diff --git a/board.hpp b/board.hpp index 6ee78b4..e76b5ea 100644 --- a/board.hpp +++ b/board.hpp @@ -1,9 +1,41 @@ #pragma once #include "vector2d.hpp" +struct Obstacle { + Vector2d start; + Vector2d end; + + Obstacle() : start(0, 0), end(0, 0) {} + Obstacle(Vector2d s, Vector2d e) : start(s), end(e) {} + // find the point of the obstacle that is nearest to p + Vector2d find_nearest_obstacle_point(Vector2d p) { + Vector2d obstacle_v = end - start; + double projection = (p - start).dot(obstacle_v) / obstacle_v.dot(obstacle_v); + if (projection < 0.0) + projection = 0.0; + if (projection > 1.0) + projection = 1.0; + return start + obstacle_v * projection; + } +}; + struct Board { double length, width; - Vector2d *borders; // array containing the position of each obstacle - int n_borders; // number of obstacles - Board(double l, double w, int n, Vector2d *border) : length(l), width(w), n_borders(n), borders(border) {} + Obstacle *obstacles; // array of obstacles (represented as lines) that are present on the board + int n_obstacles; // number of obstacles + + Board(double l, double w, int n, Obstacle *o) : length(l), width(w), n_obstacles(n + 4) { + obstacles = new Obstacle[n + 4]; + for (int i = 0; i < n; i++) { + obstacles[i] = o[i]; + } + + // borders of the board: bottom, right, top, left + obstacles[n] = Obstacle(Vector2d(0, 0), Vector2d(l, 0)); + obstacles[n + 1] = Obstacle(Vector2d(l, 0), Vector2d(l, w)); + obstacles[n + 2] = Obstacle(Vector2d(l, w), Vector2d(0, w)); + obstacles[n + 3] = Obstacle(Vector2d(0, w), Vector2d(0, 0)); + } + + ~Board() { delete[] obstacles; } }; diff --git a/forces.cpp b/forces.cpp index b48d0d0..24dfa5f 100644 --- a/forces.cpp +++ b/forces.cpp @@ -1,35 +1,26 @@ #include "forces.hpp" #include -// # Done double w(Vector2d e, Vector2d f) { if (e.dot(f) >= f.norm() * cos_phi) return 1.0; - return c; + else + return c; +} + +double g(double v_max_alpha, double w_alpha_norm) { + if (w_alpha_norm <= v_max_alpha) { + return 1; + } + return v_max_alpha / w_alpha_norm; } -// # Done -// -// $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) \vec{e}_\alpha(t) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$ -// void compute_acceleration_term(Pedestrian *p) { - - // $v_{\alpha}^{0}\vec{e}_{\alpha}$ Vector2d desired_velocity = p->e * p->desired_v0; - - // $\vec{v}_{\alpha}(t)$ Vector2d actual_velocity = p->curr_vel; - - double tau_alpha_inv = 1 / tau_alpha; - - p->F_i += (desired_velocity - actual_velocity) * tau_alpha_inv; + p->f_i += (desired_velocity - actual_velocity) / tau_alpha; } -// # Done -// -// $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta})$ -// - void compute_repulsive_effects_from_pedestrians(Pedestrian *pedestrians, Pedestrian *p, int N) { for (int i = 0; i < N; i++) { @@ -43,78 +34,41 @@ void compute_repulsive_effects_from_pedestrians(Pedestrian *pedestrians, Pedestr // continue; // } - // $\vec{r}_{\alpha\beta}$ Vector2d r_ab = p->curr_pos - pedestrians[i].curr_pos; - // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert$ double r_ab_norm = r_ab.norm(); - // $\vec{e}_{\beta}$ - Vector2d e_beta = pedestrians[i].e; + Vector2d e_b = pedestrians[i].e; - // $v_\beta \Delta t$ - double v_beta_delta_t = pedestrians[i].desired_v0 * delta_t; + double s_b = pedestrians[i].desired_v0 * delta_t; - // $\vec{r}_{\alpha\beta} - \vec{v}_{\beta}\varDelta_{t}\vec{e}_{\beta}$ - Vector2d