TGLTLSBFSSP: Inverse Kinematics - Improved Methods

# Inverse Kinematics - Improved Methods

My Inverse Kinematics Article has been one of my most popular, and I still get people asking me more about it. The sad thing is that I always have to explain to them that the article is way out of date. It was one of my first articles, written when I was still working out how to do it. Since then, I've come up with faster, and more robust methods.

 Lets look at a simple, two jointed, robot arm. It is fixed at the base, which is also the origin. The angle of the first joint, we shall call a, and the angle of the second, we shall call b. The length of each bone is 10. We can also imagine a target (red star). The vector from the tip to the target is known as the error.

## Minima

 As we move the robot arm around, changing the angles a and b, the tip gets nearer and further from the target We can plot a graph of this behaviour. On the left is a square graph. The origin is in the centre. The x-axis represents angle a, while the y-axis represents angle b. So at the origin, the arm is straight. We shall call the space of all possible orientations of the robot arm the Configuration Space. Each point on the square contains a shade of grey, which represents the distance between the tip and the target. Lighter shades are greater distances, while black represents the tip and target touching. There are, in fact, two places where the distance equals zero, representing the two ways the arm can touch the target. I have marked these with red spots. It should be obvious, therefore, that to get the tip to touch the target, you just need to find the blackest parts of the graph. These low points are known as Minima. (which is plural for the Latin word Minimum). Any algorithm you can think of, for finding the lowest points on graphs can be used for Inverse Kinematics. This is easy, of course, for a human, looking at a graph which has been drawn for us. It's a lot harder, when you consider that your algorithm will not have this graph drawn out for it in advance. Typically, a robot arm would begin at some point in the configuration space, and be instructed to reach the target. All it knows at that moment is how far it is from the target (i.e. the value of the graph at that point). Which way should it move now?

 The easiest way to find the lowest points on the graph is simply to follow the hills downwards. Look at this example. The target is the red crosshairs. In this position, rotating joint A a tiny bit will move the tip in the direction of the arrow a, and moving joint B a tiny bit will move the tip in the direction of the arrow b. To get the tip to the target, we want to move the tip in the direction of the t arrow. Clearly, rotating joint B will be of no use, as it will move the tip perpendicularally to the right direction. However, if we move joint A, it will move roughly in the right direction.
 Now let's look at this in terms of a graph. Starting at a random configuration of the arm (start), we can look at the vectors a and b, and rotate each joint slightly, in proportion to how much it helps get the tip to the target. You can think of this as examining the local gradient of the graph. Work out which direction leads us fastest downhill and move a little in that direction. Do the same over and over forever, or until you get close enough to the target to be happy. On the right, I have shown the graph of error, and added in contour lines so that you can easily see the gradient. You can see that the path the arm takes through the graph crosses the contours at right angles, which shows that it is moving directly down hill. Point P is interesting. It represents the position of the arm drawn above. You can see that as this point the arm is moving only in the A axis direction.
 Advantages This method has the advantage that it takes you smoothly from the start to the finish, which is great for computer graphics, or robotics, where you need smooth motion. It also has other advantages which we will see later. One real advantage of gradient following is that you can follow functions that are defined only by their gradient! What does this mean? I'll come to that later. Disadvantages If you need a quick answer, then this may not be the algorithm for you. As it has to follow the hill all the way down, it has to do a lot of calculations. Furthermore, as it reaches the bottom, it slows down and down, never actually reaching the target. Also, as you can see from the graph, it doesn't even take you straight to the target, it just follows the hill down, even if it's not the fastest route.

