I am going to develop a 2-d ball game where two balls (circles) collide. Now I have the problem with determining the colliding point (in fact, determining whether they are colliding in x-axis/y-axis). I have an idea that when the difference between the y coordinate of 2 balls is greater than the x coordinate difference then they collide in their y axis, otherwise, they collide in their x axis. Is my idea correct? I implemented this thing in my games. Normally it works well, but sometimes, it fails. Can anyone tell me whether my idea is right? If not, then why, and is any better way?
By collision in the x axis, I mean the circle's 1st, 4th, 5th, or 8th octant, y axis means the circle's 2nd, 3rd, 6th, or 7th octant.
Thanks in advance!
Collision between circles is easy. Imagine there are two circles:
C1 with center (x1,y1) and radius r1;
C2 with center (x2,y2) and radius r2.
Imagine there is a line running between those two center points. The distance from the center points to the edge of either circle is, by definition, equal to their respective radii. So:
if the edges of the circles touch, the distance between the centers is r1+r2;
any greater distance and the circles don't touch or collide; and
any less and then do collide.
So you can detect collision if:
(x2-x1)^2 + (y2-y1)^2 <= (r1+r2)^2
meaning the distance between the center points is less than the sum of the radii.
The same principle can be applied to detecting collisions between spheres in three dimensions.
Edit: if you want to calculate the point of collision, some basic trigonometry can do that. You have a triangle:
(x1,y1)
|\
| \
| \ sqrt((x2-x1)^2 + (y2-y1)^2) = r1+r2
|y2-y1| | \
| \
| X \
(x1,y2) +------+ (x2,y2)
|x2-x1|
The expressions |x2-x1| and |y2-y1| are absolute values. So for the angle X:
|y2 - y1|
sin X = -------
r1 + r2
|x2 - x1|
cos X = -------
r1 + r2
|y2 - y1|
tan X = -------
|x2 - x1|
Once you have the angle you can calculate the point of intersection by applying them to a new triangle:
+
|\
| \
b | \ r2
| \
| X \
+-----+
a
where:
a
cos X = --
r2
so
a = r2 cos X
From the previous formulae:
|x2 - x1|
a = r2 -------
r1 + r2
Once you have a and b you can calculate the collision point in terms of (x2,y2) offset by (a,b) as appropriate. You don't even need to calculate any sines, cosines or inverse sines or cosines for this. Or any square roots for that matter. So it's fast.
But if you don't need an exact angle or point of collision and just want the octant you can optimize this further by understanding something about tangents, which is:
0 <= tan X <= 1 for 0 <= X <= 45 degrees;
tan X >= 1 for 45 <= X <= 90
0 >= tan X >= -1 for 0 >= X => -45;
tan X <= -1 for -45 >= X => -90; and
tan X = tan (X+180) = tan (X-180).
Those four degree ranges correspond to four octants of the cirlce. The other four are offset by 180 degrees. As demonstrated above, the tangent can be calculated simply as:
|y2 - y1|
tan X = -------
|x2 - x1|
Lose the absolute values and this ratio will tell you which of the four octants the collision is in (by the above tangent ranges). To work out the exact octant just compare x1 and x2 to determine which is leftmost.
The octant of the collision on the other single is offset (octant 1 on C1 means octant 5 on C2, 2 and 6, 3 and 7, 4 and 8, etc).
As cletus says, you want to use the sum of the radii of the two balls. You want to compute the total distance between the centers of the balls, as follows:
Ball 1: center: p1=(x1,y1) radius: r1
Ball 2: center: p2=(x2,y2) radius: r2
collision distance: R= r1 + r2
actual distance: r12= sqrt( (x2-x1)^2 + (y2-y1)^2 )
A collision will happen whenever (r12 < R). As Artelius says, they shouldn't actually collide on the x/y axes, they collide at a particular angle. Except, you don't actually want that angle; you want the collision vector. This is the difference between the centers of the two circles when they collide:
collision vector: d12= (x2-x1,y2-y1) = (dx,dy)
actual distance: r12= sqrt( dx*dx + dy*dy )
Note that you have already computed dx and dy above when figuring the actual distance, so you might as well keep track of them for purposes like this. You can use this collision vector for determining the new velocity of the balls -- you're going to end up scaling the collision vector by some factors, and adding that to the old velocities... but, to get back to the actual collision point:
collision point: pcollision= ( (x1*r2+x2*r1)/(r1+r2), (y1*r2+y2*r1)/(r1+r2) )
To figure out how to find the new velocity of the balls (and in general to make more sense out of the whole situation), you should probably find a high school physics book, or the equivalent. Unfortunately, I don't know of a good web tutorial -- suggestions, anyone?
