I am working on a simulation, which contains:
a bolt, welded uprightly to the world
a nut, connected to the bolt via ScrewJoint. The mass of a nut set to 0.02 kg, the inertia is a diagonal 1.1e-9 *I. This is configured via a .sdf file.
an iiwa manipulator, which is beside a point for now.
The problem is that the nut is very hard to manipulate and I cannot find a parameter to adjust, which could've made it more lifelike. To be more specific:
I measure the ability of force, applied tangentially in a horizontal plane to the nut, to cause the screwing motion of a joint, that joins the nut with a bolt
I'd like to have greater amount of motion at lower forces, and so far I am failing to achieve that
My interest in doing this is not idle; I am interested in more complicated simulations, which are also failing when iiwa is coming in a contact with this same joint; I've asked about those here and here. (Both answered partially). To sum up those here: when manipulator grips the nut, the nut fights the screwing in such a manner, that the schunk gripper is forced to unclasp and iiwa is thrown off-track, but the nut remains stationary.
I attach below two simpler experiments to better illustrate the issue:
1. Applying tangentially in a horizontal plane 200N force using ExternallyAppliedSpatialForce.
Graph notation: (here as well as below)
The left graph contains linear quantities (m, m/s, etc) along world's Z axis, the right graph contains angular quantities (in degrees, deg, deg/s, etc) around world's Z axis. The legend entries with a trailing apostrophe use the secondary Y-axis scale; other legend entries use the primary Y-axis scale.
Experiment summary:
This works as expected, 200 N is enough to make the nut spin on a bolt, resulting in the nut traveling vertically along the bolt for just under 1 centimeter, and spinning for over 90 degrees. Note: the externally applied force does not show up on my graph.
2. Applying tangentially in a horizontal plane force using iiwa and a simple position controller.
Experiment summary:
The force here is approximately the same as before: 70N along tz, but higher (170N) in tx and ty, though it is applied now only for a brief moment. The nut travels just a few degrees or hundredth fractions of centimeter.
The video of this unsuccessful interaction is below, the contact forces are visualized using ContactVisualizer.
Please advise me on how to make this screw_joint more compliant?
I've tried varying mass and inertia of the nut (different up to the orders of magnitude) in these experiments, this
seems to scale the contact forces, but does not affect acceleration or velocity of the nut after contact.
I like your experiment using ExternallyAppliedSpatialForce to get an idea of scales, though TBH I didn't quite get the details of this setup.
Things that caught my eye though are about scales, which you can estimate with pen and paper:
Your inertia is 1e-9 kg⋅m²?! Judging from your interaction with the iiwa I estimated a radius of 1cm and with that you'd get 2e-6 kg⋅m², three orders of magnitude larger.
A force of 200 N on a 20 grams nut would cause an acceleration of 10000 m/s². As a reference, that's 1000 times the acceleration of Earth's gravity!
Are these numbers correct? Also, if you happen to have fast interactions (do you?), you might want to estimate a time step that makes sense for your application.
Hopefully this helps!
A good thing is that I've fixed it; a bad thing: I don't understand the fix.
Let's restate a problem that I was tacking: a manipulated object reacted quite predictably to the ExternallyAppliedSpatialForce, but couldn't be moved via the contact with the manipulator.
What was done:
I've update drake from 2f340192a9dc79110410faf8a6d54a8615ddca92 (circa 22 Aug, 2022) to 42448c0af1b39f0c46f760e7ae37d77097689ad3 (circa 3 Nov, 2022)
After the update, my experimental setup broke down with assertion Actuation input port for model instance ... must be connected. [Similar to the issue raised in this question.]. My fix was like that:
bolt_n_nut_ = internal::AddAndWeldModelFrom(sdf_path, "nut_and_bolt",
plant_->world_frame(),
"bolt", X_WC, plant_);
then later in ManipulationStation::Finalize:
auto zero_torque = builder.template AddSystem<systems::ConstantVectorSource<double>>(
Eigen::VectorXd::Zero(1));
builder.Connect(zero_torque->get_output_port(),
plant_->get_actuation_input_port(bolt_n_nut_));
With changes above, a manipuland began to interact with the manipulator:
Things to note in graphs:
the distance the manipuland has moved grew from fractions of millimeter up to tens of centimeters. The video presents that a nut became manipulable.
