diff --git a/guided_mrmp/controllers/multi_mpc.py b/guided_mrmp/controllers/multi_mpc.py
new file mode 100644
index 0000000000000000000000000000000000000000..2df98296ad5a3ca0d22e7d277c1b2ec9dd0036ca
--- /dev/null
+++ b/guided_mrmp/controllers/multi_mpc.py
@@ -0,0 +1,215 @@
+import numpy as np
+import casadi as ca
+from guided_mrmp.optimizer import Optimizer
+
+np.seterr(divide="ignore", invalid="ignore")
+
+class MultiMPC:
+ def __init__(self, num_robots, model, T, DT, state_cost, final_state_cost, input_cost, input_rate_cost, settings, circle_obs):
+ """
+ Initializes the MPC controller.
+ """
+ self.nx = model.state_dimension() # number of state vars
+ self.nu = model.control_dimension() # number of input/control vars
+ self.num_robots = num_robots
+ self.robot_radius = model.radius
+
+ self.robot_model = model
+ self.dt = DT
+
+ self.circle_obs = circle_obs
+
+ # how far we can look into the future divided by our dt
+ # is the number of control intervals
+ self.control_horizon = int(T / DT)
+
+ # Weight for the error in state
+ self.Q = np.diag(state_cost)
+
+ # Weight for the error in final state
+ self.Qf = np.diag(final_state_cost)
+
+ # weight for error in control
+ self.R = np.diag(input_cost)
+ self.P = np.diag(input_rate_cost)
+
+ self.acceptable_tol = settings['acceptable_tol']
+ self.acceptable_iter = settings['acceptable_iter']
+ self.print_level = settings['print_level']
+ self.print_time = settings['print_time']
+
+ def apply_quadratic_barrier(self, d_max, d, c):
+ """
+ Applies a quadratic barrier to some given distance. The quadratic barrier
+ is a soft barrier function. We are using it for now to avoid any issues with
+ invalid initial solutions, which hard barrier functions cannot handle.
+
+ params:
+ d (float): distance to the obstacle
+ c (float): controls the steepness of curve.
+ higher c --> gets more expensive faster as you move toward obs
+ d_max (float): The threshold distance at which the barrier starts to apply
+ """
+ return c*ca.fmax(0, (d_max-d)**2)
+
+ def setup_mpc_problem(self, initial_state, target, prev_cmd, As, Bs, Cs):
+ """
+ Create the cost function and constraints for the optimization problem.
+
+ inputs:
+ - initial_state (nx3 array): Initial state for each robot
+ - target : Target state for each robot
+ - prev_cmd: Previous control input for each robot
+ - As: List of A matrices for each robot
+ - Bs: List of B matrices for each robot
+ - Cs: List of C matrices for each robot
+ """
+
+ opti = ca.Opti()
+
+ # Decision variables
+ X = opti.variable(self.nx*self.num_robots, self.control_horizon + 1)
+ pos = X[:self.num_robots*2,:] # position is the first two values
+ x = pos[0::2,:]
+ y = pos[1::2,:]
+ heading = X[self.num_robots*2:,:] # heading is the last value
+
+ U = opti.variable(self.nu*self.num_robots, self.control_horizon)
+
+ # Parameters
+ initial_state = ca.MX(initial_state)
+
+ # print(f"target = {target}")
+ # target = target
+ # prev_cmd = ca.MX(prev_cmd)
+ # As = ca.MX(As)
+ # Bs = ca.MX(Bs)
+ # Cs = ca.MX(Cs)
+
+ # Cost function
+ cost = 0
+ for k in range(self.control_horizon):
+ for i in range(self.num_robots):# 0, 3 # 3,6
+ # print(f"k = {k}/{self.control_horizon-1}")
+ # print(f"target a = {target[i]}")
+ # print(f"target b = {target[i][:][k]}")
+ # # print(f"target c = {target[i][:][k]}")
+ this_target = [target[i][0][k], target[i][1][k], target[i][2][k]]
+ # print(f"this_target = {this_target}")
+ # difference between the current state and the target state
+ cost += ca.mtimes([(X[i*3 : i*3 +3, k+1] - this_target).T, self.Q, X[i*3 : i*3 +3, k+1] - this_target])
+
+
+ # control effort
+ cost += ca.mtimes([U[i*2:i*2+2, k].T, self.R, U[i*2:i*2+2, k]])
+ if k > 0:
+ # Penalize large changes in control
+ cost += ca.mtimes([(U[i*2:i*2+2, k] - U[i*2:i*2+2, k-1]).T, self.P, U[i*2:i*2+2, k] - U[i*2:i*2+2, k-1]])
+
+
+ # Final state cost
+ for i in range(self.num_robots):
+ final_target = this_target = [target[i][0][-1], target[i][1][-1], target[i][2][-1]]
+ cost += ca.mtimes([(X[i*3 : i*3 +3, -1] - final_target).T, self.Qf, X[i*3 : i*3 +3, -1] - final_target])
+
+ # robot-robot collision cost
+ dist_to_other_robots = 0
+ for k in range(self.control_horizon):
+ for r1 in range(self.num_robots):
+ for r2 in range(r1+1, self.num_robots):
+ if r1 != r2:
+ d = ca.sumsqr(pos[2*r1 : 2*r1+1, k] - pos[2*r2 : 2*r2+1, k])
+ d = ca.sqrt(d)
+ dist_to_other_robots += self.apply_quadratic_barrier(6*self.robot_radius, d-self.robot_radius*2, 1)
+
+ # obstacle collision cost
+ obstacle_cost = 0
+ for k in range(self.control_horizon):
+ for i in range(self.num_robots):
+ for obs in self.circle_obs:
+ d = ca.sumsqr(x[i, k] - obs[0]) + ca.sumsqr(y[i, k] - obs[1])