Oh, and if still want to stick with the x/y axis thing, I think you've got it right with:
if( abs(dx) > abs(dy) ) then { x-axis } else { y-axis }
As for why it might fail, it's hard to tell without more information, but you might have a problem with your balls moving too fast, and passing right by each other in a single timestep. There are ways to fix this problem, but the simplest way is to make sure they don't move too fast...
This site explains the physics, derives the algorithm, and provides code for collisions of 2D balls.
Calculate the octant after this function calculates the following: position of collision point relative to centre of mass of body a; position of collision point relative to centre of mass of body a
/**
This function calulates the velocities after a 2D collision vaf, vbf, waf and wbf from information about the colliding bodies
#param double e coefficient of restitution which depends on the nature of the two colliding materials
#param double ma total mass of body a
#param double mb total mass of body b
#param double Ia inertia for body a.
#param double Ib inertia for body b.
#param vector ra position of collision point relative to centre of mass of body a in absolute coordinates (if this is
known in local body coordinates it must be converted before this is called).
#param vector rb position of collision point relative to centre of mass of body b in absolute coordinates (if this is
known in local body coordinates it must be converted before this is called).
#param vector n normal to collision point, the line along which the impulse acts.
#param vector vai initial velocity of centre of mass on object a
#param vector vbi initial velocity of centre of mass on object b
#param vector wai initial angular velocity of object a
#param vector wbi initial angular velocity of object b
#param vector vaf final velocity of centre of mass on object a
#param vector vbf final velocity of centre of mass on object a
#param vector waf final angular velocity of object a
#param vector wbf final angular velocity of object b
*/
CollisionResponce(double e,double ma,double mb,matrix Ia,matrix Ib,vector ra,vector rb,vector n,
vector vai, vector vbi, vector wai, vector wbi, vector vaf, vector vbf, vector waf, vector wbf) {
double k=1/(ma*ma)+ 2/(ma*mb) +1/(mb*mb) - ra.x*ra.x/(ma*Ia) - rb.x*rb.x/(ma*Ib) - ra.y*ra.y/(ma*Ia)
- ra.y*ra.y/(mb*Ia) - ra.x*ra.x/(mb*Ia) - rb.x*rb.x/(mb*Ib) - rb.y*rb.y/(ma*Ib)
- rb.y*rb.y/(mb*Ib) + ra.y*ra.y*rb.x*rb.x/(Ia*Ib) + ra.x*ra.x*rb.y*rb.y/(Ia*Ib) - 2*ra.x*ra.y*rb.x*rb.y/(Ia*Ib);
double Jx = (e+1)/k * (Vai.x - Vbi.x)( 1/ma - ra.x*ra.x/Ia + 1/mb - rb.x*rb.x/Ib)
- (e+1)/k * (Vai.y - Vbi.y) (ra.x*ra.y / Ia + rb.x*rb.y / Ib);
double Jy = - (e+1)/k * (Vai.x - Vbi.x) (ra.x*ra.y / Ia + rb.x*rb.y / Ib)
+ (e+1)/k * (Vai.y - Vbi.y) ( 1/ma - ra.y*ra.y/Ia + 1/mb - rb.y*rb.y/Ib);
Vaf.x = Vai.x - Jx/Ma;
Vaf.y = Vai.y - Jy/Ma;
Vbf.x = Vbi.x - Jx/Mb;
Vbf.y = Vbi.y - Jy/Mb;
waf.x = wai.x - (Jx*ra.y - Jy*ra.x) /Ia;
waf.y = wai.y - (Jx*ra.y - Jy*ra.x) /Ia;
wbf.x = wbi.x - (Jx*rb.y - Jy*rb.x) /Ib;
wbf.y = wbi.y - (Jx*rb.y - Jy*rb.x) /Ib;
}
I agree with provided answers, they are very good.
I just want to point you a small pitfall: if the speed of balls is high, you can just miss the collision, because circles never intersect for given steps.
The solution is to solve the equation on the movement and to find the correct moment of the collision.
Anyway, if you would implement your solution (comparisons on X and Y axes) you'd get the good old ping pong! http://en.wikipedia.org/wiki/Pong
:)
The point at which they collide is on the line between the midpoints of the two circles, and its distance from either midpoint is the radius of that respective circle.
