9
First I would question your math that got you to the 12b sensor.
If you have a $dy$ of 1 mm over an arm that is $r = 1$ m long, then $\sin(\theta) = dy/r \rightarrow \theta = \mbox{asin}(dy/r)$. If you make the small angle approximation $\sin{\theta} \approx \theta$, then $\theta \approx dy/r$.
This is $\theta$ in radians, so you're looking at a full ...
8
You might try a simple microphone. They're available in tiny surface mount packages:
You'll need to rub it across the surface, and you should be able to recognise different surface textures by the sound they make.
Do a Fourier transform on it, and you should be able to tell something about the scale of the surface textures.
You might not want to rub the ...
8
The only way to get a velocity from an accelerometer is to numerically integrate the output of the accelerometer. That is,
$$
v = v_0 + a*dT \\
$$
where $dT$ is the elapsed time between accelerometer readings. This means that you need to find the initial velocity $v_0$, and that an accelerometer cannot give an absolute velocity reading, only relative.
...
7
The answer is that 3-axis accelerometers don't have a left handed coordinate system just for the gravity. In static condition (i.e. if the accelerometer is not accelerating with respect to any inertial frame) they measure the opposite of gravity acceleration, not the gravity acceleration itself.
In more general terms, the accelerometers measure the ...
7
The sound beam is not traveling in a straight line but is leaving the range finder is a multi-lobed pattern.
Of course we are interested in the main lobe. When the sound wave hits an object, it is reflected in various directions. So some of that energy returns to the sensor and triggers it, therefore measuring the distance. Reflection depends on the ...
7
Assuming your vehicle is roughly horizontal to the ground, you won't be able get a good estimate of yaw from the accelerometer. Consider the nominal case: when your accelerometer is pointing straight down (Ax=0, Ay=0, Az=g) the reading will never change as you change yaw angle.
Normally, to get yaw angle vehicles use a magnometer (measure earth's magnetic ...
7
FWIW, I once needed to create a plexiglass window/shell for a near-IR camera. Most CCD and CMOS sensors are sensitive in the near IR range (e.g. around 850nm), which is where @user3095849 suggested your sensor sits.
I went to a local plexiglass supplier and asked for samples of various sheets (they often have lots of leftover pieces) and simply tried them ...
6
Using a so-called optical flow sensor is the best way to help with holding the horizontal (i.e. in X-Y plane) position. I don't see any reason why you couldn't do the same for vertical control, although a sonar is probably easier and cheaper to use for this (likewise, if you are indoors, you could use 2 sonars for the horizontal position as well)
People ...
6
What you're looking for is generally referred to as a slip ring. Here's a cheaper slip ring sold by Adafruit along with a video demonstrating how it works: http://www.adafruit.com/products/736. They sell hobbyist parts. I've used slip rings before to manufacture my own rotating LiDAR using a Hokuyo.
Here's another company that sells what seem to be ...
6
In short, what you are trying to do is well beyond the capabilities of top robotics research labs.
That said, here is a short list of general areas you need to look into:
Robotic arm dynamics (to swing the racket)
Vision processing to track the shuttle
Shuttle dynamics to predict shuttle path (this is not well studying so you would most likely have to ...
6
First you need to know the wavelength of the sensor. According to the sharp sensors datasheet for GP2Y0A21YK, the cover should efficiently transmit light throughout the wavelength range of the LED(λ = 850 nm ± 70 nm).
Second you need to find the exact material which can transmits the wavelength of the sensor. Some transparent plastics such as acrylic glass ...
6
Localization under water was always a problem in ocean robotics as electromagnetic signals do not propagate very well in water. I think your best localization sensor in that case would be the good old sonar, which works much faster in water. You could have four of them and detect how far are the pool walls on each side then with a triangulation algorithm ...
6
There are lots of ways to solve this problem, which falls into the category of Control Engineering. There are two standard approaches:
Classical Control: The control command has to be proportional to a linear combination of the error, the rate of change of the error, and the integral over time of the error, a.k.a. a PID controller. This approach ...
6
Let's recall that sensors are used to measure a physical quantity.
The distinction between passive and active sensor characterize the sensor ability to retrieve a measurement without or with inputing energy (mechanic or electromagnetic or else) in the environment.
For example, an infrared proximity sensor is active because the sensor needs to actively emit ...
6
There isn't really a relationship between them, unless you're asking how the dynamics feed each other when combined into a system.