+ d = ca.sqrt(d)
+ obstacle_cost += self.apply_quadratic_barrier(6*self.robot_radius, d-self.robot_radius*2, 1)
+
+ opti.minimize(cost + .5*dist_to_other_robots + .5*obstacle_cost)
+
+ # Constraints
+ for i in range(self.num_robots):
+ for k in range(self.control_horizon):
+ A = ca.MX(As[i])
+ B = ca.MX(Bs[i])
+ C = ca.MX(Cs[i])
+ opti.subject_to(X[i*3:i*3+3, k+1] == ca.mtimes(A, X[i*3:i*3+3, k]) + ca.mtimes(B, U[i*2:i*2+2, k]) + C)
+
+ for i in range(self.num_robots):
+ opti.subject_to(X[i*3:i*3+3, 0] == initial_state[i])
+
+ for i in range(self.num_robots):
+ opti.subject_to(opti.bounded(-self.robot_model.max_acc, U[i*2:i*2+2, :], self.robot_model.max_acc))
+ opti.subject_to(ca.fabs(U[i*2, 0] - prev_cmd[i][0]) / self.dt <= self.robot_model.max_d_acc)
+ opti.subject_to(ca.fabs(U[i*2+1, 0] - prev_cmd[i][1]) / self.dt <= self.robot_model.max_d_steer)
+
+ for k in range(1, self.control_horizon):
+ opti.subject_to(ca.fabs(U[i*2, k] - U[i*2, k-1]) / self.dt <= self.robot_model.max_d_acc)
+ opti.subject_to(ca.fabs(U[i*2+1, k] - U[i*2+1, k-1]) / self.dt <= self.robot_model.max_d_steer)
+
+
+ return {
+ 'opti': opti,
+ 'X': X,
+ 'U': U,
+ 'initial_state': initial_state,
+ 'target': target,
+ 'prev_cmd': prev_cmd,
+ 'cost': cost,
+ 'dist_to_other_robots': dist_to_other_robots
+ }
+
+ def solve_optimization_problem(self, problem, initial_guesses=None, solver_options=None):
+ opt = Optimizer(problem)
+ results = opt.solve_optimization_problem(initial_guesses, solver_options)
+ return results
+
+ def step(self, initial_state, target, prev_cmd, initial_guesses=None):
+ """
+ Sets up and solves the optimization problem.
+
+ Args:
+ initial_state: List of current estimates of [x, y, heading] for each robot
+ target: State space reference, in the same frame as the provided current state
+ prev_cmd: List of previous commands [v, delta] for all robots
+ initial_guess: Optional initial guess for the optimizer
+
+ Returns:
+ x_opt: Optimal state trajectory
+ u_opt: Optimal control trajectory
+ """
+ As, Bs, Cs = [], [], []
+ for i in range(self.num_robots):
+ # print(f"initial_state[i] = {initial_state[i]}")
+ # print(f"prev_cmd[i] = {prev_cmd[i]}")
+ A, B, C = self.robot_model.linearize(initial_state[i], prev_cmd[i], self.dt)
+ As.append(A)
+ Bs.append(B)
+ Cs.append(C)
+
+ solver_options = {'ipopt.print_level': self.print_level,
+ 'print_time': self.print_time,
+ # 'ipopt.tol': 1e-3,
+ 'ipopt.acceptable_tol': self.acceptable_tol,
+ 'ipopt.acceptable_iter': self.acceptable_iter}
+
+ problem = self.setup_mpc_problem(initial_state, target, prev_cmd, As, Bs, Cs)
+
+ result = self.solve_optimization_problem(problem, initial_guesses, solver_options)
+
+ if result['status'] == 'succeeded':
+ x_opt = result['X']
+ u_opt = result['U']
+ else:
+ print("Optimization failed")
+ x_opt = None
+ u_opt = None
+
+ return x_opt, u_opt
+
diff --git a/guided_mrmp/controllers/multi_path_tracking.py b/guided_mrmp/controllers/multi_path_tracking.py
index 0015cc481695730ffa0ebcd2c38bc934ee94133d..89fe8ee6ba1ceb9957add2015b45edc675adbd47 100644
--- a/guided_mrmp/controllers/multi_path_tracking.py
+++ b/guided_mrmp/controllers/multi_path_tracking.py
@@ -1,111 +1,1028 @@
-from path_tracker import *
-
from guided_mrmp.planners.singlerobot.RRTStar import RRTStar
+from guided_mrmp.utils import Roomba
+from guided_mrmp.utils import Conflict, Robot, Env
-from guided_mrmp.utils import Conflict, Robot, Env, Plotting
+import numpy as np
+import matplotlib.pyplot as plt
-def plot(x_histories, y_histories, h_histories, wp_paths):
- plt.style.use("ggplot")
- fig = plt.figure()
- plt.ion()
- plt.show()
- plt.clf()
+from guided_mrmp.controllers.utils import compute_path_from_wp, get_ref_trajectory
+from guided_mrmp.controllers.multi_mpc import MultiMPC
+from guided_mrmp.conflict_resolvers.discrete_resolver import DiscreteResolver
+from guided_mrmp.utils import Roomba
- print(f"hist = {x_histories}")
+from guided_mrmp.conflict_resolvers.curve_path import smooth_path, calculate_headings
- for x_history, y_history, h_history, path in zip(x_histories, y_histories, h_histories, wp_paths):
- print(x_history)
- plt.plot(
- path[0, :],
- path[1, :],
- c="tab:orange",
- marker=".",
- label="reference track",
- )
+def initialize_libraries(library_fnames=["guided_mrmp/database/2x3_library","guided_mrmp/database/3x3_library","guided_mrmp/database/5x2_library"]):
+ """
+ Load the 2x3, 3x3, and 2x5 libraries. Store them in self.lib-x-
+ Inputs:
+ library_fnames - the folder containing the library files
+ """
+ from guided_mrmp.utils.library import Library
+ # Create each of the libraries
+ print(f"Loading libraries. This usually takes about 10 seconds...")