This interaction violates the constraints of the ScrewJoint, i.e. the manipuland moves along it's axis without as much rotation
Related
I want to place virtual objects (holograms) at far distances (20+ meters) in the HoloLens 1. However, at such distances holograms become unstable and appear to "swim" in the display. Has anyone had success with this? What worked for you?
Some potential fixes include:
Ensure 60 FPS
Adjust Stabilization Plane
Employ visual markers (vuforia)
Use static room scan (may not scale well)
For me, frame rate is not an issue. And I am using Unity 2017.4.4f1. Currently, I have a single world anchor and all objects are set relative to this anchor.
20+ meters is a lot and I am not sure if this will work good enough.
Ensuring 60 fps or at least 50/55+ is important but this wont solve the swimming at this distance. A low framerate might only cause additional swimming :)
Everything that should appear statically placed in the room should be on or very close to the stabilization plane. So what you want to avoid is having the far objects at very different distances from the user. That would otherwise cause the ones farthest off from the stabilization plane to swim.
If you only have the far away object try placing the stabilization plane at the same distance as the object, if the distances are changing a lot you can also update the stabilization plane distance at runtime to always set it to the current distance to the object.
Would be interesting to hear if it worked out :)
One more thing: If I remember correctly, objects should ideally placed directly or in close proximity to their world anchor to help stabilization.
20 metres is too far. The docs
Best practices When holograms cannot be placed at 2m and conflicts
between convergence and accommodation cannot be avoided, the optimal
zone for hologram placement is between 1.25m and 5m. In every case,
designers should structure content to encourage users to interact 1+ m
away (e.g. adjust content size and default placement parameters).
I'm learning unity by the book "Unity game development in 24 hours". The book says:
Translation: Translation is a inert transformation. That means any changes applied after it won't be affected.
Scaling: Scaling effectively changes the size of the local coordinate grid. Basically, when you scale an object to be larger, you are really scaling the local coordinate system to be larger. This causes the object to seem to grow. This change is multiplicative. For example, if an object is scaled to 1 (its natural, default size) and then translated 5 units along the x axis, the object appears to move 5 units to the right. If the same object were to be scaled to 2, however, then translating 5 units on the x axis would result in the object appearing to move 10 units to the right. This is because the local coordinate system is now double the size and 5 times 2 equals 10. Inversely, if the object were scaled to .5 and then moved, it would appear to only move 2.5 units (.5 x 5 = 2.5)
I tried to experiment this two effects but it didn't work that way. To the Translation, I can apply any changes after it. And to the Scaling, it scaled the local coordinate system in multiplicative way but it didn't multi the affect of translation. Am I understand this wrong or it's the book?
Translating (using Transform.Translate method) means moving object's transform by some vector. Simple as that.
Local scale is little bit more complicated. It scales not only the object itself, but all objects, that are children of it. And the distance moved is relative - if you have a cube that's 1x1x1 in size and you move it by 1 unit, it will move its full length. If, however, you scale it by 2 and than move it by 1 unit, it moves only half its size.
According to what you wrote, the book is probably really bad source to learn Unity3D. Try doing some official tutorials, they are really good and explain the basics really well. This one is pretty good, this one as well. And remember, anytime you are in doubt with Unity. try to search their really good documentation first.
I want to ask about jelly physics ( http://www.youtube.com/watch?v=I74rJFB_W1k ), where I can find some good place to start making things like that ? I want to make simulation of cars crash and I want use this jelly physics, but I can't find a lot about them. I don't want use existing physics engine, I want write my own :)
Something like what you see in the video you linked to could be accomplished with a mass-spring system. However, as you vary the number of masses and springs, keeping your spring constants the same, you will get wildly varying results. In short, mass-spring systems are not good approximations of a continuum of matter.
Typically, these sorts of animations are created using what is called the Finite Element Method (FEM). The FEM does converge to a continuum, which is nice. And although it does require a bit more know-how than a mass-spring system, it really isn't too bad. The basic idea, derived from the study of continuum mechanics, can be put this way:
Break the volume of your object up into many small pieces (elements), usually tetrahedra. Let's call the entire collection of these elements the mesh. You'll actually want to make two copies of this mesh. Label one the "rest" mesh, and the other the "world" mesh. I'll tell you why next.