r_ab_minus_v_beta_delta_t_e_beta = r_ab - (e_beta * v_beta_delta_t); + Vector2d r_ab2 = r_ab - (e_b * s_b); - // $\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$ - double r_ab_minus_v_beta_delta_t_e_beta_norm = r_ab_minus_v_beta_delta_t_e_beta.norm(); + double r_ab2_norm = r_ab2.norm(); - // (4): - // - // $2b = \sqrt{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$ - // + double b = sqrt(pow(r_ab_norm + r_ab2_norm, 2) - pow(s_b, 2)) / 2; - double b = sqrt((r_ab_norm + r_ab_minus_v_beta_delta_t_e_beta_norm) * (r_ab_norm + r_ab_minus_v_beta_delta_t_e_beta_norm) - (v_beta_delta_t) * (v_beta_delta_t)) / 2; + Vector2d b_grad = (r_ab / r_ab_norm + r_ab2 / r_ab2_norm) * (r_ab_norm + r_ab2_norm) / (4 * b); - // (7): - // - // $\grad_{\vec{r}_{\alpha \beta}}b = \frac{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert) (\frac{\vec{r}_{\alpha \beta}}{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert} + \frac{\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta}{\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert})}{4b}$ - // - // - Vector2d delta_r_alpha_beta_b = (((r_ab / r_ab_norm) + (r_ab_minus_v_beta_delta_t_e_beta / r_ab_minus_v_beta_delta_t_e_beta_norm)) * (r_ab_norm + r_ab_minus_v_beta_delta_t_e_beta_norm)) / (4 * b); + Vector2d f_ped = b_grad * ((V0_AB / SIGMA) * (exp(-b / SIGMA))); - // (5): - // - // $\vec{f}_{\alpha \beta}(\vec{r}_{\alpha \beta}) = \frac{V^0_{\alpha \beta}}{\sigma}e^{-b / \sigma} \grad_{\vec{r}_{\alpha \beta}}b$ - // - // - Vector2d f_alpha_beta_r_alpha_beta = delta_r_alpha_beta_b * ((V0_AB / SIGMA) * (exp(-b / SIGMA))); - - // (11): - // - // $\vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta}) = w(\vec{e}_{\alpha}, -\vec{f}_{\alpha\beta})\vec{f}_{\alpha\beta}(\vec{r}_{\alpha} - \vec{r}_{\beta})$ - // - // Add repulsive effects from other pedestrians to current pedestrian's social force - p->F_i += f_alpha_beta_r_alpha_beta * w(p->e, f_alpha_beta_r_alpha_beta * -1); + p->f_i += f_ped * w(p->e, f_ped * -1); } } -// # Done -void compute_repulsive_effects_from_borders(Board &B, Pedestrian *p) { +void compute_repulsive_effects_from_borders(Board &board, Pedestrian *p) { - Vector2d *borders = B.borders; - int n_borders = B.n_borders; + Obstacle *obstacles = board.obstacles; + int n_borders = board.n_obstacles; for (int i = 0; i < n_borders; i++) { - Vector2d border = borders[i]; + Obstacle obstacle = obstacles[i]; - // $\vec{r}_{\alpha B}$ - Vector2d r_alpha_B = p->curr_pos - border; + Vector2d r_aB = p->curr_pos - obstacle.find_nearest_obstacle_point(p->curr_pos); - // - // $\vec{F}_{\alpha B}(\vec{r}_{\alpha B}) = \frac{U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R} \frac{\vec{r}_{\alpha B}}{\Arrowvert \vec{r}_{\alpha B} \Arrowvert}$ - // + Vector2d f_obst = (r_aB / r_aB.norm()) * (U0_AB / R) * std::exp(-r_aB.norm() / R); - Vector2d F_alpha_B = (r_alpha_B / r_alpha_B.norm()) * (U0_AB / R) * std::exp(-r_alpha_B.norm() / R); - - // (12): - // - // $\vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{ B}) = w(\vec{e}_{\alpha}, -\vec{F}_{\alpha B})\vec{F}_{\alpha B}(\vec{r}_{\alpha} - \vec{r}_{ B})$ - // - - p->F_i += F_alpha_B * w(p->e, F_alpha_B * (-1)); + p->f_i += f_obst; } } diff --git a/forces.hpp b/forces.hpp index 27cc042..3d2bd4a 100644 --- a/forces.hpp +++ b/forces.hpp @@ -3,46 +3,33 @@ #include "pedestrian.hpp" #include -// $\tau_{\alpha}$ -// https://arxiv.org/pdf/cond-mat/9805244, page 