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Path generation for non-intersecting disc movement on a plane

由 …衆ロ難τιáo~ 提交于 2019-11-27 14:42:23

Have played with this for fun a bit and here the result:

Algorithm:

  1. process each disc
  2. set speed as constant*destination_vector
    • multiplicative constant a
    • and limit the speed to constant v afterwards
  3. test if new iterated position does not conflict any other disc
  4. if it does rotate the speed in one direction by some angle step ang
  5. loop until free direction found or full circle covered

  6. if no free direction found mark disc as stuck

    This is how it looks like for circle to inverse circle path:

    This is how it looks like for random to random path:

    stuck disc are yellow (none in these cases) and not moving discs are at destination already. This can also get stuck if there is no path like if disc already in destination circles another ones destination. To avoid that you need also change the colliding disc also ... You can play with the ang,a,v constants to make different appearance and also you could try random direction of angle rotation to avoid that swirling/twister movement

Here the source code I used (C++):

//---------------------------------------------------------------------------
const int    discs =23;     // number of discs
const double disc_r=5;      // disc radius
const double disc_dd=4.0*disc_r*disc_r;
struct _disc
    {
    double x,y,vx,vy;       // actual position
    double x1,y1;           // destination
    bool _stuck;            // is currently stuck?
    };
_disc disc[discs];          // discs array
//---------------------------------------------------------------------------
void disc_generate0(double x,double y,double r)     // circle position to inverse circle destination
    {
    int i;
    _disc *p;
    double a,da;
    for (p=disc,a=0,da=2.0*M_PI/double(discs),i=0;i<discs;a+=da,i++,p++)
        {
        p->x =x+(r*cos(a));
        p->y =y+(r*sin(a));
        p->x1=x-(r*cos(a));
        p->y1=y-(r*sin(a));
        p->vx=0.0;
        p->vy=0.0;
        p->_stuck=false;
        }
    }
//---------------------------------------------------------------------------
void disc_generate1(double x,double y,double r)     // random position to random destination
    {
    int i,j;
    _disc *p,*q;
    double a,da;
    Randomize();
    for (p=disc,a=0,da=2.0*M_PI/double(discs),i=0;i<discs;a+=da,i++,p++)
        {
        for (j=-1;j<0;)
            {
            p->x=x+(2.0*Random(r))-r;
            p->y=y+(2.0*Random(r))-r;
            for (q=disc,j=0;j<discs;j++,q++)
             if (i!=j)
              if (((q->x-p->x)*(q->x-p->x))+((q->y-p->y)*(q->y-p->y))<disc_dd)
               { j=-1; break; }
            }
        for (j=-1;j<0;)
            {
            p->x1=x+(2.0*Random(r))-r;
            p->y1=y+(2.0*Random(r))-r;
            for (q=disc,j=0;j<discs;j++,q++)
             if (i!=j)
              if (((q->x1-p->x1)*(q->x1-p->x1))+((q->y1-p->y1)*(q->y1-p->y1))<disc_dd)
               { j=-1; break; }
            }
        p->vx=0.0;
        p->vy=0.0;
        p->_stuck=false;
        }
    }
//---------------------------------------------------------------------------
void disc_iterate(double dt)                    // iterate positions
    {
    int i,j,k;
    _disc *p,*q;
    double v=25.0,a=10.0,x,y;
    const double ang=10.0*M_PI/180.0,ca=cos(ang),sa=sin(ang);
    const int n=double(2.0*M_PI/ang);
    for (p=disc,i=0;i<discs;i++,p++)
        {
        p->vx=a*(p->x1-p->x); if (p->vx>+v) p->vx=+v; if (p->vx<-v) p->vx=-v;
        p->vy=a*(p->y1-p->y); if (p->vy>+v) p->vy=+v; if (p->vy<-v) p->vy=-v;
        x=p->x; p->x+=(p->vx*dt);
        y=p->y; p->y+=(p->vy*dt);
        p->_stuck=false;
        for (k=0,q=disc,j=0;j<discs;j++,q++)
         if (i!=j)
          if (((q->x-p->x)*(q->x-p->x))+((q->y-p->y)*(q->y-p->y))<disc_dd)
            {
            k++; if (k>=n) { p->x=x; p->y=y; p->_stuck=true; break; }
            p->x=+(p->vx*ca)+(p->vy*sa); p->vx=p->x;
            p->y=-(p->vx*sa)+(p->vy*ca); p->vy=p->y;
            p->x=x+(p->vx*dt);
            p->y=y+(p->vy*dt);
            j=-1; q=disc-1;
            }
        }
    }
//---------------------------------------------------------------------------

