I've got robotic arm with 6 DoF. I make constrain that x, z carteasian coordinate and orientation is exactly specified. I would like to get joint coordinates which are at cartesian position [x, y_max, z], where y_max is the maximum y cartesian coordinate which is reachable by the end-effector of the robotic arm.
For example:
I set x to be 0.5, z to by 1.0 and I want to find joint coordinates that satisfy after forward kinematics that robot's end-effector is at cartesian coordinates [0.5, maximum reachable coordinate, 1.0].
I know that if I know cartesian position and orientation I can find joint coordinates by inverse kinematics and check if the end-effector is at desired coordinateds by forward kinematics, but what if I don't know one of the axis in cartesian and it depends on robot how far it is possible to move? As far as I know, inverse kinematics is possible to solve analyticaly or numericaly, but to solve it I need to know the whole frame of the finish coordinate.
Moreover I would like to have orientation dependent on y coordinate. (for example I would like to guarantee that end-effector is always looking at coordinates [0.5, 0, 0]).
You could use a numerical task-based inverse kinematics with a task such as:
Orientation: the orientation you have specified
Position in (x, z): the coordinates you have specified
Position in y: something very far away
The behavior of a task-based approach (with proper damping) when a target is not feasible is to "stretch" the robot as far as it can without violating its constraints. Here is an example with a humanoid robot and three tasks:
(for example I would like to guarantee that end-effector is always looking at coordinates [0.5, 0, 0])
This should be possible with a proper task as well. For example, in C++ the mc_rtc framework has a LookAtTask to keep a frame looking at a desired point.
Related
I'm trying to implement different types of lights in my ray-tracer coded in C. I have successfully implemented spot, point, directional and rectangular area lights.
For rectangular area light I define two vectors (U and V) in space and I use them to move into the virtual (delimited) rectangle they form.
Depending on the intensity of the light I take several samples on the rectangle then I calculate the amount of the light reaching a point as though each sample were a single spot light.
With rectangles it is very easy to find the position of the various samples, but things get complicated when I try to do the same with a disk light.
I found little documentation about that and most of them already use ready-made functions to do so.
The only interesting thing I found is this document (https://graphics.pixar.com/library/DiskLightSampling/paper.pdf) but I'm unable to exploit it.
Would you know how to help me achieve a similar result (of the following image) with vector operations? (ex. Having the origin, orientation, radius of the disk and the number of samples)
Any advice or documentation in this regard would help me a lot.
This question reduces to:
How can I pick a uniformly-distributed random point on a disk?
A naive approach would be to generate random polar coordinates and transform them to cartesian coordinates:
Randomly generate an angle θ between 0 and 2π
Randomly generate a distance d between 0 and radius r of your disk
Transform to cartesian coordinates with x = r cos θ and y = r sin θ
This is incorrect because it causes the points to bunch up in the center; for example:
A correct, but inefficient, way to do this is via rejection sampling:
Uniformly generate random x and y, each over [0, 1]
If sqrt(x^2 + y^2) < 1, return the point
Goto 1
The correct way to do this is illustrated here:
Randomly generate an angle θ between 0 and 2π
Randomly generate a distance d between 0 and radius r of your disk
Transform to cartesian coordinates with x = sqrt(r) cos θ and y = sqrt(r) sin θ
Let's say I know two persons are standing at GPS location A and B. A is looking at B.
I would like to know B's (x, y, z) coordinates based on A, where the +y axis is the direction to B (since A is looking at B), +z is the vertically to the sky. (therefore +x is right-hand side of A)
I know how to convert a GPS coordinate to UTM, but in this case, a coordinate system rotation and translation seem needed. I am going to come up with a calculation, but before that, will there be some codes to look at?
I think this must be handled by many applications, but I could not find so far.
Convert booth points to 3D Cartesian
GPS suggest WGS84 so see How to convert a spherical velocity coordinates into cartesian
Construct transform matrix with your desired axises
see Understanding 4x4 homogenous transform matrices. So you need 3 perpendicular unit vectors. The Y is view direction so
Y = normalize(B-A);
one of the axises will be most likely up vector so you can use approximation
Z = normalize(A);
and as origin you can use point A directly. Now just exploit cross product to create X perpendicular to both and make also Y perpendicular to X and Z (so up stays up). For more info see Representing Points on a Circular Radar Math approach
Transfrom B to B' by that matrix
Again in the QA linked in #1 is how to do it. It is simple matrix/vector multiplication.