+ lib_2x3 = Library(library_fnames[0])
+ lib_2x3.read_library_from_file()
+
+ lib_3x3 = Library(library_fnames[1])
+ lib_3x3.read_library_from_file()
- plt.plot(
- x_history,
- y_history,
- c="tab:blue",
- marker=".",
- alpha=0.5,
- label="vehicle trajectory",
- )
+
+ lib_2x5 = Library(library_fnames[2])
+ lib_2x5.read_library_from_file()
+
+ return lib_2x3, lib_3x3, lib_2x5
+
+class DiscreteRobot:
+ def __init__(self, start, goal, label):
+ self.start = start
+ self.goal = goal
+ self.current_position = start
+ self.label = label
+
+class MultiPathTracker:
+ def __init__(self, env, initial_positions, dynamics, target_v, T, DT, waypoints, settings, lib_2x3, lib_3x3, lib_2x5):
+ """
+ Initializes the PathTracker object.
+ Parameters:
+ - initial_positions: List of the initial positions of the robots [x, y, heading].
+ - dynamics: The dynamics model of the robots.
+ - target_v: The target velocity of the robots.
+ - T: The time horizon for the model predictive control (MPC).
+ - DT: The time step for the MPC.
+ - waypoints: A list of waypoints defining the desired path for each robot.
+ """
+ # State of the robot [x,y, heading]
+ self.env = env
+
+ self.states = initial_positions
+ self.num_robots = len(initial_positions)
+ self.dynamics = dynamics
+ self.T = T
+ self.DT = DT
+ self.target_v = target_v
+
+ self.radius = dynamics.radius
+
+
+ self.update_ref_paths = False
+
+ # helper variable to keep track of mpc output
+ # starting condition is 0,0
+ self.control = np.zeros((self.num_robots, 2))
+
+ self.K = int(T / DT)
+
+ # For a car model
+ # Q = [20, 20, 10, 20] # state error cost
+ # Qf = [30, 30, 30, 30] # state final error cost
+ # R = [10, 10] # input cost
+ # P = [10, 10] # input rate of change cost
+ # self.mpc = MPC(VehicleModel(), T, DT, Q, Qf, R, P)
+
+ # libraries for the discrete solver
+ self.lib_2x3 = lib_2x3
+ self.lib_3x3 = lib_3x3
+ self.lib_2x5 = lib_2x5
+
+
+ # For a circular robot (easy dynamics)
+ Q = [40, 40, 0] # state error cost
+ Qf = [20,20, 0] # state final error cost
+ R = [10, 10] # input cost
+ P = [10, 10] # input rate of change cost
+ self.mpc = MultiMPC(self.num_robots, dynamics, T, DT, Q, Qf, R, P, settings['model_predictive_controller'], settings['environment']['circle_obstacles'])
+
+ self.circle_obs = settings['environment']['circle_obstacles']
+
+ # Path from waypoint interpolation
+ self.paths = []
+ for wp in waypoints:
+ self.paths.append(compute_path_from_wp(wp[0], wp[1], 0.05))
+
+
+ print(f"paths = {len(self.paths)}")
+
+ # Helper variables to keep track of the sim
+ self.sim_time = 0
+ self.x_history = [ [] for _ in range(self.num_robots) ]
+ self.y_history = [ [] for _ in range(self.num_robots) ]
+ self.v_history = [ [] for _ in range(self.num_robots) ]
+ self.h_history = [ [] for _ in range(self.num_robots) ]
+ self.a_history = [ [] for _ in range(self.num_robots) ]
+ self.d_history = [ [] for _ in range(self.num_robots) ]
+ self.optimized_trajectories_hist = [ [] for _ in range(self.num_robots) ]
+ self.optimized_trajectory = None
+
+
+ def trajectories_overlap(self, traj1, traj2, threshold):
+ """
+ Checks if two trajectories overlap. We only care about xy positions.
+
+ Args:
+ traj1 (3xn numpy array): First trajectory. First row is x, second row is y, third row is heading.
+ traj2 (3xn numpy array): Second trajectory.
+ threshold (float): Distance threshold to consider a collision.
- plot_roomba(x_history[-1], y_history[-1], h_history[-1])
+ Returns:
+ bool: True if trajectories overlap, False otherwise.
