the question might seem simple, but I am unable to find the best reference for it. Using ODE force function, I am able to apply force to a ball's COM and the ball starts rolling. It stops after a few seconds. The distance travelled depends upon the force. However, I am looking for algorithms that can help to predict the actual ball location after specified time 'x'. Note that the ball moves in 2D and I need to figure out the x and y coordinates of ball after lets say 1 second. Velocity and initial coordinates are given.
Please suggest algorithms that can do it precisely. the newton velocity acceleration relation only works for shorter prediction time as they depend on linear velocity component.
Please help
Related
Question is, I want to calculate the speed of my arm for Slap detection. So I am using openpose to get the body points (here total points: 25) using body_25 model and using this along with the time I want to deduce the speed of my arm, i googled through openpose, stackoverflow, github.But could not succeed?
Velocity = Distance / Time = dx/dt
dx = frame3_bodypoints - frame_1_bodypoints;
dt = ?
I don't know how to find this from the openpose, is there a way I can find this? Any thoughts, would be great help!
I've never used OpenPose. But Newtonian physics would indicate that a slap corresponds to a sudden change in velocity of the hand.
I think it's a reasonable first approximation to assume that the Δt between frames is constant. Instantaneous variation in frame rate is called jitter. I would expect jitter to be small for modern recording devices. In any case, I don't know how to get instantaneous frame rate with the tools (OpenCV, PIL) that I am familiar with. I couldn't find any references to frame rate or time in the OpenPose docs.
For calculating velocity and delta-velocity, you have choices. Straight up linear velocity of the hand might be the easiest. For position changes use the geometric mean of positions (Δs = sqrt((x2-x1)^2 + (y2-y1)^2).
You could also calculate an angular velocity between the hand and the elbow, but that would be a little more involved and prone to noise.
I 'program' simple hyper casual mobile games in my free time using a sudo programming language software called construct 3, as I am still learning actual languages and can't yet use them well enough to make games.
Essentially I am writing my own super simple bouncing ball physics engine. I have up to 3 balls in this little pinball game of mine at any time. I have given each ball an x velocity and y velocity instance variable.
Here is my question: how do the x and y velocities change when the ball bounces off of a surface with any angle? I know that if the floor is flat and it hits that, x stays the same and y flips it's polarity. I know the opposite happens with hitting a wall. But I have no idea how to calculate any other angle besides the 4 main axes. I'm sure it is a simple trig function. Oh, and dumb your answer down to the most simple sudo-code response you can make.
For any collision of an object against a flat surface of an angle alpha, your object will bounce back with an angle -alpha. Also, your have what's called a conservation of momentum, which means if your surface doesn't move and does not absorb anything, the total velocity of your object will not change either.
That being said, "all you need to do" is to parameter both the angle of your surface to the horizontal and the angle of your object incoming to your surface, so you can easily register an angle alpha. This way, you will be able to get a -alpha angle between your object and the surface after the collision in the frame of your surface, and you will then need to go back to the "horizontal frame" by simply adding the angle of your surface.
As far as your implementation should go, this is what I suggest:
Start with a function horizontalToAngularFrame that will takes one or more parameter depending if you're in 2D or 3D, so you can define the angle
Code another function AngularFrameToHorizontal with the same number of parameter
When an object enters in collision, just treat is as you would treat an object in the horizontal frame, and use the 2 previously coded functions to bring the angles back to your horizontal frame
I made an object tracker that calculates the position of an object recorded in a live camera feed using stereoscopic cameras. The math was simple, once you know the camera distance and orientation. However, now I thought it would be nice to allow me to quickly extract all these parameters, so when I change my setup or cameras I will be able to quickly calibrate it again.
To calculate the object position I made some simplifications/assumptions, which made the math easier: the cameras are in the same YZ plane, so there is only a distance in x between them. Their tilt is also just in the XY plane.
