I'm using Three.js and Cannon.js.
Also, I'm trying to reproduce this object (the multiball one).
Basically, the ball goes from its origin to the pressure point and stops, or rotates around the pressure point (this is called mass-spring).
For the moment I was able to set the direction of the ball via velocity but the ball doesn't stop at the pressure point and the more distance there are the higher the velocity is (I need constant velocity).
I've asked the question on Github and someone told me to have a look at this example. The only problem is that it only rotates around a planet where I'm searching how to move a ball to a certain point which doesn't sound to be the same. Any help is greatly appreciated
Maybe you need a physics model like this one:
friction model
m = 2 (it could be also m = 1 or m = 3 or whatever looks good)
coefficients:
cf = coefficient of friction
cs = coefficient of spring
Dynamics / update of position and velocity with time-increment dt
x_cursor = x coordinate of cursor's position
y_cursor = y coordinate of cursor's position
x = x + dt * v_x
y = y + dt * v_y
v_norm = sqrt(v_x^2 + v_y^2)^(m-1)
v_x = v_x - dt * cf * v_norm * v_x - dt * cs * ( x - x_cursor )
v_y = v_y - dt * cf * v_norm * v_y - dt * cs * ( y - y_cursor )
Related
I can't quite figure this one out.
I am trying to approximate the location (latitude / longitude) of a beacon based on 3 distance measurements from 3 fixed locations. However the distance readings available may have an error of up to 1 km.
Similar questions regarding trilateration have been asked here (with precise measurements), here, here (distance measurement errors in Java, but not in lat/lon coordinates and no answers) as well as others. I also managed to dig up this paper dealing with imperfect measurement data, however it for one assumes a cartesian coordinate system and is also rather mathematical than close to a usable implementation.
So none of above links and answers are really applicable to the following problem:
All available distance measurements are approximated in km (where data most frequently contains readings in-between 1 km and 100 km, in case this matters)
Measurement errors of up to 1 km are possible.
3 distance measurements are performed based on 3 fixed (latitude / longitude known) positions.
target approximation should also be a latitude / longitude combination.
So far I have adapted this Answer to C#, however I noticed that due to the measurement inaccuracies this algorithm does not work (as the algorithm assumes the 3 distance-circles to perfectly intersect with each other):
public static class Trilateration
{
public static GeoLocation Compute(DistanceReading point1, DistanceReading point2, DistanceReading point3)
{
// not my code :P
// assuming elevation = 0
const double earthR = 6371d;
//using authalic sphere
//if using an ellipsoid this step is slightly different
//Convert geodetic Lat/Long to ECEF xyz
// 1. Convert Lat/Long to radians
// 2d. Convert Lat/Long(radians) to ECEF
double xA = earthR * (Math.Cos(Radians(point1.GeoLocation.Latitude)) * Math.Cos(Radians(point1.GeoLocation.Longitude)));
double yA = earthR * (Math.Cos(Radians(point1.GeoLocation.Latitude)) * Math.Sin(Radians(point1.GeoLocation.Longitude)));
double zA = earthR * Math.Sin(Radians(point1.GeoLocation.Latitude));
double xB = earthR * (Math.Cos(Radians(point2.GeoLocation.Latitude)) * Math.Cos(Radians(point2.GeoLocation.Longitude)));
double yB = earthR * (Math.Cos(Radians(point2.GeoLocation.Latitude)) * Math.Sin(Radians(point2.GeoLocation.Longitude)));
double zB = earthR * (Math.Sin(Radians(point2.GeoLocation.Latitude)));
double xC = earthR * (Math.Cos(Radians(point3.GeoLocation.Latitude)) * Math.Cos(Radians(point3.GeoLocation.Longitude)));
double yC = earthR * (Math.Cos(Radians(point3.GeoLocation.Latitude)) * Math.Sin(Radians(point3.GeoLocation.Longitude)));
double zC = earthR * Math.Sin(Radians(point3.GeoLocation.Latitude));
// a 64 bit Vector3 implementation :)
Vector3_64 P1 = new(xA, yA, zA);
Vector3_64 P2 = new(xB, yB, zB);
Vector3_64 P3 = new(xC, yC, zC);
//from wikipedia
//transform to get circle 1 at origin
//ransform to get circle 2d on x axis
Vector3_64 ex = (P2 - P1).Normalize();
double i = Vector3_64.Dot(ex, P3 - P1);
Vector3_64 ey = (P3 - P1 - i * ex).Normalize();
Vector3_64 ez = Vector3_64.Cross(ex, ey);
double d = (P2 - P1).Length;
double j = Vector3_64.Dot(ey, P3 - P1);
//from wikipedia
//plug and chug using above values
double x = (Math.Pow(point1.DistanceKm, 2d) - Math.Pow(point2.DistanceKm, 2d) + Math.Pow(d, 2d)) / (2d * d);
double y = ((Math.Pow(point1.DistanceKm, 2d) - Math.Pow(point3.DistanceKm, 2d) + Math.Pow(i, 2d) + Math.Pow(j, 2d)) / (2d * j)) - ((i / j) * x);
// only one case shown here
double z = Math.Sqrt(Math.Pow(point1.DistanceKm, 2d) - Math.Pow(x, 2d) - Math.Pow(y, 2d));
//triPt is a vector with ECEF x,y,z of trilateration point
Vector3_64 triPt = P1 + x * ex + y * ey + z * ez;
//convert back to lat/long from ECEF
//convert to degrees
double lat = Degrees(Math.Asin(triPt.Z / earthR));
double lon = Degrees(Math.Atan2(triPt.Y, triPt.X));
return new GeoLocation(lat, lon);
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private static double Radians(double degrees) =>
degrees * Math.Tau / 360d;
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private static double Degrees(double radians) =>
radians * 360d / Math.Tau;
}
Above code most often than not does not work in my case, and instead only returns "Not a number" as it tries to take the square root of a negative number when calculating the final z value (due to measurement inaccuracies).