+ """
+ for i in range(traj1.shape[1]):
+ for j in range(traj2.shape[1]):
+ if np.linalg.norm(traj1[0:2, i] - traj2[0:2, j]) < 2*threshold:
+ return True
+ return False
+
+
+ def ego_to_global_roomba(self, state, mpc_out):
+ """
+ Transforms optimized trajectory XY points from ego (robot) reference
+ into global (map) frame.
+
+ Args:
+ mpc_out (numpy array): Optimized trajectory points in ego reference frame.
+
+ Returns:
+ numpy array: Transformed trajectory points in global frame.
+ """
+ # Extract x, y, and theta from the state
+ x = state[0]
+ y = state[1]
+ theta = state[2]
+
+ # Rotation matrix to transform points from ego frame to global frame
+ Rotm = np.array([
+ [np.cos(theta), -np.sin(theta)],
+ [np.sin(theta), np.cos(theta)]
+ ])
+
+ # Initialize the trajectory array (only considering XY points)
+ trajectory = mpc_out[0:2, :]
+
+ # Apply rotation to the trajectory points
+ trajectory = Rotm.dot(trajectory)
+ # Translate the points to the robot's position in the global frame
+ trajectory[0, :] += x
+ trajectory[1, :] += y
- plt.ylabel("y")
- plt.yticks(
- np.arange(min(path[1, :]) - 1.0, max(path[1, :] + 1.0) + 1, 1.0)
+ return trajectory
+
+ def get_next_control(self, state, show_plots=False):
+ # optimization loop
+ # start=time.time()
+
+ # Get Reference_traj -> inputs are in worldframe
+ # 1. Get the reference trajectory for each robot
+ targets = []
+ for i in range(self.num_robots):
+ targets.append(get_ref_trajectory(np.array(state[i]), np.array(self.paths[i]), self.target_v, self.T, self.DT, len(self.x_history[i])+1))
+
+ # dynamycs w.r.t robot frame
+ # curr_state = np.array([0, 0, self.state[2], 0])
+ curr_states = np.zeros((self.num_robots, 3))
+ x_mpc, u_mpc = self.mpc.step(
+ curr_states,
+ targets,
+ self.control
)
- plt.xlabel("x")
- plt.xticks(
- np.arange(min(path[0, :]) - 1.0, max(path[0, :] + 1.0) + 1, 1.0)
+
+ # only the first one is used to advance the simulation
+ # self.control[:] = [u_mpc[0, 0], u_mpc[1, 0]]
+
+ self.control = []
+ for i in range(self.num_robots):
+ self.control.append([u_mpc[i*2, 0], u_mpc[i*2+1, 0]])
+
+ return x_mpc, self.control
+
+
+ def done(self):
+ for i in range(self.num_robots):
+ # print(f"state = {self.states[i]}")
+ # print(f"path = {self.paths[i][:, -1]}")
+ if (np.sqrt((self.states[i][0] - self.paths[i][0, -1]) ** 2 + (self.states[i][1] - self.paths[i][1, -1]) ** 2) > 1):
+ return False
+ return True
+
+ def plot_current_world_state(self):
+ """
+ Plot the current state of the world.
+ """
+
+ import matplotlib.pyplot as plt
+ import matplotlib.cm as cm
+
+ # Plot the current state of each robot using the most recent values from
+ # x_history, y_history, and h_history
+ colors = cm.rainbow(np.linspace(0, 1, self.num_robots))
+
+ for i in range(self.num_robots):
+ plot_roomba(self.x_history[i][-1], self.y_history[i][-1], self.h_history[i][-1], colors[i], False, self.radius)
+
+ # plot the goal of each robot with solid circle
+ for i in range(self.num_robots):
+ x, y, theta = self.paths[i][:, -1]
+ plt.plot(x, y, 'o', color=colors[i])
+ circle1 = plt.Circle((x, y), self.radius, color=colors[i], fill=False)
+ plt.gca().add_artist(circle1)
+
+ # plot the ref path of each robot
+ for i in range(self.num_robots):
+ plt.plot(self.paths[i][0, :], self.paths[i][1, :], '--', color=colors[i])
+
+
+ # set the size of the plot to be 10x10
+ plt.xlim(0, 10)
+ plt.ylim(0, 10)
+
+ # force equal aspect ratio
+ plt.gca().set_aspect('equal', adjustable='box')
+
+
+ plt.show()
+
+ def run(self, show_plots=False):
+ """
+ Run the path tracker algorithm.
+ Parameters:
+ - show_plots (bool): Flag indicating whether to show plots during the simulation. Default is False.
+ Returns:
+ - numpy.ndarray: Array containing the history of x, y, and h coordinates.