Usage is simple:

  1. call generate0/1 with center and radius of your plane where discs will be placed
  2. call iterate (dt is time elapsed in seconds)
  3. draw the scene

if you want to change this to use t=<0,1>

  1. loop iterate until all disc at destination or timeout
  2. remember any change in speed for each disc in a list need the position or speed vector and time it occur
  3. after loop rescale the discs list all to the range of <0,1>
  4. render/animate the rescaled lists

[Notes]

My test is running in real time but I did not apply the <0,1> range and have not too many discs. So you need to test if this is fast enough for your setup.

To speed up you can:

  • enlarge the angle step
  • test the collision after rotation against last collided disc and only when free test the rest...
  • segmentate the disc into (overlapping by radius) regions handle each region separately
  • also I think some field approach here could speed up things like create field map once in a while for better determine the obstacle avoidance direction

[edit1] some tweaks to avoid infinite oscillations around obstacle

For more discs some of them get stuck bouncing around already stopped disc. To avoid that just change the ang step direction once in a while this is the result:

you can see the oscillating bouncing before finish

this is the changed source:

void disc_iterate(double dt)                    // iterate positions
    {
    int i,j,k;
    static int cnt=0;
    _disc *p,*q;
    double v=25.0,a=10.0,x,y;
    const double ang=10.0*M_PI/180.0,ca=cos(ang),sa=sin(ang);
    const int n=double(2.0*M_PI/ang);
    // process discs
    for (p=disc,i=0;i<discs;i++,p++)
        {
        // compute and limit speed
        p->vx=a*(p->x1-p->x); if (p->vx>+v) p->vx=+v; if (p->vx<-v) p->vx=-v;
        p->vy=a*(p->y1-p->y); if (p->vy>+v) p->vy=+v; if (p->vy<-v) p->vy=-v;
        // stroe old and compute new position
        x=p->x; p->x+=(p->vx*dt);
        y=p->y; p->y+=(p->vy*dt);
        p->_stuck=false;
        // test if coliding
        for (k=0,q=disc,j=0;j<discs;j++,q++)
         if (i!=j)
          if (((q->x-p->x)*(q->x-p->x))+((q->y-p->y)*(q->y-p->y))<disc_dd)
            {
            k++; if (k>=n) { p->x=x; p->y=y; p->_stuck=true; break; }   // if full circle covered? stop
            if (int(cnt&128))   // change the rotation direction every 128 iterations
                {
                // rotate +ang
                p->x=+(p->vx*ca)+(p->vy*sa); p->vx=p->x;
                p->y=-(p->vx*sa)+(p->vy*ca); p->vy=p->y;
                }
            else{
                //rotate -ang
                p->x=+(p->vx*ca)-(p->vy*sa); p->vx=p->x;
                p->y=+(p->vx*sa)+(p->vy*ca); p->vy=p->y;
                }
            // update new position and test from the start again
            p->x=x+(p->vx*dt);
            p->y=y+(p->vy*dt);
            j=-1; q=disc-1;
            }
        }
    cnt++;
    }

It isn't perfect, but my best idea has been to move the discs along quadratic Bezier curves. That means you've got just 2 free variables per disc that you're trying to find values for.

At that point, you can "plug" an error function into a nonlinear optimizer. Longer you're willing to wait, the better your solution will be, in terms of discs avoiding each other.

Only one actual hit:

Doesn't bother displaying hits, the discs actually start overlapped:

I've produced a full example, but the key is the error function to be minimized, which I reproduce here:

double errorf(unsigned n, const double *pts, double *grad,
              void *data)
{
  problem_t *setup = (problem_t *)data;
  double error = 0.0;

  for(int step=0; step<setup->steps; step++) {
    double t = (1.0+step) / (1.0+setup->steps);
    for(int i=0; i<setup->N; i++)
      quadbezier(&setup->starts[2*i],
                 &pts[2*i],
                 &setup->stops[2*i],
                 t,
                 &setup->scratch[2*i]);

    for(int i=0; i<setup->N; i++)
      for(int j=i+1; j<setup->N; j++) {
        double d = distance(&setup->scratch[2*i],
                            &setup->scratch[2*j]);
        d /= RADIUS;
        error += (1.0/d) * (1.0/d);
      }
  }

  return error / setup->steps;
}

Ignore n, grad and data. setup describes the specific problem being optimized, number of discs, and where they start and stop. quadbezier does the Bezier curve interpolation, placing its answer into ->scratch. We check ->steps points part way along the path, and measure how close the discs are to one another at each step. To make the optimization problem smoother, it doesn't have a hard switch when the discs start touching, it just tries to keep them all as far apart from one another as possible.

