
Any abrupt noise, such as a clap, could be regarded as the door is closing.
Perhaps, combining the suggestions in the other answers, such as trying to analyze the frequency signature of the door closing using Fourier analysis, which would require more processing but would make it less prone to error.
It's probably going to take some experimentation before finding a way to solve this problem.
You should tap in to the door close switches in the car.
Trying to do this with sound analysis is overengineering.
There are a lot of suggestions about different signal processing
approaches to take, but really, by the time you learn about detection
theory, build an embedded signal processing board, learn the processing
architecture for the chip you chose, attempt an algorithm, debug it, and then
tune it for the car you want to use it on (and then re-tune and re-debug
it for every other car), you will be wishing you just stickey taped a reed
switch inside the car and hotglued a magnet to the door.
Not that it's not an interesting problem to solve for the dsp experts,
but from the way you're asking this question, it's clear that sound
processing isn't the route you want to take. It will just be such a nightmare
to make it work right.
Also, the clapper is just an high pass filter fed into a threshold detector. (plus a timer to make sure 2 claps quickly enough together)
There is a lot of relevant literature on this problem in the radar world (it's called detection theory).
You might have a look at "cell averaging CFAR" (constant false alarm rate) detection. Wikipedia has a little bit here. Your idea is very similar to this, and it should work! :)
Good luck!
I would start by looking at the spectral. I did this on the two audio files you gave, and there does seem to be some similarity you could use. For example the main difference between the two seems to be around 40-50Hz. My .02.
UPDATE
I had another idea after posting this. If you can, add an accelerometer onto the device. Then correlate the vibrational and acoustic signals. This should help with cross vehicle door detection. I'm thinking it should be well correlated since the sound is vibrationally driven, wheres the stereo for example, is not. I've had a device that was able to detect my engine rpm with a windshield mount (suction cup), so the sensitivity might be there. (I make no promises this works!)
(source: charlesrcook.com)
%% Test Script (Matlab)
clear
hold all %keep plots open
dt=.001
%% Van driver door
data = wavread('van_driver_door_closing.wav');
%Frequency analysis
NFFT = 2^nextpow2(length(data));
Y = fft(data(:,2), NFFT)/length(data);
freq = (1/dt)/2*linspace(0,1,NFFT/2);
spectral = [freq' 2*abs(Y(1:NFFT/2))];
plot(spectral(:,1),spectral(:,2))
%% Repeat for van sliding door
data = wavread('van_driverdoorclosing.wav');
%Frequency analysis
NFFT = 2^nextpow2(length(data));
Y = fft(data(:,2), NFFT)/length(data);
freq = (1/dt)/2*linspace(0,1,NFFT/2);
spectral = [freq' 2*abs(Y(1:NFFT/2))];
plot(spectral(:,1),spectral(:,2))
The process for finding distinct spike in audio signals is called transient detection. Applications like Sony's Acid and Ableton Live use transient detection to find the beats in music for doing beat matching.
The distinct spike you see in the waveform above is called a transient, and there are several good algorithms for detecting it. The paper Transient detection and classification in energy matters describes 3 methods for doing this.
I would imagine that the frequency and amplitude would also vary significantly from vehicle to vehicle. Best way to determine that would be taking a sample in a Civic versus a big SUV. Perhaps you could have the user close the door in a "learning" mode to get the amplitude and frequency signature. Then you could use that to compare when in usage mode.
You could also consider using Fourier analysis to eliminate background noises that aren't associated with the door close.
Maybe you should try to detect significant instant rise in air pressure that should mark a door close. You can pair it with this waveform and sound level analysis and these all might give you a better result.
On the issue of less frequent sampling, the highest sound frequency which can be captured is half of the sampling rate. Thus, if the car door sound was strongest at 1000Hz (for example) then a sampling rate below 2000Hz would lose that sound entirely
A very simple noise gate would probably do just fine in your situation. Simply wait for the first sample whose amplitude is above a specified threshold value (to avoid triggering with background noise). You would only need to get more complicated than this if you need to distinguish between different types of noise (e.g. a door closing versus a hand clap).