+ """
+
+ # Add the initial state to the histories
+ self.states = np.array(self.states)
+ for i in range(self.num_robots):
+ self.x_history[i].append(self.states[i, 0])
+ self.y_history[i].append(self.states[i, 1])
+ self.h_history[i].append(self.states[i, 2])
+ if show_plots: self.plot_sim()
+
+ self.plot_current_world_state()
+
+ while 1:
+ # check if all robots have reached their goal
+ if self.done():
+ print("Success! Goal Reached")
+ return np.asarray([self.x_history, self.y_history, self.h_history])
+
+ # plot the current state of the robots
+ self.plot_current_world_state()
+
+ # get the next control for all robots
+ x_mpc, controls = self.get_next_control(self.states)
+
+ next_states = []
+ for i in range(self.num_robots):
+ next_states.append(self.dynamics.next_state(self.states[i], controls[i], self.DT))
+
+ self.states = next_states
+
+ self.states = np.array(self.states)
+ for i in range(self.num_robots):
+ self.x_history[i].append(self.states[i, 0])
+ self.y_history[i].append(self.states[i, 1])
+ self.h_history[i].append(self.states[i, 2])
+
+ if self.update_ref_paths:
+ self.update_reference_paths()
+ self.update_ref_paths = False
+
+ # use the optimizer output to preview the predicted state trajectory
+ # self.optimized_trajectory = self.ego_to_global(x_mpc.value)
+ if show_plots: self.optimized_trajectory = self.ego_to_global_roomba(x_mpc)
+ if show_plots: self.plot_sim()
+
+
+class MultiPathTrackerDatabase(MultiPathTracker):
+ def get_temp_starts_and_goals(self):
+ # the temporary starts are the current positions of the robots snapped to the grid
+ # based on the continuous space location of the robot, we find the cell in the grid that
+ # includes that continuous space location using the top left of the grid as a reference point
+
+ import math
+ temp_starts = []
+ for r in range(self.num_robots):
+ print(f"self.states = {self.states}")
+ x, y, theta = self.states[r]
+ cell_x = min(max(math.floor((x - self.top_left_grid[0]) / self.cell_size), 0), self.grid_size - 1)
+ cell_y = min(max(math.floor((self.top_left_grid[1] - y) / self.cell_size), 0), self.grid_size - 1)
+ temp_starts.append([cell_x, cell_y])
+
+
+ # the temmporary goal is the point at the end of the robot's predicted traj
+ temp_goals = []
+ for r in range(self.num_robots):
+ traj = self.ego_to_global_roomba(self.states[r], self.trajs[r])
+ x = traj[0][-1]
+ y = traj[1][-1]
+ cell_x = min(max(math.floor((x - self.top_left_grid[0]) / self.cell_size), 0), self.grid_size - 1)
+ cell_y = min(max(math.floor((self.top_left_grid[1] - y) / self.cell_size), 0), self.grid_size - 1)
+ temp_goals.append([cell_x,cell_y])
+
+ # self.starts = temp_starts
+ # self.goals = temp_goals
+
+ return temp_starts, temp_goals
+
+ def create_discrete_robots(self, starts, goals):
+ discrete_robots = []
+ for i in range(len(starts)):
+ start = starts[i]
+ goal = goals[i]
+ discrete_robots.append(DiscreteRobot(start, goal, i))
+ return discrete_robots
+
+ def get_discrete_solution(self, conflict, all_conflicts, grid):
+ # create an instance of a discrete solver
+
+ starts, goals = self.get_temp_starts_and_goals()
+ # print(f"temp starts = {starts}")
+ # print(f"temp goals = {goals}")
+
+ disc_robots = self.create_discrete_robots(starts, goals)
+
+ disc_conflict = []
+ for c in conflict:
+ disc_conflict.append(disc_robots[c])
+
+ disc_all_conflicts = []
+ for c in all_conflicts:
+ this_conflict = []
+ for i in c:
+ this_conflict.append(disc_robots[i])
+ disc_all_conflicts.append(this_conflict)
+
+
+
+ print(f"this conflict = {disc_conflict}")
+ print(f"all conflicts = {all_conflicts}")
+
+ # visualize the grid
+ self.draw_grid()
+
+ grid_solver = DiscreteResolver(disc_conflict, disc_robots, starts, goals, disc_all_conflicts,grid, self.lib_2x3, self.lib_3x3, self.lib_2x5)
+ subproblem = grid_solver.find_subproblem()
+
+ if subproblem is None:
+ print("Couldn't find a discrete subproblem")
+ return None
+ # print(f"subproblem = {subproblem}")
+ grid_solution = grid_solver.solve_subproblem(subproblem)
+ # print(f"grid_solution = {grid_solution}")
+ return grid_solution
+
+ def get_initial_guess(self, grid_solution, num_robots, N, cp_dist):
+ # turn this solution into an initial guess
+ # turn this solution into an initial guess
+ initial_guess_state = np.zeros((num_robots*3, N+1))
+ # the initial guess for time is the length of the longest path in the solution
+ initial_guess_T = 2*max([len(grid_solution[i]) for i in range(num_robots)])
+
+ for i in range(num_robots):
+
+ print(f"Robot {i+1} solution:")
+ rough_points = np.array(grid_solution[i])
+ points = []
+ for point in rough_points:
+ if point[0] == -1: break
+ points.append(point)
+
+ points = np.array(points)
+ print(f"points = {points}")
+
+ smoothed_curve, _ = smooth_path(points, N+1, cp_dist)
+
+ print(f"smoothed_curve = {smoothed_curve}")
+
+
+
+ # translate the smoothed curve so that the first point is at the current robot position
+ # smoothed_curve[:, 0] += current_robot_x_pos
+ # smoothed_curve[:, 1] += current_robot_y_pos
+
+ initial_guess_state[i*3, :] = (smoothed_curve[:, 0])*self.cell_size # x
+ initial_guess_state[i*3 + 1, :] = (smoothed_curve[:, 1])*self.cell_size # y
+
+ current_robot_x_pos = self.states[i][0]
+ current_robot_y_pos = self.states[i][1]
+
+
+ # translate the initial guess so that the first point is at (0,0)
+ initial_guess_state[i*3, :] -= initial_guess_state[i*3, 0]
+ initial_guess_state[i*3 + 1, :] -= initial_guess_state[i*3+1, 0]