Completely compilable code, Makefile and some Python for turning a bunch of quadratic bezier curves into a series of images is available at https://github.com/jkominek/discs

Performance is a bit sluggish on huge numbers of points, but there are a number of options for improvement.

  1. If the user is making minor tweaks to the starting and finishing positions, then after every tweak, rerun the optimization in the background, using the previous solution as the new starting point. Fixing up a close solution should be faster than recreating it from scratch every time.
  2. Parallelize the n^2 loop over all points.
  3. Check to see if other optimization algorithms will do better on this data. Right now it starts with a global optimization pass, and then does a local optimization pass. There are algorithms which already "know" how to do that sort of thing, and are probably smarter about it.
  4. If you can figure out how to compute the gradient function for free or close to, I'm sure it would be worth it to do so, and switch to algorithms that can make use of the gradient information. It might be worth it even if the gradient isn't cheap.
  5. Replace the whole steps thing with a suboptimization that finds the t at which the two discs are closest, and then uses that distance for the error. Figuring out the gradient for that suboptimization should be much easier.
  6. Better data structures for the intermediate points, so you don't perform a bunch of unnecessary distance calculations for discs that are very far apart.
  7. Probably more?

The usual solution for this kind of problem is to use what is called a "heat map" (or "influence map"). For every point in the field, you compute a "heat" value. The disks move towards high values and away from cold values. Heat maps are good for your type of problem because they are very simple to program, yet can generate sophisticated, AI-like behavior.

For example, imagine just two disks. If your heat map rule is equi-radial, then the disks will just move towards each other, then back away, oscillating back and forth. If your rule randomizes intensity on different radials, then the behavior will be chaotic. You can also make the rule depend on velocity in which case disks will accelerate and decelerate as they move around.

Generally, speaking the heat map rule should make areas "hotter" at they approach some optimal distance from a disk. Places that are too near a disk, or too far away get "colder". By changing this optimal distance you can determine how close the disks congregate together.

Here are a couple of articles with example code showing how to use heat maps:

http://haufler.org/2012/05/26/beating-the-scribd-ai-challenge-implementing-traits-through-heuristics-part-1/

http://www.gamedev.net/page/resources/_/technical/artificial-intelligence/the-core-mechanics-of-influence-mapping-r2799

Game AI Pro, Volume 2, chapter on Heat Maps

I don't have enough rep to comment yet, so sorry for the non-answer. But to the RTS angle, RTS's generally use the A* algorithm for path finding. Is there a reason you're insisting on using a physics-based model?

Secondly, your attempt you linked that operates rather smoothly, but with the acceleration in the middle, behaves how I initially thought. Since your model treats it as a rubber band, it basically is looking for which way to rotate for the shortest path to the desired location.

If you arent worried about a physical approach, I would attempt as follows: Try to move directly toward the target. if it collides, it should attempt to roll clockwise around its most recent collision until it is in a position on the vector at 90 degrees to the vector from current location to the target location.

If we assume a test case of 5 in a row at the top of a box and five in a row at the bottom, they will move directly toward each other until they collide. The entire top row will slide to the right until they fall over the edge of the bottom row as it moves to the left and floats over the edge of the top row. (Think of what the whiskey and water shot glass trick looks like when it starts)

Since the motion is not determined by a potential energy stored in the spring which will accelerate the object during a rotation, you have complete control over how the speed changes during the simulation.

In a circular test like you have above, if all disks are initialized with the same speed, the entire clump will go to the middle, collide and twist as a unit for approximately a quarter turn at which point they will break away and head for their goal.

If the timing is lightly randomized, I think you'll get the behavior you're looking for.

I hope this helps.

标签
algorithm
language-agnostic
game-physics
computational-geometry
motion-planning
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