Related
Given the center, radius and and 3 points on a circle, I want to draw an arc that starts at the first point, passing through the second and ends at the third by specifying the angle to start drawing and the amount of angle to rotate. To do this, I need to calculate the points on the arc. I want the number of points calculated to be variable so I can adjust the accuracy of the calculated arc, so this means I probably need a loop that calculates each point by rotating a little after it has calculated a point. I've read the answer to this question Draw arc with 2 points and center of the circle but it only solves the problem of calculating the angles because I don't know how 'canvas.drawArc' is implemented.
This question has two parts:
How to find the arc between two points that passes a third point?
How to generate a set of points on the found arc?
Let's start with first part. Given three points A, B and C on the (O, r) circle we want to find the arc between A and C that passes through B. To find the internal angle of the arc we need to calculate the oriented angles of AB and AC arcs. If angle of AB was greater than AC, we are in wrong direction:
Va.x = A.x - O.x;
Va.y = A.y - O.y;
Vb.x = B.x - O.x;
Vb.y = B.y - O.y;
Vc.x = C.x - O.x;
Vc.y = C.y - O.y;
tb = orientedAngle(Va.x, Va.y, Vb.x, Vb.y);
tc = orientedAngle(Va.x, Va.y, Vc.x, Vc.y);
if tc<tb
tc = tc - 2 * pi;
end
function t = orientedAngle(x1, y1, x2, y2)
t = atan2(x1*y2 - y1*x2, x1*x2 + y1*y2);
if t<0
t = t + 2 * pi;
end
end
Now the second part. You said:
I probably need a loop that calculates each point by rotating a little
after it has calculated a point.
But the question is, how little? Since the perimeter of the circle increases as its radius increase, you cannot reach a fixed accuracy with a fixed angle. In other words, to draw two arcs with the same angle and different radii, we need a different number of points. What we can assume to be [almost] constant is the distance between these points, or the length of the segments we draw to simulate the arc:
segLen = someConstantLength;
arcLen = abs(tc)*r;
segNum = ceil(arcLen/segLen);
segAngle = tc / segNum;
t = atan2(Va.y, Va.x);
for i from 0 to segNum
P[i].x = O.x + r * cos(t);
P[i].y = O.y + r * sin(t);
t = t + segAngle;
end
Note that although in this method A and C will certainly be created, but point B will not necessarily be one of the points created. However, the distance of this point from the nearest segment will be very small.
I have an array of diamonds as shown in the image and I know the position of every diamond and the distance from the origin of the diamond to any vertex (They are all the same distance from the center). I am also given a point. Given that information what is the most efficient method to find which diamond the point is in.
I know that I can just check the distance of the point from the position of every diamond but that seems way too cpu intensive as I have to do this multiple times.
Also, this shouldn't matter, but I am using C# and Unity 3D to do this.
If your diamonds form a regular pattern as in your picture, then just perform coordinate transformation to rotate the whole thing 45 degrees CW or CCW with (0, 0) as the origin. After that the problem becomes trivial: locating a point in a regular orthogonal grid.
Diamonds border line have equations
x + y = a0 + u * Size
y - x = b0 + v * Size
where a0, b0 are coordinates of the some vertex of base diamond (that has cell coordinates 0, 0), u and v are cell coordinates, Size is edge length. So to find what diamond point (px, py) belongs to, you can calculate
u = Floor((px + py - a0) / Size))
v = Floor((py - px - b0) / Size))
I've never deal much with location-based data, so very much new to the whole GPS coding related questions. I have a problem that I don't seem to find a very efficient way in solving it or maybe there's an algorithm that I'm not too sure.
Let said you have given 4 lat/long coordinates which construct some kind of a rectangular area: (X0, Y0), (X1, Y0), (X0, Y1), (X1, Y1)
-----------------------------------
| b |
| a |
| | d
| |
| c |
-----------------------------------
e
Is there a way to find all the point that are inside the given rectangular area : a, b, c
And all the points outside of the area? e, d
I can easily to construct a 2D matrix to do this, but that's only if the coordinates are in integer, but with lat/long pairs, the coordinates are usually in float numbers which we cannot use it to construct a 2D table.
Any cool ideas?
Edited 1:
What about this Ray-casting algorithm? Is this a good algorithm to be used for GPS coordinates which is a float number?
If your rectangle is axis-aligned, #Eyal's answer is the right one (and you actually don't need 8 values but 4 are enough).