+
+ # translate the initial guess so that the first point is at the current robot position
+ initial_guess_state[i*3, :] += current_robot_x_pos
+ initial_guess_state[i*3 + 1, :] += current_robot_y_pos + self.cell_size
+
+
+ headings = calculate_headings(smoothed_curve)
+ headings.append(headings[-1])
+
+ initial_guess_state[i*3 + 2, :] = headings
+
+
+
+ initial_guess_control = np.zeros((num_robots*2, N))
+
+ dt = initial_guess_T / N
+ change_in_position = []
+ for i in range(num_robots):
+ x = initial_guess_state[i*3, :] # x
+ y = initial_guess_state[i*3 + 1, :] # y
+
+
+ change_in_position = []
+ for j in range(len(x)-1):
+ pos1 = np.array([x[j], y[j]])
+ pos2 = np.array([x[j+1], y[j+1]])
+
+ change_in_position.append(np.linalg.norm(pos2 - pos1))
+
+ velocity = np.array(change_in_position) / dt
+ initial_guess_control[i*2, :] = velocity
+
+ # omega is the difference between consecutive headings
+ headings = initial_guess_state[i*3 + 2, :]
+ omega = np.diff(headings)
+ initial_guess_control[i*2 + 1, :] = omega
+
+ return {'X': initial_guess_state, 'U': initial_guess_control, 'T': initial_guess_T}
+
+ def place_grid(self, robot_locations):
+ """
+ Given the locations of robots that need to be covered in continuous space, find
+ and place the grid. We don't need a very large grid to place subproblems, so
+ we will only place a grid of size self.grid_size x self.grid_size
+
+ inputs:
+ - robot_locations (list): locations of robots involved in conflict
+ outputs:
+ - grid (numpy array): The grid that we placed
+ - top_left (tuple): The top left corner of the grid in continuous space
+ """
+ # Find the minimum and maximum x and y coordinates of the robot locations
+ self.min_x = min(robot_locations, key=lambda loc: loc[0])[0]
+ self.max_x = max(robot_locations, key=lambda loc: loc[0])[0]
+ self.min_y = min(robot_locations, key=lambda loc: loc[1])[1]
+ self.max_y = max(robot_locations, key=lambda loc: loc[1])[1]
+
+ # find the average x and y coordinates of the robot locations
+ avg_x = sum([loc[0] for loc in robot_locations]) / len(robot_locations)
+ avg_y = sum([loc[1] for loc in robot_locations]) / len(robot_locations)
+
+ self.temp_avg_x = avg_x
+ self.temp_avg_y = avg_y
+
+ print(f"avg_x = {avg_x}, avg_y = {avg_y}")
+
+ # Calculate the top left corner of the grid
+ # make it so that the grid is centered around the robots
+ self.cell_size = self.radius*3
+ self.grid_size = 5
+
+ print(f"avg_x = {avg_x} - {int(self.grid_size*self.cell_size/2)}")
+ print(f"avg_y = {avg_y} - {int(self.grid_size*self.cell_size/2)}")
+ self.top_left_grid = (avg_x - int(self.grid_size*self.cell_size/2), avg_y + int(self.grid_size*self.cell_size/2))
+ print(f"self.grid_size = {self.grid_size}")
+ print(f"top_left_grid = {self.top_left_grid}")
+
+ self.draw_grid()
+
+ # Check if, for every robot, the cell value of the start and the cell value
+ # of the goal are the same. If they are, then we can't find a discrete solution that
+ # will make progress.
+ all_starts_goals_equal = self.all_starts_goals_equal()
+
+
+
+ import copy
+ original_top_left = copy.deepcopy(self.top_left_grid)
+
+ x_shift = [-5,5]
+ y_shift = [-5,5]
+ for x in np.arange(x_shift[0], x_shift[1],.5):
+ y =0
+ # print(f"x = {x}, y = {y}")
+ self.top_left_grid = (original_top_left[0] + x*self.cell_size*.5, original_top_left[1] - y*self.cell_size*.5)
+ all_starts_goals_equal = self.all_starts_goals_equal()
+ # self.draw_grid()
+ if not all_starts_goals_equal: break
+
+ if all_starts_goals_equal:
+ for y in np.arange(y_shift[0], y_shift[1],.5):
+ x =0
+ # print(f"x = {x}, y = {y}")
+ self.top_left_grid = (original_top_left[0] + x*self.cell_size*.5, original_top_left[1] - y*self.cell_size*.5)
+ all_starts_goals_equal = self.all_starts_goals_equal()
+ # self.draw_grid()
+ if not all_starts_goals_equal: break
+
+ print(f"updated top_left_grid = {self.top_left_grid}")
+ # self.draw_grid()
+
+ if all_starts_goals_equal:
+ print("All starts and goals are equal")
+ return None
+
+ grid = self.get_obstacle_map()
+
+ return grid
+
+ def get_obstacle_map(self):
+ """
+ Create a map of the environment with obstacles
+ """
+ # create a grid of size self.grid_size x self.grid_size
+ grid = np.zeros((self.grid_size, self.grid_size))
+
+ # check if there are any obstacles in any of the cells
+ grid = np.zeros((self.grid_size, self.grid_size))
+ for i in range(self.grid_size):
+ for j in range(self.grid_size):
+ x, y = self.get_grid_cell_location(i, j)
+ for obs in []:
+ # for obs in self.circle_obs:
+ if np.linalg.norm(np.array([x, y]) - np.array(obs[:2])) < obs[2] + self.radius:
+ grid[j, i] = 1
+ break
+
+ return grid
+
+ def get_grid_cell(self, x, y):
+ """
+ Given a continuous space x and y, find the cell in the grid that includes that location
+ """
+ import math
+
+ # find the closest grid cell that is not an obstacle
+ cell_x = min(max(math.floor((x - self.top_left_grid[0]) / self.cell_size), 0), self.grid_size - 1)
+ cell_y = min(max(math.floor((self.top_left_grid[1] - y) / self.cell_size), 0), self.grid_size - 1)
+
+ return cell_x, cell_y
+
+ def get_grid_cell_location(self, cell_x, cell_y):
+ """
+ Given a cell in the grid, find the center of that cell in continuous space
+ """
+ x = self.top_left_grid[0] + (cell_x + 0.5) * self.cell_size
+ y = self.top_left_grid[1] - (cell_y + 0.5) * self.cell_size
+ return x, y
+
+ def plot_trajs(self, traj1, traj2, radius):
+ """
+ Plot the trajectories of two robots.