### How do you work out the gradient?

The easiest way to work out the gradient, (and the way which works in very many cases), is to literally try rotating each joint in turn, and measure how effective it is. This pseudocode keeps measuring the gradient, and moving the arm until the tip gets close enough.

 ``` function Calc_Distance(angle_A, angle_B) work out the tip position for joint A = angle_A and joint B = angle_B return distance from calculated tip position to target end function dist = Calc_Distance(a, b) while (dist > 0.1) { gradient_a = Calc_Distance(a+1, b) - Calc_Distance(a-1, b) gradient_b = Calc_Distance(a, b+1) - Calc_Distance(a, b-1) a -= gradient_a b -= gradient_b dist = Calc_Distance(a, b) } ```

 Our old friend Isaac Newton invented a method whereby you may you may calculate the gradient of an function you're sitting on. It's known as differentiation. Using this method, we may perform a single calculation for each joint to work out which direction to move it in. This is most useful for structures with many joints.

 ``` for each joint if 3D: axis = axis of rotation for this joint if 2D: axis = (0, 0, 1) ToTip = tip - joint_centre ToTarget = target - tip movement_vector = crossproduct(ToTip, axis) gradient = dotproduct(movement_vector, ToTarget) end loop ```

You can see that movement_vector is as long as ToTip, and at right angles to it. The further the tip is away from the joint, the faster it will move when the joint rotates. The vector axis is a unit vector.

One of the problems with gradient following is that, as you approach the target, the gradient falls off to nothing, meaning that the tip takes forever to get there. An optimisation of the gradient following method literally accelerates the process. Previously, at each iteration, the gradient was simply subtracted from the position in configuration space, moving the structure closer to the minimum. This time, we shall subtract the gradient from the speed at which we move through the configuration space.
 ``` dist = Calc_Distance(a, b) old_gradient_a = 0 old_gradient_b = 0 while (dist > 0.1) { gradient_a = Calc_Distance(a+1, b) - Calc_Distance(a-1, b) gradient_b = Calc_Distance(a, b+1) - Calc_Distance(a, b-1) have we gone past it? if sign(old_gradient_a) != sign(gradient_a) then a -= speeda * old_gradient_a / (gradient_a-old_gradient_a) speeda = 0 else speeda += ga if sign(old_gradient_b) != sign(gradient_b) then b -= speeda * old_gradient_b / (gradient_b-old_gradient_b) speedb = 0 else speedb += gb move a -= speed_a b -= speed_b dist = Calc_Distance(a, b) } ```

## Targets

So far, we have been assuming the target is a point in space, and so there is only one correct solution for the tip position. However, in a real situation, a target may be something like a table top, a hand rail, or even a volume of space.

In fact, a target need not even be defined in space. Since we can use gradient following, a target may be a direction (like downwards), or a direction field (a vortex for example).

 Plane Target If you want the tip of your structure to touch a table top, or a wall, for example, you can use a Plane Target. To make a plane shaped target, we need to be able to calculate the vector from the tip, to the nearest point on the plane. We shall define our plane by it's surface normal (green arrow), and a point on the plane (green sphere). Clearly the vector we want will be parallel to the surface normal. But how long is it? ``` p = point_on_plane - tip t = n * dotproduct(p, n) ``` Cylinder Target Again, useful for touching cylinder shaped objects, or just making the tip stay near an object. ``` v1 = axis * dotproduct(tip-i, axis) v2 = i+v1 DistanceToTarget = magnitude(v2) - radius ToTarget = v2 * (DistanceToTarget / magnitude(v2)) ``` Ring Target A ring target is very useful for guiding roboic fingertips to pick up an object. You can place the ring around a cup, for instance, and slowly decrease the radius of the ring, until the fingertips grip the cup.

## Vector Fields

A target need not be a point, plane or ring. Indeed it need not be any particular place at all. We might just want to define which direction the tip should move in. You might want the tip to keep away from an object, go round and object, or be contained to some corridor.

One way we can create this behaviour is by defining a vector field. Each point in space has a vector associated with it, and wherever the tip is in that space, it considers the target to be in the direction of that vector.

In order to simplify your code, with all the different types of target around, you can write a function which, given a TARGET, and a Tip_Position, will return a vector from the tip to the nearest point on the target. We begin by defining a structure TARGET containing several objects used by all types of target. Firstly, type, will tell the function which type of target this is. Then, centre, axis and size are three generic values that can be used to define many types of target.
 ``` constant POINT = 1 constant PLANE = 2 constant RING = 3 structure TARGET integer Target_Type vector centre vector axis number size end structure function to_target(TARGET T, vector Tip_Position) if T.Target_Type = POINT v = T.centre - Tip_Position return v end if if T.Target_Type = PLANE p = T.centre - Tip_Position v = T.axis * dotproduct(p, T.axis) return v end if if T.Target_Type = VECTOR_FIELD return vector at this Tip_Position end if end function ```