If you deal with a rotated rectangle (will work for any quadrilateral), the ray-casting method is appropriate: consider the horizontal line Y=Yt through your test point and find the edges that cross it (one endpoint above, one endpoint below). There will be 0 or 2 such edges. In case 0, you are outside. Otherwise, compute the abscissas of the intersections of these edges with the line. If 0 or 2 intersection are on the left of the test point, you are outside.
Xi= Xt + (Yt - Y0) (X1 - X0) / (Y1 - Y0)
An alternative solution to #YvesDaoust's and #EyalSchneider's is to find the winding number or the crossing number of each point (http://geomalgorithms.com/a03-_inclusion.html). This solution scales to a polygon of any number of vertices (regardless of axis-alignment).
The Crossing Number (cn) method
- which counts the number of times a ray starting from the point P crosses the polygon boundary edges. The point is outside when this "crossing number" is even; otherwise, when it is odd, the point is inside. This method is sometimes referred to as the "even-odd" test.
The Winding Number (wn) method
- which counts the number of times the polygon winds around the point P. The point is outside only when this "winding number" wn = 0; otherwise, the point is inside.
Incidentally, #YvesDaoust's solution effectively calculates the crossing number of the point.
There is an unlimited number of points inside a rectangle, so you have to define a
step with (distane between two points).
You could just iterate with two nested loops,
lat, lon coordinates can be converted to integer using a multiplication factor of:
multiply with 1E7 (10000000) to get maximum acuracy of 1cm, or
10000000: 1cm
1000000: 10cm
100000: 1m
10000: 10m
1000: 100m
100: 1km
10: 11km
1: 111km
Now iterate
// convert to spherical integer rectangle
double toIntFact = 1E7;
int x = (int) (x0 * toIntFact);
int y = (int) (y0 * toIntFact);
int tx1 = x1 * toIntFact;
int ty1 = y1 * toIntFact;
int yStep = 100000; // about 1.11 m latitudinal span. choose desired step above in list
int xStep = (int) (yStep / cos(Math.toRadians(y0))); // longitude adaption factor depending of cos(latitude); more acurate (symetric) is to use cos of centerLatitude: (y0 + y1) / 2;
for (int px = x; px < tx1; px+= xStep) {
for (int py = y; py < ty1; py+= yStep) {
drawPoint(px, py); // or whatever
}
}
This should give an point set with same distances inbetween point for about some kilometer wide rectangles.
The code does not work when overlapping the Datum limit (-180 to 180 jump) or
when overlapping the poles. Delivers useable results up to latitude 80° N or S.
This code uses some kind of implicit equidistant (equirectangular) projection (see the division by cos(centerLat) to correct the fact that 1 degree of latitude is another distance measured in meters than one degree of longitude.
If the size of the rectangle exceeds some ten or hundred kilomters, then depending on your requirements have to use an advanced projection: e.g convert lat, lon with an WGS84 to UTM conversion. The result are coordinates in meters, which then you iterate analog.
But are you sure that this is what you want?
Nobody wants to find all atoms inside a rectangle.
May all screen pixels, or a method isInsideRectangle(lat,lon, Rectangle);
So think again for what you need that.
I'm trying to deduct the 2D-transformation parameters from the result.
Given is a large number of samples in an unknown X-Y-coordinate system as well as their respective counterparts in WGS84 (longitude, latitude). Since the area is small, we can assume the target system to be flat, too.
Sadly I don't know which order of scale, rotate, translate was used, and I'm not even sure if there were 1 or 2 translations.
I tried to create a lengthy equation system, but that ended up too complex for me to handle. Basic geometry also failed me, as the order of transformations is unknown and I would have to check every possible combination order.
Is there a systematic approach to this problem?
Figuring out the scaling factor is easy, just choose any two points and find the distance between them in your X-Y space and your WGS84 space and the ratio of them is your scaling factor.
The rotations and translations is a little trickier, but not nearly as difficult when you learn that the result of applying any number of rotations or translations (in 2 dimensions only!) can be reduced to a single rotation about some unknown point by some unknown angle.
Suddenly you have N points to determine 3 unknowns, the axis of rotation (x and y coordinate) and the angle of rotation.
Calculating the rotation looks like this:
Pr = R*(Pxy - Paxis_xy) + Paxis_xy
Pr is your rotated point in X-Y space which then needs to be converted to WGS84 space (if the axes of your coordinate systems are different).
R is the familiar rotation matrix depending on your rotation angle.
Pxy is your unrotated point in X-Y space.