For each tetrahedron in your world mesh, measure how deformed it is relative to its corresponding rest tetrahedron. The measure of how deformed it is is called "strain". This is typically accomplished by first measuring what is known as the deformation gradient (often denoted F). There are several good papers that describe how to do this. Once you have F, one very typical way to define the strain (e) is:
e = 1/2(F^T * F) - I. This is known as Green's strain. It is invariant to rotations, which makes it very convenient.
Using the properties of the material you are trying to simulate (gelatin, rubber, steel, etc.), and using the strain you measured in the step above, derive the "stress" of each tetrahdron.
For each tetrahedron, visit each node (vertex, corner, point (these all mean the same thing)) and average the area-weighted normal vectors (in the rest shape) of the three triangular faces that share that node. Multiply the tetrahedron's stress by that averaged vector, and there's the elastic force acting on that node due to the stress of that tetrahedron. Of course, each node could potentially belong to multiple tetrahedra, so you'll want to be able to sum up these forces.
Integrate! There are easy ways to do this, and hard ways. Either way, you'll want to loop over every node in your world mesh and divide its forces by its mass to determine its acceleration. The easy way to proceed from here is to:
Multiply its acceleration by some small time value dt. This gives you a change in velocity, dv.
Add dv to the node's current velocity to get a new total velocity.
Multiply that velocity by dt to get a change in position, dx.
Add dx to the node's current position to get a new position.
This approach is known as explicit forward Euler integration. You will have to use very small values of dt to get it to work without blowing up, but it is so easy to implement that it works well as a starting point.
Repeat steps 2 through 5 for as long as you want.
I've left out a lot of details and fancy extras, but hopefully you can infer a lot of what I've left out. Here is a link to some instructions I used the first time I did this. The webpage contains some useful pseudocode, as well as links to some relevant material.
http://sealab.cs.utah.edu/Courses/CS6967-F08/Project-2/
The following link is also very useful:
http://sealab.cs.utah.edu/Courses/CS6967-F08/FE-notes.pdf
This is a really fun topic, and I wish you the best of luck! If you get stuck, just drop me a comment.
That rolling jelly cube video was made with Blender, which uses the Bullet physics engine for soft body simulation. The bullet documentation in general is very sparse and for soft body dynamics almost nonexistent. You're best bet would be to read the source code.
Then write your own version ;)
Here is a page with some pretty good tutorials on it. The one you are looking for is probably in the (inverse) Kinematics and Mass & Spring Models sections.
Hint: A jelly can be seen as a 3 dimensional cloth ;-)
Also, try having a look at the search results for spring pressure soft body model - they might get you going in the right direction :-)
See this guy's page Maciej Matyka, topic of soft body
Unfortunately 2d only but might be something to start with is JellyPhysics and JellyCar
Users can sketch in my app using a very simple tool (move mouse while holding LMB). This results in a series of mousemove events and I record the cursor location at each event. The resulting polyline curve tends to be rather dense, with recorded points almost every other pixel. I'd like to smooth this pixelated polyline, but I don't want to smooth intended kinks. So how do I figure out where the kinks are?
The image shows the recorded trail (red pixels) and the 'implied' shape as a human would understand it. People tend to slow down near corners, so there is usually even more noise here than on the straight bits.
Polyline tracker http://www.freeimagehosting.net/uploads/c83c6b462a.png
What you're describing may be related to gesture recognition techniques, so you could search on them for ideas.
The obvious approach is to apply a curve fit, but that will have the effect of smoothing away all the interesting details and kinks. Another approach suggested is to look at speeds and accelerations, but that can get hairy (direction changes can be very fast or very slow and deliberate)
A fairly basic but effective approach is to simplify the samples directly into a polyline.
For example, work your way through the samples (e.g.) from sample 1 to sample 4, and check if all 4 samples lie within a reasonable error of the straight line between 1 & 4. If they do, then extend this to points 1..5 and repeat until such a time as the straight line from the start point to the end point no longer provides a resonable approximation to the curve defined by those samples. Create a line segment up to the previous sample point and start accumulating a new line segment.
You have to be careful about your thresholds when the samples are too close to each other, so you might want to adjust the sensitivity when regarding samples fewer than 4-5 pixels away from each other.
This will give you a set of straight lines that will follow the original path fairly accurately.
