lbaumberger
133 lines
5.5 KiB
C++
133 lines
5.5 KiB
C++
#include <forces.h>
|
|
#include <math.h>
|
|
|
|
// return the direction between curr position and destination
|
|
// $\vectorarrow{e}_\alpha(t)$
|
|
Vector2d get_desired_direction(Pedestrian &p)
|
|
{
|
|
Vector2d diff = p.desired_next_node - p.curr_pos;
|
|
double length = diff.norm();
|
|
return (p.desired_next_node - p.curr_pos) / length;
|
|
}
|
|
|
|
// return the direction between curr position and destination
|
|
// $\vectorarrow{e}_\alpha(t)$
|
|
Vector2d get_actual_direction(Pedestrian &p)
|
|
{
|
|
Vector2d diff = p.actual_next_node - p.curr_pos;
|
|
double length = diff.norm();
|
|
return (p.actual_next_node - p.curr_pos) / length;
|
|
}
|
|
|
|
// double w(Vector2d e, Vector2d f) {
|
|
//
|
|
// if (e.dot(f) >= f.norm() * PHI_DEG) return 1.0;
|
|
// return C;
|
|
//
|
|
// }
|
|
double w(Vector2d e, Vector2d f)
|
|
{
|
|
// The w() function should check if the angle between the desired direction e and the force f is within the field of view.
|
|
// Per equation (7), the condition is:
|
|
// e · f ≥ ||f|| * cos(phi)
|
|
// where phi = 100° (half of the 200° field of view (PHI_DEG)).
|
|
const double phi = (PHI_DEG / 2.0) * M_PI / 180.0; // half-angle in radians
|
|
if (e.dot(f) >= f.norm() * cos(phi))
|
|
return 1.0;
|
|
return C;
|
|
}
|
|
|
|
// F_i is the current position resp. direction vector of pedestrian p in time i?
|
|
//
|
|
// $\vectorarrow{F}_\alpha(\vectorarrow{v}_\alpha, v^0_\alpha \vectorarrow{e}_\alpha) \vectorarrow{e}_\alpha(t) = \frac{1}{\tau_\alpha}(v^0_\alpha \vectorarrow{e}_\alpha - \vectorarrow{v}_\alpha)$
|
|
//
|
|
void compute_acceleration_term(Pedestrian *p)
|
|
{
|
|
|
|
Vector2d desired_velocity = get_desired_direction(*p) * p->desired_v0;
|
|
|
|
// TODO: How to get this number
|
|
// Probably needs to lookup the map and see if the desired position is reachable
|
|
Vector2d actual_velocity = get_actual_direction(*p) * p->actual_v0;
|
|
|
|
double tau_inv = 1 / TAU;
|
|
|
|
p->F_i += (desired_velocity - actual_velocity) * tau_inv;
|
|
}
|
|
|
|
// $\vectorarrow{f}_{\alpha \beta}(\vectorarrow{r}_{\alpha \beta}) = -\gradient_{\vectorarrow{r}_{\alpha \beta}} V_{\alpha \beta}[b(\vectorarrow{r}_{\alpha \beta})]$
|
|
void compute_repulsive_effects_from_pedestrians(Pedestrian *P, Pedestrian *p, int N)
|
|
{
|
|
double delta_t = 0.5; // assuming 0.5 seconds steps
|
|
for (int i = 0; i < N; i++)
|
|
{
|
|
// TODO: add check for $\vectorarrow{e} * \vectorarrow{f} >= \Arrowvert \vectorarrow{f} \Arrowvert \cos(\phi) $
|
|
// only consider close pedestrians
|
|
if (P + i == p)
|
|
continue;
|
|
if ((P[i].curr_pos - p->curr_pos).norm() < DISTANCE)
|
|
{
|
|
//
|
|
// $2b = \sqrt{(\Arrowvert \vectorarrow{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vectorarrow{r}_{\alpha \beta} - v_\beta \Delta t \vectorarrow{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$
|
|
//
|
|
// $\Arrowvert \vectorarrow{r}_{\alpha \beta} \Arrowvert$
|
|
Vector2d r_ab = p->curr_pos - P[i].curr_pos;
|
|
double r_ab_norm = (p->curr_pos - P[i].curr_pos).norm();
|
|
|
|
// $\Arrowvert \vectorarrow{r}_{\alpha \beta} - v_\beta \Delta t \vectorarrow{e}_\beta \Arrowvert$
|
|
Vector2d e_beta = get_actual_direction(P[i]);
|
|
Vector2d r_ab_something = p->curr_pos - P[i].curr_pos - (e_beta * (P[i].actual_v0 * delta_t));
|
|
double r_ab_something_norm = (p->curr_pos - P[i].curr_pos - (e_beta * (P[i].actual_v0 * delta_t))).norm();
|
|
|
|
// $v_\beta \Delta t$
|
|
double v_beta_delta_t = P[i].actual_v0 * delta_t;
|
|
|
|
double b = sqrt((r_ab_norm + r_ab_something_norm) * (r_ab_norm + r_ab_something_norm) - (v_beta_delta_t) * (v_beta_delta_t)) / 2;
|
|
|
|
//
|
|
// $\vectorarrow{f}_{\alpha \beta}(\vectorarrow{r}_{\alpha \beta}) = \frac{V^0_{\alpha \beta}}{\sigma}e^{-b / \sigma} \grad_{\vectorarrow{r}_{\alpha \beta}}b$
|
|
//
|
|
// TODO: what is $V^0_{\alpha \beta}$ probably some number?