8 const double tau_alpha = 0.5; -// $V^0_{\alpha \beta}$ -// https://arxiv.org/pdf/cond-mat/9805244, page 8 const double V0_AB = 2.1; -// $\sigma$ -// https://arxiv.org/pdf/cond-mat/9805244, page 8 const double SIGMA = 0.3; -// $U^0_{\alpha B}$ -// https://arxiv.org/pdf/cond-mat/9805244, page 8 const double U0_AB = 10.0; -// $R$ -// https://arxiv.org/pdf/cond-mat/9805244, page 8 const double R = 0.2; -// $c$ -// https://arxiv.org/pdf/cond-mat/9805244, page 8 const double c = 0.5; -// $\varphi$ -// https://arxiv.org/pdf/cond-mat/9805244, page 8 -const double phi = 100.0; +const double phi = 100.0 / 180.0 * M_PI; const double cos_phi = cos(phi); -// $\varDelta_{t}$ -// step size = 0.2 seconds -// https://arxiv.org/pdf/cond-mat/9805244, page 8 const double delta_t = 2; +const double dt = 0.01; + // # Optional (forces.cpp: compute_repulsive_effects_from_pedestrians) // const double DISTANCE = 2.0; double w(Vector2d e, Vector2d f); +double g(double v_max_alpha, double w_alpha_norm); + void compute_acceleration_term(Pedestrian *p); void compute_repulsive_effects_from_pedestrians(Pedestrian *P, Pedestrian *p, int N); diff --git a/main.cpp b/main.cpp index 2e6035b..10ee86e 100644 --- a/main.cpp +++ b/main.cpp @@ -4,12 +4,8 @@ #include "pedestrian.hpp" #include "vector2d.hpp" -double g(double v_max_alpha, double w_alpha_norm) { - if (w_alpha_norm <= v_max_alpha) { - return 1; - } - return v_max_alpha / w_alpha_norm; -} +#define BOARD_WIDTH 10.0 +#define BOARD_LENGTH 50.0 using namespace std; @@ -23,67 +19,58 @@ using namespace std; Pedestrian *init_pedestrians(int n_p) { Pedestrian *P = new Pedestrian[n_p]; - for (int i = 0; i < n_p; i++) { - P[i] = Pedestrian(); + std::random_device rd; // use for truly random simulation + std::mt19937 gen(rd()); // use rd instead of 42 as seed for real randomness + std::normal_distribution velocity_gen(1.34, 0.26); // 1.34 and 0.26 are the mean and stdrd deviation used in paper + std::uniform_real_distribution x_gen(2, BOARD_LENGTH - 2); + std::uniform_real_distribution y_gen(2, BOARD_WIDTH - 2); + for (int i = 0; i < n_p / 2; i++) { + Vector2d start_pos = Vector2d(1, y_gen(gen)); + Vector2d destination = Vector2d(BOARD_LENGTH - 1, y_gen(gen)); + double desired_v0 = velocity_gen(gen); + double v_max = 1.3 * desired_v0; + P[i] = Pedestrian(WHITE, (destination - start_pos) / (destination - start_pos).norm() * desired_v0, start_pos, (destination - start_pos) / (destination - start_pos).norm(), v_max, desired_v0, destination, Vector2d()); + } + + for (int i = n_p / 2; i < n_p; i++) { + Vector2d start_pos = Vector2d(BOARD_LENGTH - 1, y_gen(gen)); + Vector2d destination = Vector2d(1, y_gen(gen)); + double desired_v0 = velocity_gen(gen); + double v_max = 1.3 * desired_v0; + P[i] = Pedestrian(BLACK, (destination - start_pos) / (destination - start_pos).norm() * desired_v0, start_pos, (destination - start_pos) / (destination - start_pos).norm(), v_max, desired_v0, destination, Vector2d(0, 0)); } return P; } -Board init_board(int width, int length) { - Vector2d obs[] = {Vector2d(2.0, 1.0), Vector2d(4.0, 1.0), Vector2d(6.0, 2.0), Vector2d(6.0, 4.0)}; - return Board(width, length, 4, obs); -} +int main(int argc, const char *argv[]) { -int main(int argc, const char* argv[]) { - - Board B = init_board(10, 15); + Obstacle obs[] = {}; + Board board = Board(BOARD_LENGTH, BOARD_WIDTH, 0, obs); int n_p = atoi(argv[1]); int n_iter = atoi(argv[2]); - Pedestrian *pedestrians = init_pedestrians(n_p); - for (int t = 0; t < n_iter; t++) { for (int i = 0; i < n_p; i++) { Pedestrian &p = pedestrians[i]; - p.F_i.reset(); + p.f_i.reset(); - // - // $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) \vec{e}_\alpha(t) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$ - // compute_acceleration_term(&p); - // - // $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta})$ - // compute_repulsive_effects_from_pedestrians(pedestrians, &p, n_p); - // - // $\sum_{B} \vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{B})$ - // - compute_repulsive_effects_from_borders(B, &p); + compute_repulsive_effects_from_borders(board, &p); - // - // $\vec{w}_i = \vec{v}_i + \vec{F}_idt$ - // - p.w_alpha = p.curr_vel + p.F_i * delta_t; + Vector2d w_alpha = p.curr_vel + p.f_i * dt; - // - // $\vec{v}_i^{t+1} = \vec{w}_ig(\frac{v^{max}_i}{\Arrowvert \vec{w}_i \Arrowvert}) $ - // - p.next_vel = p.w_alpha * g(p.v_max, p.w_alpha.norm()); + p.curr_vel = w_alpha * g(p.v_max, w_alpha.norm()); - // - // $\vec{r}_i^{t+1} += \vec{r}^t_i + v^t_idt$ - // - p.curr_pos = p.curr_pos + p.curr_vel * delta_t; - } + p.curr_pos = p.curr_pos + p.curr_vel * dt; - for (int i = 0; i < n_p; i++) { - pedestrians[i].update(); + p.e = (p.destination - p.curr_pos) / (p.destination - p.curr_pos).norm(); } } diff --git a/pedestrian.hpp b/pedestrian.hpp index 8cbef8b..54b3abe 100644 --- a/pedestrian.hpp +++ b/pedestrian.hpp @@ -1,85 +1,21 @@ #pragma once -#include "vector2d.hpp" #include "random" +#include "vector2d.hpp" +// For visual simulation +enum Color { BLACK, WHITE }; struct Pedestrian { - - // TODO: Update initial values - Pedestrian() : - curr_vel(), - curr_pos(), - e(), - v_max(1.34 * 1.3), - desired_v0(1.34), - destination(), - F_i(), - next_pos(), - next_vel(), - w_alpha() - {} - - Pedestrian( - Vector2d c_v, - Vector2d c_p, - Vector2d e, - Vector2d d, - Vector2d f, - Vector2d n_p, - Vector2d n_v, - Vector2d w_alpha - ) : - curr_vel(c_v), - curr_pos(c_p), - e(e), - v_max(), - desired_v0(), - destination(d), - F_i(f), - next_pos(n_p), - next_vel(n_v), - w_alpha(w_alpha) - {} - - // $\vec{v}_{\alpha}(t)$ + Color color; Vector2d curr_vel; - - // $\vec{r}_\alpha(t)$ Vector2d curr_pos; - - // $\vec{e}_{\alpha}(t)$ Vector2d e; - - // $v_{\alpha}^{max} = 1.3 v^{0}_{\alpha}$ double v_max; - - // $v_\alpha^0$ double desired_v0; - - // # ============================================================= - - // $\vec{r}^0_\alpha$ Vector2d destination; + Vector2d f_i; - // social force - Vector2d F_i; + Pedestrian() : color(WHITE), curr_vel(0.0, 0.0), curr_pos(0.0, 0.0), e(0.0, 0.0), v_max(0.0), desired_v0(0.0), destination(0.0, 0.0), f_i(0.0, 0.0) {} - // # ============================================================= - - // $\vec{r}_\alpha(t+1)$ - Vector2d next_pos; - - // $\vec{v}_{\alpha}(t + 1)$ - Vector2d next_vel; - - // $\vec{w}_{\alpha}$ - Vector2d w_alpha; - - // # ============================================================= - - void update() { - curr_pos = next_pos; - curr_vel = next_vel; - e = (destination - curr_pos) / (destination - curr_pos).norm(); - } + Pedestrian(Color clr, Vector2d c_v, Vector2d c_p, Vector2d e, double v_max, double desired_v0, Vector2d d, Vector2d f) : color(clr), curr_vel(c_v), curr_pos(c_p), e(e), v_max(v_max), desired_v0(desired_v0), destination(d), f_i(f) {} };