If you require additional smoothing, or want to create a scalable vector graphic, then you can then curve-fit from the polyline. First, identify the kinks (the places in your polyline where the angle between one line and the next is sharp - e.g. anything over 140 degrees is considered a smooth curve, anything less than that is considered a kink) and break the polyline at those discontinuities. Then curve-fit each of these sub-sections of the original gesture to smooth them. This will have the effect of smoothing the smooth stuff and sharpening the kinks. (You could go further and insert small smooth corner fillets instead of these sharp joints to reduce the sharpness of the joins)
Brute force, but it may just achieve what you want.
Rather than trying to do this from the resultant data, have you considered looking at the timing of the data as it comes in? If the mouse stops or slows noticably, you use the trend since the last 'kink' (the last time the mouse slowed) to establish the direction of travel. If the user goes off in a new direction, you call it a kink, otherwise, you ignore the current slowing trend and start waiting for the next one.
Well, one way would be to use a true curve-fitting algorithm. Generate a bezier curve (with exact endpoints, using Catmull-Rom or something similar), then optimize & recursively subdivide (using distance from actual line points as a cost metric). This may be too complicated for your use-case, though.
Record the order the pixels are drawn in. Then, compute the slope between pixels that are "near" but not "close". I'm guessing a graph of the slope between pixel(i) and pixel(i+7) might exhibit easily identifable "jumps" around kinks in the curve.
I have an application in which users interact with each-other. I want to visualize these interactions so that I can determine whether clusters of users exist (within which interactions are more frequent).
I've assigned a 2D point to each user (where each coordinate is between 0 and 1). My idea is that two users' points move closer together when they interact, an "attractive force", and I just repeatedly go through my interaction logs over and over again.
Of course, I need a "repulsive force" that will push users apart too, otherwise they will all just collapse into a single point.
First I tried monitoring the lowest and highest of each of the XY coordinates, and normalizing their positions, but this didn't work, a few users with a small number of interactions stayed at the edges, and the rest all collapsed into the middle.
Does anyone know what equations I should use to move the points, both for the "attractive" force between users when they interact, and a "repulsive" force to stop them all collapsing into a single point?
Edit: In response to a question, I should point out that I'm dealing with about 1 million users, and about 10 million interactions between users. If anyone can recommend a tool that could do this for me, I'm all ears :-)
In the past, when I've tried this kind of thing, I've used a spring model to pull linked nodes together, something like: dx = -k*(x-l). dx is the change in the position, x is the current position, l is the desired separation, and k is the spring coefficient that you tweak until you get a nice balance between spring strength and stability, it'll be less than 0.1. Having l > 0 ensures that everything doesn't end up in the middle.
In addition to that, a general "repulsive" force between all nodes will spread them out, something like: dx = k / x^2. This will be larger the closer two nodes are, tweak k to get a reasonable effect.
I can recommend some possibilities: first, try log-scaling the interactions or running them through a sigmoidal function to squash the range. This will give you a smoother visual distribution of spacing.
Independent of this scaling issue: look at some of the rendering strategies in graphviz, particularly the programs "neato" and "fdp". From the man page:
neato draws undirected graphs using ``spring'' models (see Kamada and
Kawai, Information Processing Letters 31:1, April 1989). Input files
must be formatted in the dot attributed graph language. By default,
the output of neato is the input graph with layout coordinates
appended.
fdp draws undirected graphs using a ``spring'' model. It relies on a
force-directed approach in the spirit of Fruchterman and Reingold (cf.
Software-Practice & Experience 21(11), 1991, pp. 1129-1164).
Finally, consider one of the scaling strategies, an attractive force, and some sort of drag coefficient instead of a repulsive force. Actually moving things closer and then possibly farther later on may just get you cyclic behavior.
Consider a model in which everything will collapse eventually, but slowly. Then just run until some condition is met (a node crosses the center of the layout region or some such).
Drag or momentum can just be encoded as a basic resistance to motion and amount to throttling the movements; it can be applied differentially (things can move slower based on how far they've gone, where they are in space, how many other nodes are close, etc.).
Hope this helps.
The spring model is the traditional way to do this: make an attractive force between each node based on the interaction, and a repulsive force between all nodes based on the inverse square of their distance. Then solve, minimizing the energy. You may need some fairly high powered programming to get an efficient solution to this if you have more than a few nodes. Make sure the start positions are random, and run the program several times: a case like this almost always has several local energy minima in it, and you want to make sure you've got a good one.
Also, unless you have only a few nodes, I would do this in 3D. An extra dimension of freedom allows for better solutions, and you should be able to visualize clusters in 3D as well if not better than 2D.