In my case measurements may return data like this (visualized with some random online tool):
where only 2 or even none of the distance circles intersect:
What I am looking for is the an algorithm returning the best possible approximation of the target location based on three distance measurements with a known maximum error of 1 km or further approaches I could take.
I have also thought of iterating over points on the circles to then determining the minimum average distance to all the points on the other circles but the 3-dimensional sphere geometry of the earth is giving me a headache. Also there's probably a way better and simpler approach to this which I just can't figure out right now.
As this is more of an algorithmic problem, rather than any language-specific thing, I appreciate any help in whatever programming language, pseudo code or natural language.
If you have access to a scientific computing library which provides non-linear optimization utilities, then you could try finding the point which minimizes the following:
(||x - p_1|| - r_1)^2 + (||x - p_2|| - r_2)^2 + (||x - p_3|| - r_3)^2 + (||x - p_earth|| - r_earth)^2
where p_i is the location (in Cartesian coordinates) of the ith location you measure from, r_i is the corresponding distance reading, p_earth is the location of the Earth, r_earth is the radius of the earth, and ||a|| denotes the norm/length of the vector a.
Each term in the expression is trying to minimize the residual radius error.
This can of course be modified to suit your needs - e.g. if constrained optimization is available, you could encode the requirement that the point be on the surface on the earth as a constraint rather than a term to optimize for. If spherical earth model isn't accurate enough, you could define an error from the Earth's surface, or just project your result onto the Earth if that is accurate enough.
I am trying to make boids algorithm in Unity 3D.
I got into one problem: How to implement field of view in specific angle?
360 degrees is easy - U check only distance between two boids. But I dont want boids able to look behind themself. I also want to be able to change angle of view in Inspector, so it must be based on calculations.
I would be gratefull for any ideas:(
I already tried with mesh collider which is cone but it didnt go well. - not working for 180 and higher. So I am looking best way to calculate this.
Assume that the boid is at point p = (p.x, p.y, p.z) heading toward some point h = (h.x, h.y, h.z), and we want to know whether an object at point q = (q.x, q.y, q.z) is in the boid's field of vision.
The Law of Cosines gives us a formula for the cosine of the angle φ between the boid's heading and the boid's path to the object:
(h−p) · (q−p)
cos(φ) = ---------------
||h−p|| ||q−p||
= (dx1*dx2 + dy1*dy2 + dz1*dz2) /
(sqrt(dx1*dx1 + dy1*dy1 + dz1*dz1) * sqrt(dx2*dx2 + dy2*dy2 + dz2*dz2))
where
dx1 = h.x - p.x
dy1 = h.y - p.y
dz1 = h.z - p.z
dx2 = q.x - p.x
dy2 = q.y - p.y
dz2 = q.z - p.z
Given some angle ρ (in whatever units your cosine function accepts, usually radians) past which the boid cannot see (putting the field of vision at 2ρ), we have
φ > ρ if and only if cos(φ) < cos(ρ),
so we can precompute cos(ρ) and then use the above formula for repeated tests.
To avoid division by zero and other numerical problems, you might want to check whether the denominator of the division is very small and if so declare that the boid can feel whatever the object is even outside its field of vision.