To reverse the triangulation I thought a test pattern (square) of 4 points of which I know the distances to each other would suffice. Ideally I would like to get the cameras' positions (distances to test pattern and each other), their rotation in X (and maybe Y and Z if applicable/possible), as well as their view angle (to translate pixel position to real world distances - that should be a camera constant, but in case I change cameras, it is quite a bit to define accurately)
I started with the same trigonometric calculations, but always miss parameters. I am wondering if there is an existing solution or a solid approach. If I need to add parameter (like distances, they are easy enough to measure), it's no problem (my calculations didn't give me any simple equations with that possibility though).
I also read about Homography in opencv, but it seems it applies to 2D space only, or not?
Any help is appreciated!
I have become a part of this infinite question of how to estimate position from accelerometer data achieved by an Inertial measurement unit. I am wondering how to compensate for integration ''drift'' during linear movement using Kalman filtering.
At this moment I got my acceleration in a fixed coordinate system and all movements are in know directions with no change in angular position.
So at this point we got acceleration in 3D (x-y-z) in known directions, an acceleration in x will yield for zero acceleration in y and z and so on. Assuming perfect conditions, which are not the case, of course some noise with be added to the other directions when moving in one direction but lets ''leave'' this out at this point. In addition, It is important to note that the system only has to estimate a limited period, approximately about 1 second using a sampling freq of 512 Hz.
It also important to note that I have compensated for the offset (gravity and misalignment of the accelerometer in the IMU) and bias of the acceleromter data when static. Meaning when the sensor is non-moving all my readings are constant zero before going into the Kalman filter.
To more characterize my problem I have this graph to illustrate my problem with drift. This is estimations on 5 seconds to more show what I'm struggling with.
Position-estimation-drift-problem
Here we are looking into a movement in one direction, the movement are 20cm movement in y direction which in my case are forward relative to my starting position.
Is there a way to reduce/eliminate this drift when integrating my signal. For instance assume something about drifting when my sensor is non-moving. Or to compute using some correction in my Kalman algorithm to subtract or add to my estimated velocity and position. The system does not have to run in real time so any tuning bias compensation can be adjusted for looking back into the data. But I would be preferable if it was possible to take new measurements with slightly different movements and not tune more then needed.
Finally where/how can I compensate for this, in the Kalman algorithm or before/after, or should I be in for a disappointment already?
If I left out some important information please ask so i can elaborate more, an at last any thoughts/ideas are welcome!
Remember I do only need to estimate for second’s worth of time so my hope is that this makes it more achievable, but i might be wrong?
I can only guess/suggest few tricks, but you will probably get some significant error if you only based on accelerometer.
seems that detecting motionless is not resetting the speed, just acceleration (according to your graph) so this should be an easy fix
if we are talking an a car/other type of surface motion with contact / friction, your motionless can be set by characterizing the noise of in motion/self sensor noise
kalman parameters may be off
run multiple kernels and average results (may also try particle filter)
if its not for online application you can also try fitting offsets/drift and reduce them by assuming there is not motion in constant speed or other approaches that can replace the kalman filter which is designed for real time best estimation.
error seems a-symmetric in time, just run it in both directions (:
what are you measuring at 512 Hz??? maybe you can better model it
I can go on and on but if you supply data and code, it would be much easier.
Good luck,
Lev
I cannot get my head around the maths / geometry for this, but I am sure there is a simple (ish) algorithm for this.
Trying to control a telescope on alt/azimuth, and I need to sole the following problem.
I know my lat/long - I definately know where my house is.
From this, I know what angle polaris / centre of rotation of starfield is at, and what bearing.
I know how long an astronomical day is in secs, so how long one full rotation of starfield will take.
I want to calculate how much a given point in the sky will move (in say one second) in terms of delta-x (rotation of scope horizontally, azimuth), and delta-y (elevation of scope in degrees, altitude).
I know which point in sky I am looking at as I have compass and inclinometer readings from device on my scope.
A star close to polaris in view will move only a small dx and dy as
it tracks a small piece of sky. A star moving from due east to due
west will track a much larger path in sky, as it sweeps over the
largest track. A star much more southerly will track reducing
amounts of dx and dy.
Does anyone know how to compute dx and dy given lat/long, direction and elevation of scope ?
Yes there is an algorithm and I can even get you a open source code to do that. You just have to explore the Toshimi taki (japanese amateur astronomeur & programmer) website:
Toshimi Taki Website!