If that's the case, then I would suggest maybe starting with a review of the Luenberger observer. It's typically taught as the "dual" of the state feedback control problem - state feedback controls attempt to drive the system ...
5
Gyroscopes will only give you the rate of change of the yaw angle, not the absolute yaw angle.
Unless you plan to set the yaw angle initially and have it drift further and further into garbage values (as you integrate the rate of change), you'll need another sensor to provide periodic updates on your actual yaw. This could be a magnetometer (compass), or ...
5
A compass will work just fine under water, I am an avid scuba diver and a compass is a standard piece of kit for navigating.
5
My favorite is the Learning OpenCV book. It has a fantastic stereo / 3D section that introduces concepts from the ground up. If you're at a university, you might be able to find the digital version available from the library website.
Depends, especially on how you are going to combine scans into a full 3D pointclound (if you need 360 degree views.) ...
5
Honestly it depends what you want, I will cover some options from cheapest to most expensive
Ultrasonic sensor on a servo 5-50$ depending on the model. It can be fairly accurate with around 1cm accurcy and 255 steps on a full circle, but it can have poor performance in dusty environments and can have poor results with curved items.
Ir distance sensor, 10-...
5
This is going to depend on the style of motor in the servo and the style of gearbox. If the servo can't be back-driven when unpowered, then it's likely some form of a worm gear assembly that will prevent static force transmission back to the motor. This means that you won't be able to tell weight by current draw for holding position because the holding ...
4
I know this is an old question but I will just add a bit to the currently existing answers. First, this is a very complex problem that everyone is trying to tackle, including google with their Tango project. In general, to localise indoor you either need to rely on your internal sensors, or get assistances from a indoor infrastructure deployed to assist you ...
4
Is it possible? Certainly. Is it worthwhile? Depends on how sensitive you need it to be.
Dog urine has a strong ammonia component (or maybe it's just my dogs that stink so bad) that could be used as the primary analyte to look for. Unfortunately, calibrated electronic ammonia sensors are expensive. I was actually looking into this just a week ago because I ...
4
You part list is fine as this is your first build. However I would suggest you to use ready made flight controller instead of buying arduino and program it yourself. Once you are comfortable flying quad rotor, it will be easier for you to test your code and adjust controller gains which is a very crucial step to control quad rotor as per your requirement. ...
4
What you are describing is essentially a textbook case for using a Kalman filter. First you need a prediction step. Let's assume you are predicting the pose of the robot $(x,y,\theta)$, given the previous pose estimate and your high-frequency velocity measurements $(v,\omega)$, where $v$ is the linear velocity and $\omega$ is the angular velocity.
$P$ is ...
4
The Kinect is certainly a popular choice these days for robotics. However, time-of-flight, structured light, and stereo cameras all have their own strengths and weaknesses. These two threads have a good discussion:
What main factors/features explain the high price of most industrial computer vision hardware?
Question for those who have experience using ...
4
If it's actually underwater, how about a webcam looking at the tile pattern on the floor? (Could be considered "cheating" as it will obviously fail in a natural lake, for example.)
You can find a paper using and demonstrating this method is this paper:
Carreras, Marc, et al. "Vision-based localization of an underwater robot in a structured environment." ...
4
One of the prime sensors for global localisation on land is GPS. This is not an option underwater because electromagnetic waves get absorbed quickly.
There are however alternatives, which provide navigation information which is not so easily available on land.
Large Baseline (LBL) - is a method based on sonar, which works very similar to GPS, just using ...
4
How fast are you driving the car and are you allowing slip during turning?
From this powerpoint, the turning radius is given by:
$$
R = \frac{L}{\delta}
$$
where $R$ is the turning radius, $L$ is the wheelbase length, and $\delta$ is the steering angle. Note that the equation is for low speed driving.
Given this equation, you can generate a circle around ...
4
I would go with one of two-ish methods to do this, but both methods require the craft to know its own position. You could do this with GPS, or an IMU, or any other means or combination of position tracking.
Method 1 - Only track where you are and where "home" is. Use sensors to detect obstacles along your path and navigate around them as applicable.
...
4
The only benchmark I know in development is at NIST. Their Intelligent Systems Division project page should help. On that page they discuss the specific possible metrics of grasp cycle time, grasp efficiency, finger strength, grasp strength, in-hand manipulation, object pose estimation, slip resistance, split cylinder artifact, touch sensitivity, finger ...
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