I have two known Google Geolocation points A and B. I need to return GeoLocation point C which is on AB line and on distance x from point A:
Geolocation returnGeolocationC(Geolocation A, Geolocation B, double x) {
...
return C;
}
I know that I can use Haversine formula and I can calculate AB distance and therefore I also have AC and CB distance. Any idea or hint how to implement this?
Edit: Line is straight, no need to consider roads.
Well, this is a good problem which solution will depend on the area of interest, for instance:
Consider the situation faced by a botanist studying a stand of oak trees on a small plot of land. One component of the data analysis involves determining the location of these trees and calculating the distance betwee
n them. In this situation, straight line or Euclidean distance is the most logical choice. This only requires the use of the Pythagorean Theorem to calculate the shortest distance between two points:
straight_line_distance = sqrt ( ( x2 - x1 )**2 + ( y2 - y1 )**2 );
The variables x and y refer to co-ordinates in a two-dimensional plane and can reflect any unit of measurement, such as feet or miles.
Consider a different situation, an urban area, where the objective is to calculate the distance between customers’ homes and various retail outlets. In this situation, distance takes on a more specific meaning, usually road distance, making straight line distance less suitable. Since streets in many cities are based on a grid system, the typical trip may be approximated by what is known as the Manhattan, city block or taxi cab distance (Fothering-
ham, 2002):
block_distance = ( abs( x2 - x1 ) + abs( y2 - y1 ) ) ;
Instead of the hypotenuse of the right-angled triangle that was calculated for the straight line distance, the above formula simply adds the two sides that form the right angle. The straight line and city block formulae are closely related, and can be generalized by what are referred to as the Minkowski metrics, which in this case are restricted to two dimensions:
minkowski_metric = ( abs(x2 - x1)**k + abs(y2 - y1)**k )**(1/k);
The advantage of this formula is that you only need to vary the exponent to get a range of distance measures. When k = 1, it is equivalent to the city block distance; when k=2, it is the Euclidean distance. Less commonly,
other values of k may be used if desired, usually between 1 and 2. In some situations, it may have been determined that actual distances were greater than the straight line, but less than the city block, in which case a value such as "1.4" may be more appropriate. One of the interesting features of the Minkowski metric is that for values considerably larger than 2 (approaching infinity), the distance is the larger of two sides used in the city block calculation, although this is typically not applicable in a geographic context.
So pseudocode would be something like the following:
distance2d (x1, y1, x2, y2, k)
(max( abs(x2 - x1), abs(y2 - y1) ) * (k > 2))
+
((abs(x2 - x1)**k + abs(y2 - y1)** k )**(1/ k)) * (1 <=k<=2)
end
If 1 <= k <=2, the basic Minkowski metric is applied, since (1 <= k <=2) resolves to 1 and (k > 2) resolves to 0. If k > 2, an alternate formula is applied, since computations become increasingly intensive for large values of k. This second formula is not really necessary, but is useful in demonstrating how modifications can be easily incorporated in distance measures.
The previous distance measures are based on the concept of distance in two dimensions. For small areas like cities or counties, this is a reasonable implification. For longer distances such as those that span larger countries
or continents, measures based on two dimensions are no longer appropriate, since they fail to account for the curvature of the earth. Consequently, global distance measures need to use the graticule, the co-ordinate system
comprised of latitude and longitude along with special formulae to calculate the distances. Lines of latitude run in an east to west direction either above or below the equator. Lines of longitude run north and south through the poles, often with the Prime Meridian (running through Greenwich, England) measured at 0°. Further details of latitude and longitude are available (Slocum et al., 2005). One issue with using latitude and longitude is that the co-ordinates may require some transformation and preparation before they are suitable to use in distance calculations. Coordinates are often expressed in the sexagesimal system (similar to time) of degrees, minutes, and seconds, in which each degree consists of 60 minutes and each
minute is 60 seconds. Furthermore, it is also necessary to provide and indication of the position relative to the equator (North or South) and the Prime Meridian (East or West). The full co-ordinates may take on a variety of formats; below is a typical example that corresponds approximately to the city of Philadelphia:
39° 55' 48" N 75° 12' 12" W
As you mentioned Harvesine, and also I am extending a lot, we can compare results using law of cosines and Harvesine, so pseudocode:
begin
ct = constant('pi')/180 ;
radius = 3959 ; /* 6371 km */
#Both latitude and longitude are in decimal degrees ;
lat1 = 36.12;
long1 = -86.67;
lat2 = 33.94;
long2 = -118.40 ;
#Law of Cosines ;
a = sin(lat1*ct) * sin(lat2*ct) ;
b = cos(lat1*ct) * cos(lat2*ct) * cos((long2-long1) *ct);
c = arcos(a + b) ;
d = radius * c ;
put 'Distance using Law of Cosines ' d
# Haversine ** ;
a2 = sin( ((lat2 - lat1)*ct)/2)**2 +
cos(lat1*ct) * cos(lat2*ct) * sin(((long2 - long1)*ct)/2)**2
c2 = 2 * arsin(min(1,sqrt(a2))) ;
d2 = radius * c2 ;
put 'Distance using Haversine formula =' d2
end
In addition to the constant that will be used to convert degrees to radians, the radius of the earth is required, which on average is equal to 6371 kilometres or 3959 miles. The Law of Cosines uses spherical geometry to
calculate the great circle distance for two points on the globe. The formula is analogous to the Law of Cosines for plane geometry, in which three connected great arcs correspond to the three sides of the triangle. The Haversine formula is mathematically equivalent to the Law of Cosines, but is often preferred since it is less sensitive to round-off error that can occur when measuring distances between points that are located very close tog
ether (Sinnott, 1984). With the Haversine, the error can occur for points that are on opposite sides of the earth, but this is usually less of a problem.