## Other Constraints

Making the tip reach it's target may not be the only important constraint we want to place on our robot / character / model. It's no good reaching the target if:

- It looks unconfortable or unnatural
- It makes the character fall over
- There are obstacles in the way

The good thing about Method 1, is that you can add lots of soft constraints into the mix, and it will try to solve them all at once, and you don't need to know how to differentiate them.

### Looking Comfortable

When you reach out with your arm and touch something, there are an infinite number of ways you could do it. Importantly, you could have your elbow pointing down, up, sideways, or anywhere inbetween. You could have your fingers straight or bent. The inverse kinematics we have seen so far could come up with any of these solutions, because it has no reason to choose one over the others, as long as the target is reached.

All we need to do, to make things look comfortable, is to impose a small 'penalty' for uncomfortable or unnatural positions. It depends how you define these things, but for something like a human arm, natural suggests lazy. I don't want to lift my arm any higher than I have to. Therefore, we can add to our error function, a cost for the centre of gravity of the arm.
 ``` function Calc_AveHeight(angle_A, angle_B) AveHeight = 0 TotalMass = 0 for each bone loop AveHeight = AveHeight + (Height_Of_Centre_Of_Gravity_Of_This_Bone * Mass_Of_This_Bone) TotalMass = TotalMass + Mass_Of_This_Bone end loop AveHeight = AveHeight / TotalMass return AveHeight end function function Calc_Distance(angle_A, angle_B) work out the tip_position for joint A = angle_A and joint B = angle_B return distance from calculated tip_position to target end function function Calc_Error(angle_A, angle_B) Error = Calc_Distance(angle_A, angle_B) * W1 + Calc_AveHeight(angle_A, angle_B) * W2 end function dist = Calc_Distance(a, b) while (dist > 0.1) { gradient_a = Calc_Error(a+1, b) - Calc_Error(a-1, b) gradient_b = Calc_Error(a, b+1) - Calc_Error(a, b-1) a -= gradient_a b -= gradient_b error = Calc_Error(a, b) } ```

As you might have guessed, Gradient following is not an amazing means of function minimisation. It can take a long time to reach it's target, possibly requiring thousands of calculations, and even then, only coming within a finite distance of the target. There are many other algorithms for function minimisation. Here are a few:

Minimization Algotithms: http://esd.lbl.gov/ITOUGH2/Minimization/minalg.html
A very brief description of several algorithms for function minimisation.

Downhill Simplex Method for Many (~20) Dimensions: http://paula.univ.gda.pl/~dokgrk/simplex.html
"The downhill simplex method is due to Nelder & Mead (1965). The method requires only function evaluations, not derivatives. It is not very efficient in terms of the number of function evaluations that it requires. However the downhill simplex method may frequently be the best method to use."

Rosenbrock Method: http://iridia.ulb.ac.be/~fvandenb/work/rosenbrock/
The Rosenbrock method is a 0th order search algorithm (it means it does not require any derivatives of the target function. Only simple evaluations of the objective function are used). Yet, it approximates a gradient search thus combining advantages of 0th order and 1st order strategies.

 Return to the Good LookingTextured Light Sourced Bouncy Fun Smart and Stretchy Page.