Paxis_xy is the axis of rotation in X-Y space.
To actually find the 3 unknowns, you need to un-scale your WGS84 points (or equivalently scale your X-Y points) by the scaling factor you found and shift your points so that the two coordinate systems have the same origin.
First, finding the angle of rotation: take two corresponding pairs of points P1, P1' and P2, P2' and write out
P1' = R(P1-A) + A
P2' = R(P2-A) + A
where I swapped A = Paxis_xy for brevity. Subtracting the two equations gives:
P2'-P1' = R(P2-P1)
B = R * C
Bx = cos(a) * Cx - sin(a) * Cy
By = cos(a) * Cx + sin(a) * Cy
By + Bx = 2 * cos(a) * Cx
(By + Bx) / (2 * Cx) = cos(a)
...
(By - Bx) / (2 * Cy) = sin(a)
a = atan2(sin(a), cos(a)) <-- to get the right quadrant
And you have your angle, you can also do a quick check that cos(a) * cos(a) + sin(a) * sin(a) == 1 to make sure either you got all the calculations correct or that your system really is an orientation-preserving isometry (consists only of translations and rotations).
Now that we know a we know R and so to find A we do:
P1` = R(P1-A) + A
P1' - R*P1 = (I-R)A
A = (inverse(I-R)) * (P1' - R*P1)
where the inversion of a 2x2 matrix is easy.
EDIT: There is an error in the above, or more specifically one case that needs to be treated separately.
There is one combination of translations and rotations that does not reduce to a single rotation and that is a single translation. You can think of it in terms of fixed points (how many points are unchanged after the operation).
A translation has no fixed points (all points are changed) and a rotation has 1 fixed point (the axis doesn't change). It turns out that two rotations leave 1 fixed point and a translation and a rotation leaves 1 fixed point, which (with a little proof that says the number of fixed points tells you the operation performed) is the reason that arbitrary combinations of these result in a single rotation.
What this means for you is that if your angle comes out as 0 then using the method above will give you A = 0 as well, which is likely incorrect. In this case you have to do A = P1' - P1.
If I understood the question correctly, you have n points (X1,Y1),...,(Xn,Yn), the corresponding points, say, (x1,y1),...,(xn,yn) in another coordinate system, and the former are supposedly obtained from the latter by rotation, scaling and translation.
Note that this data does not determine the fixed point of rotation / scaling, or the order in which the operations "should" be applied. On the other hand, if you know these beforehand or choose them arbitrarily, you will find a rotation, translation and scaling factor that transform the data as supposed to.
For example, you can pick an any point, say, p0 = [X1, Y1]T (column vector) as the fixed point of rotation & scaling and subtract its coordinates from those of two other points to get p2 = [X2-X1, Y2-Y1]T, and p3 = [X3-X1, Y3-Y1]T. Also take the column vectors q2 = [x2-x1, y2-y1]T, q3 = [x3-x1, y3-y1]T. Now [p2 p3] = A*[q2 q3], where A is an unknwon 2x2 matrix representing the roto-scaling. You can solve it (unless you were unlucky and chose degenerate points) as A = [p2 p3] * [q2 q3]-1 where -1 denotes matrix inverse (of the 2x2 matrix [q2 q3]). Now, if the transformation between the coordinate systems really is a roto-scaling-translation, all the points should satisfy Pk = A * (Qk-q0) + p0, where Pk = [Xk, Yk]T, Qk = [xk, yk]T, q0=[x1, y1]T, and k=1,..,n.
If you want, you can quite easily determine the scaling and rotation parameter from the components of A or combine b = -A * q0 + p0 to get Pk = A*Qk + b.
The above method does not react well to noise or choosing degenerate points. If necessary, this can be fixed by applying, e.g., Principal Component Analysis, which is also just a few lines of code if MATLAB or some other linear algebra tools are available.
I want to improve a collision system.
Right now I detect if 2 irregular objects collide if their bounding rectangles collide.
I want to obtain the for rectangle the corresponding ellipse while for the other one to use a circle. I found a method to obtain the ellipse coordinates but I have a problem when I try to detect if it intersects the circle.
Do you know a algorithm to test if a circle intersects an ellipse?
Short answer: Solving exactly for whether the two objects intersect is complicated enough to be infeasible for the purpose of collision detection. Discretize your ellipse as an n-sided polygon for some n (depending on how accurate you need to be) and do collision detection with that polygon.