You can find a really easy formula at this link.
Since you have the distance from one of the points and not the fraction of the distance on the segment you can slightly modify the formula:
A=sin(d-x)/sin(d)
B=sin(x)/sin(d)
x = A*cos(lat1)*cos(lon1) + B*cos(lat2)*cos(lon2)
y = A*cos(lat1)*sin(lon1) + B*cos(lat2)*sin(lon2)
z = A*sin(lat1) + B*sin(lat2)
lat=atan2(z,sqrt(x^2+y^2))
lon=atan2(y,x)
where x is the required distance and d is the distance between A and B (that you can evaluate with Haversine), both divided by the Earth radius.
You can also use another formula for sin(d):
nx = cos(lat1)*sin(lon1)*sin(lat2) - sin(lat1)* cos(lat2)*sin(lon2)
ny = -cos(lat1)*cos(lon1)*sin(lat2) + sin(lat1)* cos(lat2)*cos(lon2)
nz = cos(lat1)*cos(lon1)*cos(lat2)*sin(lon2) - cos(lat1)*sin(lon1)*cos(lat2)*cos(lon2)
sind = sqrt(nx^2+ny^2+nz^2)
It's more complex than the Haversine formula, but you can memoize some of the factors in the two steps.
As the OP posted a non working Java implementation, this is my corrections to make it work.
private static GpsLocation CalcGeolocationWithDistance(GpsLocation pointA, GpsLocation pointB, double distanceFromA)
{ //distanceFromA = 2.0 km, PointA and PointB are in Europe on 4.0km distance.
double earthRadius = 6371000.0;
double distanceAB = CalcDistance(pointA.Latitude, pointA.Longitude, pointB.Latitude, pointB.Longitude);
//distance AB is calculated right according to Google Maps (4.0 km)
double a = Math.Sin((distanceAB - distanceFromA) / earthRadius) / Math.Sin(distanceAB / earthRadius);
double b = Math.Sin(distanceFromA / earthRadius) / Math.Sin(distanceAB / earthRadius);
double x = a * Math.Cos(pointA.Latitude * Math.PI / 180) * Math.Cos(pointA.Longitude * Math.PI / 180) + b * Math.Cos(pointB.Latitude * Math.PI / 180) * Math.Cos(pointB.Longitude * Math.PI / 180);
double y = a * Math.Cos(pointA.Latitude * Math.PI / 180) * Math.Sin(pointA.Longitude * Math.PI / 180) + b * Math.Cos(pointB.Latitude * Math.PI / 180) * Math.Sin(pointB.Longitude * Math.PI / 180);
double z = a * Math.Sin(pointA.Latitude * Math.PI / 180) + b * Math.Sin(pointB.Latitude * Math.PI / 180);
double lat = Math.Atan2(z, Math.Sqrt(x * x + y * y)) * 180 / Math.PI;
double lon = Math.Atan2(y, x) * 180 / Math.PI;
//lat and lon are mo more placed somewhere in Africa ;)
return new GpsLocation(lat, lon);
}
Background:
I'm working with transport routes and Google provides Route points far apart enough to create 'shapes'. These are the bus/train routes you see in Google Maps.
My Requirement:
Google's points are far enough to create straight lines. However I want a point every, say, 5 metres.
Problem:
So, say I have two points [lat,long]:
[-33.8824219918503,151.206686052582] and [-33.8815434600467,151.206556440037]
Given those two points I can calculate the distance between them. Say it's 1km for the sake of argument.
So we can imagine an imaginary straight line in between those two points.