+ """
+ import matplotlib.pyplot as plt
+ import matplotlib.cm as cm
+
+ # Plot the current state of each robot using the most recent values from
+ # x_history, y_history, and h_history
+ colors = cm.rainbow(np.linspace(0, 1, self.num_robots))
+
+ for i in range(self.num_robots):
+ plot_roomba(self.x_history[i][-1], self.y_history[i][-1], self.h_history[i][-1], colors[i], False, self.radius)
+
+ # plot the goal of each robot with solid circle
+ for i in range(self.num_robots):
+ x, y, theta = self.paths[i][:, -1]
+ plt.plot(x, y, 'o', color=colors[i])
+ circle1 = plt.Circle((x, y), self.radius, color=colors[i], fill=False)
+ plt.gca().add_artist(circle1)
+
+
+ for i in range(traj1.shape[1]):
+ circle1 = plt.Circle((traj1[0, i], traj1[1, i]), radius, color='k', fill=False)
+ plt.gca().add_artist(circle1)
+
+ for i in range(traj2.shape[1]):
+ circle2 = plt.Circle((traj2[0, i], traj2[1, i]), radius, color='k', fill=False)
+ plt.gca().add_artist(circle2)
+
+
+
+ # set the size of the plot to be 10x10
+ plt.xlim(0, 10)
+ plt.ylim(0, 10)
+
+ # force equal aspect ratio
+ plt.gca().set_aspect('equal', adjustable='box')
+
+
+ plt.show()
+
+ def draw_grid(self):
+ starts, goals = self.get_temp_starts_and_goals()
+
+ # draw the whole environment with the local grid drawn on top
+ import matplotlib.pyplot as plt
+ import matplotlib.cm as cm
+
+ # Plot the current state of each robot using the most recent values from
+ # x_history, y_history, and h_history
+ colors = cm.rainbow(np.linspace(0, 1, self.num_robots))
+
+ for i in range(self.num_robots):
+ plot_roomba(self.x_history[i][-1], self.y_history[i][-1], self.h_history[i][-1], colors[i], False, self.radius)
+
+ # plot the goal of each robot with solid circle
+ for i in range(self.num_robots):
+ x, y, theta = self.paths[i][:, -1]
+ plt.plot(x, y, 'o', color=colors[i])
+ circle1 = plt.Circle((x, y), self.radius, color=colors[i], fill=False)
+ plt.gca().add_artist(circle1)
+
+ # draw the horizontal and vertical lines of the grid
+ for i in range(self.grid_size + 1):
+ # Draw vertical lines
+ plt.plot([self.top_left_grid[0] + i * self.cell_size, self.top_left_grid[0] + i * self.cell_size],
+ [self.top_left_grid[1], self.top_left_grid[1] - self.grid_size * self.cell_size], 'k-')
+ # Draw horizontal lines
+ plt.plot([self.top_left_grid[0], self.top_left_grid[0] + self.grid_size * self.cell_size],
+ [self.top_left_grid[1] - i * self.cell_size, self.top_left_grid[1] - i * self.cell_size], 'k-')
+
+ # draw the obstacles
+ for obs in self.circle_obs:
+ circle = plt.Circle((obs[0], obs[1]), obs[2], color='red', fill=False)
+ plt.gca().add_artist(circle)
+
+
+ # plot the robots' continuous space subgoals
+ for idx in range(self.num_robots):
+
+ traj = self.ego_to_global_roomba(self.states[idx], self.trajs[idx])
+ x = traj[0][-1]
+ y = traj[1][-1]
+ plt.plot(x, y, '^', color=colors[idx])
+ circle1 = plt.Circle((x, y), self.radius, color=colors[idx], fill=False)
+ plt.gca().add_artist(circle1)
+
+ # set the size of the plot to be 10x10
+ plt.xlim(0, 10)
+ plt.ylim(0, 10)
+
+ # force equal aspect ratio
+ plt.gca().set_aspect('equal', adjustable='box')
+
+ plt.show()
+
+ def draw_grid_solution(self, state):
+
+ # draw the whole environment with the local grid drawn on top
+ import matplotlib.pyplot as plt
+ import matplotlib.cm as cm
+
+ # Plot the current state of each robot using the most recent values from
+ # x_history, y_history, and h_history
+ colors = cm.rainbow(np.linspace(0, 1, self.num_robots))
+
+ for i in range(self.num_robots):
+ plot_roomba(self.x_history[i][-1], self.y_history[i][-1], self.h_history[i][-1], colors[i], False, self.radius)
+
+ # plot the goal of each robot with solid circle
+ for i in range(self.num_robots):
+ x, y, theta = self.paths[i][:, -1]
+ plt.plot(x, y, 'o', color=colors[i])
+ circle1 = plt.Circle((x, y), self.radius, color=colors[i], fill=False)