|
|
//
|
|
//
|
|
// $\grad_{\vectorarrow{r}_{\alpha \beta}} = \frac{(\Arrowvert \vectorarrow{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vectorarrow{r}_{\alpha \beta} - v_\beta \Delta t \vectorarrow{e}_\beta \Arrowvert) (\frac{\vectorarrow{r}_{\alpha \beta}}{\Arrowvert \vectorarrow{r}_{\alpha \beta} \Arrowvert} + \frac{\vectorarrow{r}_{\alpha \beta} - v_\beta \Delta t \vectorarrow{e}_\beta}{\Arrowvert \vectorarrow{r}_{\alpha \beta} - v_\beta \Delta t \vectorarrow{e}_\beta \Arrowvert})}{4b}$
|
|
//
|
|
Vector2d delta_r_alpha_beta = (((r_ab / r_ab_norm) + (r_ab_something / r_ab_something_norm)) * (r_ab_norm + r_ab_something_norm)) / (4 * b);
|
|
|
|
// $\vectorarrow{f}_{\alpha \beta}$
|
|
Vector2d f_alpha_beta_r_alpha_beta = delta_r_alpha_beta * ((V0_AB / SIGMA) * (exp(-b / SIGMA)));
|
|
|
|
p->F_i += f_alpha_beta_r_alpha_beta * w(get_desired_direction(*p), f_alpha_beta_r_alpha_beta * -1);
|
|
}
|
|
}
|
|
}
|
|
|
|
// TODO: missing
|
|
void compute_repulsive_effects_from_borders(Sidewalk &S, Pedestrian *p)
|
|
{
|
|
|
|
Vector2d *obstacles = S.obstacles;
|
|
int n_obs = S.n_obstacles;
|
|
|
|
for (int i = 0; i < n_obs; i++)
|
|
{
|
|
Vector2d obstacle = obstacles[i];
|
|
// TODO: find out the variables and parameters
|
|
//
|
|
// $\vectorarrow{F}_{\alpha B}(\vectorarrow{r}_{\alpha B}) = \frac{U^0_{\alpha B}}{R} e^{- \Arrowvert \vectorarrow{r}_{\alpha B} \Arrowvert / R} \frac{\vectorarrow{r}_{\alpha B}}{\Arrowvert \vectorarrow{r}_{\alpha B} \Arrowvert}$
|
|
//
|
|
// double e = 2.718;
|
|
// we could just use exp
|
|
|
|
// $\vectorarrow{r}_{\alpha B}$
|
|
Vector2d r_ab = p->curr_pos - obstacle;
|
|
// $\Arrowvert \vectorarrow{r}_{\alpha B} \Arrowvert$
|
|
double r_ab_norm = r_ab.norm();
|
|
if (r_ab_norm < 1e-6)
|
|
continue;
|
|
// repulsive force vector (Direction * Magnitude) (Edquation 6)
|
|
Vector2d f_ab = (r_ab / r_ab_norm) * (U0_AB / R) * exp(-r_ab_norm / R);
|
|
p->F_i += f_ab * w(get_desired_direction(*p), f_ab * -1);
|
|
}
|
|
}
|