How do I generate coordinates for that imaginary line for every, say, 5 metres?
Destination point given distance and bearing from start point applied to your problem:
class Numeric
def to_rad
self * Math::PI / 180
end
def to_deg
self * 180 / Math::PI
end
end
include Math
R = 6371.0
def waypoint(φ1, λ1, θ, d)
φ2 = asin( sin(φ1) * cos(d/R) + cos(φ1) * sin(d/R) * cos(θ) )
λ2 = λ1 + atan2( sin(θ) * sin(d/R) * cos(φ1), cos(d/R) - sin(φ1) * sin(φ2) )
λ2 = (λ2 + 3 * Math::PI) % (2 * Math::PI) - Math::PI # normalise to -180..+180°
[φ2, λ2]
end
φ1, λ1 = -33.to_rad, -71.6.to_rad # Valparaíso
φ2, λ2 = 31.4.to_rad, 121.8.to_rad # Shanghai
d = R * acos( sin(φ1) * sin(φ2) + cos(φ1) * cos(φ2) * cos(λ2 - λ1) )
θ = atan2( sin(λ2 - λ1) * cos(φ2), cos(φ1) * sin(φ2) - sin(φ1) * cos(φ2) * cos(λ2 - λ1) )
waypoints = (0..d).step(2000).map { |d| waypoint(φ1, λ1, θ, d) }
markers = waypoints.map { |φ, λ| "#{φ.to_deg},#{λ.to_deg}" }.join("|")
puts "http://maps.googleapis.com/maps/api/staticmap?size=640x320&sensor=false&markers=#{markers}"
Generates a Google Static Maps link with the waypoints from Valparaíso to Shanghai every 2,000 km:
http://maps.googleapis.com/maps/api/staticmap?size=640x320&sensor=false&markers=-33.0,-71.60000000000002|-32.54414813683714,-93.02142653011552|-28.59922979115139,-113.43958859125276|-21.877555679819015,-131.91586675556778|-13.305784544363858,-148.5297601858932|-3.7370081151180683,-163.94988578467394|6.094273692291354,-179.03345538133888|15.493534924596633,165.33401731030006|23.70233917422386,148.3186618914762|29.83806632244171,129.34766276764626
Step 1 - Get the overall distance
Comprehensive answer can be found here: http://www.movable-type.co.uk/scripts/latlong.html
TL;DR:
This uses the ‘haversine’ formula to calculate the great-circle
distance between two points – that is, the shortest distance over the
earth’s surface – giving an ‘as-the-crow-flies’ distance between the
points (ignoring any hills, of course!).
var R = 6371; // km
var dLat = (lat2-lat1).toRad();
var dLon = (lon2-lon1).toRad();
var lat1 = lat1.toRad();
var lat2 = lat2.toRad();
var a = Math.sin(dLat/2) * Math.sin(dLat/2) +
Math.sin(dLon/2) * Math.sin(dLon/2) * Math.cos(lat1) * Math.cos(lat2);
var c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1-a));
var distance = R * c;
Step 2 - Get the percentage travelled.
Now you have the distance for this straight line, you can then work out a percentage of the overall distance for each 5 meters.
Step 3 - Apply the percentage travelled to the difference between the Latitude and Longitude
Find out the difference between the starting latitude and the final latitude. With this number, multiply it by the percentage traveled (as a decimal). This can then be added back to the starting latitude to find the current latitude of this point. Repeat for longitude.
The following solution isn't exactly what you've requested, but may suffice for your purposes...
Check the official docs (https://developers.google.com/maps/documentation/javascript/reference) for the interpolate method. From the docs: 'Returns the LatLng which lies the given fraction of the way between the origin LatLng and the destination LatLng.'
So if you know that your original points are, say, 100m apart, and you specify 0.05 as the fraction, the method will return the lat/lng along that line for every 5m.
I'm trying to figure out an algorithm for finding a random point a set distance away from a base point. So for example:
This could just be basic maths and my brain not working yet (forgive me, haven't had my coffee yet :) ), but I've been trying to work this out on paper and I'm not getting anywhere.
coordinate of point on circle with radius R and center (xc, yc):
x = xc + R*cos(a);
y = yc + R*sin(a);
changing value of angle a from 0 to 2*PI you can find any point on circumference.
Use the angle from the verticle as your random input.
Pseudocode:
angle = rand(0,1)
x = cos(angle * 2 * pi) * Radius + x_centre
y = sin(angle * 2 * pi) * Radius + y_centre
Basic Pythagoras.
Pick random number between 0 and 50 and solve h^2 = a^2 + b^2
Add a few random descisions on direction.