I just digged a bit through the Tensorflow Object Detection API code especially the eval_util part, as I wanted to implement the COCO metrics.
But I noticed that the metrics are solely calculated using the bounding boxes which have normalized coordinates between [0, 1].
There are no aspect ratios or absolute coordinates used.
So, doesn't this mean that the intersection over unions calculated on these results are incorrect?
Let's take an 200x100 image pixel as an example.
If the box would be off by 20px to the left, that's 0.1 in normalized coordinates.
But if it would be off by 20px to the top, that would be 0.2 in normalized coordinates.
Doesn't that mean, being off to the top is harder penalizing the score than being off to the side?
I believe the predicted coordinates are resized to the absolute image coordinates in the eval binary.
But the other thing I would say is that IOU is scale invariant in the sense that if you scale two boxes by some factor, they will still have the same IOU overlap. As an example if we scale by 2 in the x-direction and scale by 3 in the y direction:
If A is (x1, y1, x2, y2) and B is (u1, v1, u2, v2), then IOU((A, B))
= IOU((2*x1, 3*y1, 2*x2, 3*y2), (2*u1, 3*v1, 2*u2, 3*v2))
What this means is that evaluating in normalized coordinates should give the same result as evaluating in absolute coordinates.
When you specify a rotation for an object, you do something like this :
_earthNode.rotation = SCNVector4Make(1, 0, 0, M_PI/2);
What I am not getting is how to specify a specific rotation for each axis ? Because let's say that I wanted to rotate my node from PI on x, PI/2 on y, and PI/4 on z, how would I do that ? I thought that I could do something like this :
_earthNode.rotation = SCNVector4Make(1, 0.5, 0.25, M_PI);
But it doesn't change anything....
How does this property works ?
The rotation vector in Scene Kit is specified as the axis of rotation (first 3 components) follow by the angle (4th component), called axis-angle representation.
The format you are trying to specify (the different angles along each axis) is called Euler angles (unless I'm remembering wrong).
Translating between the two representations is just a bit of trigonometry. A quick online search for "Euler angles to axis angle" lead to this page which shows who to do it in Java.
SCNNode has an eulerAngles property that allows you to do just that
For modelviewMatrix I understand how to form translate and scale Matrix. But I am unable to understand how to form viewMatrix using GLKMatrix4MakeLookAt. Can anyone explain how to it works and how to give value to parameters(eye center up X Y Z).
GLK_INLINE GLKMatrix4 GLKMatrix4MakeLookAt(float eyeX, float eyeY, float eyeZ,
float centerX, float centerY, float centerZ,
float upX, float upY, float upZ)
GLKMatrix4MakeLookAt creates a viewing matrix (in the same way as gluLookAt does, in case you look at other OpenGL code). As the parameters suggest, it considers the position of the viewer's eye, the point in space they're looking at (e.g., a point on an object), and the up vector, which specifies which direction is "up" (e.g., pointing towards the sky). The viewing matrix generated is the combination of a rotation matrix (composed of a set of orthonormal bases [basis vectors]) and an translation.
Logically, the matrix is basically constructed in a few steps:
compute the line-of-sight vector, which is the normalized vector going from the eye's position to the point you're looking at, the center point.
compute the cross product of the line-of-sight vector with the up vector, and normalize the resulting vector.
compute the cross product of the vector computed in step 2. with the line-of-sight to complete the orthonormal basis.
create a 3x3 rotation matrix by setting the first row to the vector created in step 2., the middle row with the vector from step 3., and the bottom row to the negated, normalized line-of-sight vector.
those three steps produce a rotation matrix that will rotate the world coordinate system into eye coordinates (a coordinate system where the eye is located at the origin, and the line-of-sight is down the -z axis. The final viewing matrix is computed by multiplying a translation to the negated eye position, which moves the "world coordinate positioned eye" to the origin for eye coordinates.
Here's a related question showing the code of GLKMatrix4MakeLookAt, and here's a question with more detail about eye coordinates and related coordinate systems: (What exactly are eye space coordinates?) .