Long answer: If you insist on determining if the smooth ellipse and circle intersect, there are two main approaches. Both involve solving first for the closest point to the circle's center on the ellipse, and then comparing that distance to the circle's radius.
Approach 1: Use a parametrization of the ellipse. Transform your coordinates so that the ellipse is at the origin, with its axes aligned to the x-y axes. That is:
Center of ellipse: (0,0)
Center of circle: c = (cx, cy)
Radius of circle: r
Radius of x-aligned axis of ellipse: a
Radius of y-aligned axis of ellipse: b.
The equation of the ellipse is then given by a cos(t), b sin(t). To find the closest point, we want to minimize the square distance
|| (a cos t, b sin t) - c ||^2. As Jean points out, this is "just calculus": take a derivative, and set it equal to 0. Unless I'm missing something, though, solving the resulting (quite nasty) equation for t is not possible analytically, and must be approximated using e.g. Newton's Method.
Plug in the t you find into the parametric equation to get the closest point.
Pro: Numerical solve is only in one variable, t.
Con: You must be able to write down a parametrization of the ellipse, or transform your coordinates so that you can. This shouldn't be too hard for any reasonable representation you have of the ellipse. However, I'm going to show you a second method, which is much more general and might be useful if you have to generalize your problem to, say, 3D.
Approach 2: Use multidimensional calculus. No change of coordinates is necessary.
Center of circle: c = (cx, cy)
Radius of cirlce: r
Ellipse is given by g(x, y) = 0 for a function g. For instance, per Curd's answer you might use g(x,y) = distance of (x,y) from focus 1 + distance of (x,y) from focus 2 - e.
Finding the point on the ellipse closest to the center of the circle can then be phrased as a constrained minimization problem:
Minimize ||(x,y) - c||^2 subject to g(x,y) = 0
(Minimizing the square distance is equivalent to minimizing the distance, and much more pleasant to deal with since it's a quadratic polynomial in x,y.)
To solve the constrained minimization problem, we introduce Lagrange multiplier lambda, and solve the system of equations
2 * [ (x,y) -c ] + lambda * Jg(x,y) = 0
g(x,y) = 0
Here Jg is the gradient of g. This is a system of three (nonlinear) equations in three unknowns: x, y, and lambda. We can solve this system using Newton's Method, and the (x,y) we get is the closest point to the circle's center.
Pro: No parametrization needs to be found
Pro: Method is very general, and works well whenever writing g is easier than finding a parametric equation (such as in 3D)
Con: Requires a multivariable Newton solve, which is very hairy if you don't have access to a numerical method package.
Caveat: both of these approaches technically solve for the point which extremizes the distance to the circle's center. Thus the point found might be the furthest point from the circle, and not the closest. For both methods, seeding your solve with a good initial guess (the center of the circle works well for Method 2; you're on your own for Method 1) will reduce this danger.
Potential Third Approach?: It may be possible to directly solve for the roots of the system of two quadratic equations in two variables representing the circle and ellipse. If a real root exists, the objects intersect. The most direct way of solving this system, again using a numerical algorithm like Newton's Method, won't help because lack of convergence does not necessary imply nonexistence of a real root. For two quadratic equations in two variables, however, there may exist a specialized method that's guaranteed to find real roots, if they exist. I myself can't think of a way of doing this, but you may want to research it yourself (or see if someone on stackoverflow can elaborate.)
An ellipse is defined a the set of points whose
sum of the distance to point A and the distance to point B is constant e.
(A and B are called the foci of the ellipse).
All Points P, whose sum AP + BP is less than e, lie within the ellipse.
A circle is defined as the set of points whose
distance to point C is r.
A simple test for intersection of circle and ellipse is following:
Find
P as the intersection of the circle and the line AC and
Q as the intersection of the circle and the line BC.
Circle and ellipse intersect (or the circle lies completely within the ellipse) if
AP + BP <= e or AQ + BQ <= e
EDIT:
After the comment of Martin DeMello and adapting my answer accordingly I thought more about the problem and found that the answer (with the 2nd check) still doesn't detect all intersections:
If circle and ellipse are intersecting only very scarcely (just a little more than being tangent) P and Q will not lie within the ellipse:
So the test described above detects collision only if the overlap is "big enough".
Maybe it is good enough for your practical purposes, although mathematically it is not perfect.
I know that it's too late but I hope it would help somebody. My approach to solve this problem was to interpolate the ellipse into an n-dimensions polygon, then to construct a line between every 2 points and find whether the circle intersects with any of the lines or not. This doesn't provide the best performance, but it is handy and easy to implement.
To interpolate the ellipse to an n-dimensions polygon, you can use:
float delta = (2 * PI) / n;
std::vector<Point*> interpolation;
for(float t = 0; t < (2 * PI); t += delta) {
float x = rx * cos(t) + c->get_x();
float y = ry * sin(t) + c->get_y();
interpolation.push_back(new Point(x, y));
}
c: The center of the ellipse.
rx: The radius of x-aligned axis of the ellipse.
ry: The radius of y-aligned axis of the ellipse.
Now we have the interpolation points, we can find the intersection between the circle and the lines between every 2 points.
One way to find the line-cricle intersection is described here,
an intersection occurs if an intersection occurred between any of the lines and the circle.
Hope this helps anybody.
find the point on the ellipse closest to the center of the circle
and then check if the distance from this point is smaller than the radius of the circle
if you need help doing this just comment, but it's simply calculus
edit: here's a ways towards the solution, since there is something wrong with curds
given center α β on the ellipse
and (for lack of remembering the term) x radius a, y radius b
the parametrization is
r(Θ) = (ab)/( ( (BcosΘ)^2 + (asinΘ)^2 )^.5)
x(Θ) = α + sin(Θ)r(Θ)
y(Θ) = β + cos(Θ)r(Θ)
and then just take the circle with center at (φ, ψ) and radius r
then the distance d(Θ) = ( (φ - x(Θ))^2 + (ψ - y(Θ) )^2)^.5
the minimum of this distance is when d'(Θ) = 0 (' for the derivative)
d'(Θ) = 1/d(Θ) * (-φx'(Θ) + x(Θ)x'(Θ) - ψy'(Θ) + y(Θ)y'(Θ) )
==>
x'(Θ) * (-φ + x(Θ)) = y'(Θ) * (ψ - y(Θ))
and keep going and going and hopefully you can solve for Θ
The framework you're working in might have things to help you solve this, and you could always take the easy way out and approximate roots via Newton's Method
if a circle and an ellipse collide, then either their boundaries intersect 1, 2, 3, or 4 times(or infinitely many in the case of a circular ellipse that coincides with the circle), or the circle is within the ellipse or vice versa.
I'm assuming the circle has an equation of (x - a)^2 + (y - b)^2 <= r^2 (1) and the ellipse has an equation of [(x - c)^2]/[d^2] + [(y - e)^2]/[f^2] <= 1 (2)
To check whether one of them is inside the other, you can evaluate the equation of the circle at the coordinates of the center of the ellipse(x=c, y=e), or vice versa, and see if the inequality holds.
to check the other cases in which their boundaries intersect, you have to check whether the system of equations described by (1) and (2) has any solutions.
you can do this by adding (1) and (2), giving you
(x - a)^2 + (y - b)^2 + [(x - c)^2]/[d^2] + [(y - e)^2]/[f^2] = r^2 + 1
next you multiply out the terms, giving
x^2 - 2ax + a^2 + y^2 - 2by + b^2 + x^2/d^2 - 2cx/d^2 + c^2/d^2 + y^2/f^2 - 2ey/f^2 + e^2/f^2 = r^2 + 1
collecting like terms, we get
(1 + 1/d^2)x^2 - (2a + 2c/d^2)x + (1 + 1/f^2)y^2 - (2b + 2e/f^2)y = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2
now let m = (1 + 1/d^2), n = -(2a + 2c/d^2), o = (1 + 1/f^2), and p = -(2b + 2e/f^2)
the equation is now mx^2 + nx + oy^2 + py = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2
now we need to complete the squares on the left hand side
m[x^2 + (n/m)x] + o[y^2 + (p/o)y] = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2
m[x^2 + (n/m)x + (n/2m)^2 - (n/2m)^2] + o[y^2 + (p/o)y + (p/2o)^2 - (p/2o)^2] = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2
m[(x + n/2m)^2 - (n/2m)^2] + o[(y + p/2o)^2 - (p/2o)^2] = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2
m(x + n/2m)^2 - m(n/2m)^2 + o(y + p/2o)^2 - o(p/2o)^2 = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2
m(x + n/2m)^2 + o(y + p/2o)^2 = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2 + m(n/2m)^2 + o(p/2o)^2
this system has a solution iff 11 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2 + m(n/2m)^2 + o(p/2o)^2 >= 0
There you have it, if I didn't make any algebraic mistakes. I don't know how much you can simplify the resulting expression, so this solution might be quite computationally expensive if you're going to check for many circles/ellipses
Enlarge the ellipse's major and minor radii by the radius of the circle. Then test if the center of the given circle is within this new larger ellipse by summing the distances to the foci of the enlarged ellipse.
This algorithm is quite efficient. You can early-out if the given circle doesn't intersect a circle which circumscribes the ellipse. This is slower than a bounding box test, but finding the bounding box of a non-axis-aligned ellipse is tricky.
Forget about a mathematical solution. As you can easily see by drawing, you can have up to four solutions, and thus likely a fourth grade polynomial.
Instead just do a binary search along the edge of one of the figures. It is easy to determine if a point lies within an ellipse and even more so in a circle (just see if distance is shorter than radius).
If you really want to go for the maths, Wolfram MathWorld has a nice article here: http://mathworld.wolfram.com/Circle-EllipseIntersection.html but be warned, you'll still have to write a polynomial equation solver, probably using something like binary search.
Supposing:
the ellipse is centred at the origin and with the semi-major
axis (of length a) oriented along the x axis, and with a semi-minor
axis of length b; E2 is the eccentricity squared, ie (aa-bb)/(a*a);
the circle is centred at X,Y and of radius r.
The easy cases are:
the circle centre is inside the ellipse (ie hypot(X/a, Y/b) <= 1)
so there is an intersection;
the circle centre is outside a circle centred at 0 of radius a+r
(ie hypot(X,Y) > a+r) so there isn't an intersection.
One approach for the other cases is to compute the geodetic
coordinates (latitude, height) of the circle centre. The circle
intersects the ellipse if and only if the height is less than the radius.
The geodetic latitude of a point on an ellipse is the angle
the normal to the ellipse at the point makes with the x axis, and
the height of a point outside the ellipse is the distance of the
point from the point on the ellipse closest to it. Note the geodetic latitude is not same as the polar angle from the ellipse centre to the point unless the
ellipse is in fact circular.
In formulae the conversion from geodetic coordinates lat,ht to
cartesian coordinates X,Y is
X = (nu+ht)*cos(lat), Y = (nu * (1-E2) + ht)*sin(lat)
where nu = a/sqrt( 1 - E2*sin(lat)sin(lat)).
The point on the ellipse closest to X,Y is the point
with the same latitude, but zero height, ie x = nucos(lat),
y = nu * (1-E2) * sin(lat).
Note that nu is a function of latitude.
Unfortunately the process of finding lat,ht from X,Y is an
iterative one. One approach is to first find the latitude, and then
the height.
A little algebra shows that the latitude satisfies
lat = atan2( Y+ E2*nusin(lat), X)
which can be used to compute successive approximations to the latitude,
starting at lat = atan2( Y, X(1.0-E2)), or (more efficiently) can be
solved using newton's method.
The larger E2 is, ie the flatter the ellipse is, the more
iterations will be required. For example if the ellipse is nearly
circular (say E2<0.1) then five iterations will get x,y below
to within a*1e-12, but if the ellipse is very flat, e.g. E2=0.999
you'll need around 300 iterations to get the same accuracy!
Finally, given the latitude, the height can be computed
by computing (x,y):
x = nucos(lat), y = nu(1-E2)*sin(lat)
and then h is the distance from x,y to the circle centre,
h = hypot( X-x, Y-y)
This isn't that hard. user168715's answer is generally right, but doing calculus isn't necessary. Just trigonometry.
Find the angle between the center of the two objects. Using this you can find the closest point to the circle's center on the ellipse using the polar-form:
(Taken from Wikipedia article on Ellipses)
Now compare the distance between the two object centers, subtracting the ellipse radius and circle radius.
Maybe I'm missing something; maybe ArcTan/Cos/Sin are slow -- but I don't think so, and there should be fast-approximations if needed.
I wanted to provide some input into the more general problem involving contact between two ellipses. Calculating the distance of closest approach of two ellipses was a long standing problem and was only solved analytically within the last ten years-it is by no means simple. The solution to the problem may be found here http://www.e-lc.org/docs/2007_01_17_00_46_52/.
The general method to determine if there is contact between two ellipses is to first calculate the distance of closest approach of the ellipses in their current configuration and then subtract this from their current magnitude of separation. If this result is less than or equal to 0, then they are in contact.
If anyone is interested I can post code that calculates the distance of closest approach--it's in C++. The code is for the general case of two arbitrary ellipses, but you can obviously do it for a circle and ellipse, since a circle is an ellipse with equal minor and major axes.