+ plt.gca().add_artist(circle1)
+
+ # draw the horizontal and vertical lines of the grid
+ for i in range(self.grid_size + 1):
+ # Draw vertical lines
+ plt.plot([self.top_left_grid[0] + i * self.cell_size, self.top_left_grid[0] + i * self.cell_size],
+ [self.top_left_grid[1], self.top_left_grid[1] - self.grid_size * self.cell_size], 'k-')
+ # Draw horizontal lines
+ plt.plot([self.top_left_grid[0], self.top_left_grid[0] + self.grid_size * self.cell_size],
+ [self.top_left_grid[1] - i * self.cell_size, self.top_left_grid[1] - i * self.cell_size], 'k-')
+
+ # draw the obstacles
+ for obs in self.circle_obs:
+ circle = plt.Circle((obs[0], obs[1]), obs[2], color='red', fill=False)
+ plt.gca().add_artist(circle)
+
+ for i in range(self.num_robots):
+ x = state[i*3, :]
+ y = state[i*3 + 1, :]
+ plt.plot(x, y, 'x', color=colors[i])
+
+ # plot the robots' continuous space subgoals
+ for idx in range(self.num_robots):
+
+ traj = self.ego_to_global_roomba(self.states[idx], self.trajs[idx])
+ x = traj[0][-1]
+ y = traj[1][-1]
+ plt.plot(x, y, '^', color=colors[idx])
+ circle1 = plt.Circle((x, y), self.radius, color=colors[idx], fill=False)
+ plt.gca().add_artist(circle1)
+
+ # set the size of the plot to be 10x10
+ plt.xlim(0, 10)
+ plt.ylim(0, 10)
+
+ # force equal aspect ratio
+ plt.gca().set_aspect('equal', adjustable='box')
+
+ # title
+ plt.title("Discrete Solution")
+
+ plt.show()
+
+ def all_starts_goals_equal(self):
+ """
+ Check if, for every robot, the cell value of the start and the cell value
+ of the goal are the same.
+ """
+ all_starts_goals_equal = True
+ for r in range(self.num_robots):
+ start = self.states[r]
+ traj = self.ego_to_global_roomba(start, self.trajs[r])
+ goal = [traj[0, -1], traj[1, -1]]
+
+ start_cell = self.get_grid_cell(start[0], start[1])
+ goal_cell = self.get_grid_cell(goal[0], goal[1])
+
+ if start_cell != goal_cell:
+ all_starts_goals_equal = False
+ break
+
+ return all_starts_goals_equal
+
+ def get_next_control(self, state, show_plots=False):
+ # optimization loop
+ # start=time.time()
+
+ self.update_ref_paths = False
+
+ # Get Reference_traj -> inputs are in worldframe
+ # 1. Get the reference trajectory for each robot
+ targets = []
+ for i in range(self.num_robots):
+ ref = get_ref_trajectory(np.array(state[i]), np.array(self.paths[i]), self.target_v, self.T, self.DT,0)
+
+ print(f"Robot {i} reference trajectory = {ref}")
+ targets.append(ref)
+ self.trajs = targets
+
+ # 2. Check if the targets of any two robots overlap
+ self.all_conflicts = []
+ for i in range(self.num_robots):
+ for j in range(i + 1, self.num_robots):
+ print(f"targets[i] = {targets[i]}")
+ traj1 = self.ego_to_global_roomba(state[i], targets[i])
+ traj2 = self.ego_to_global_roomba(state[j], targets[j])
+ if self.trajectories_overlap(traj1, traj2, self.radius):
+ # plot the trajectories
+
+ self.plot_trajs(traj1, traj2, self.radius)
+
+
+ print(f"Collision detected between robot {i} and robot {j}")
+ self.all_conflicts.append((i, j))
+
+
+
+ for c in self.all_conflicts:
+ # 3. If they do collide, then reroute the reference trajectories of these robots
+
+ # Get the robots involved in the conflict
+ robots = c
+ robot_positions = [state[i] for i in robots]
+
+ # Put down a local grid
+ self.grid = self.place_grid(robot_positions)
+
+ # set the starts (robots' current positions)
+ self.starts = []
+ self.goals = []
+ for i in range(self.num_robots):
+ self.starts.append(self.states[i])
+
+ traj = self.ego_to_global_roomba(self.states[i], self.trajs[i])
+ x = traj[0][-1]
+ y = traj[1][-1]
+ self.goals.append([x,y])
+
+
+
+ # Solve a discrete version of the problem
+ # Find a subproblem and solve it
+ grid_solution = self.get_discrete_solution(c, [c],self.grid)
+
+
+
+ if grid_solution:
+
+ self.update_ref_paths = False
+ initial_guess = self.get_initial_guess(grid_solution, self.num_robots, 20, 1)
+ initial_guess_state = initial_guess['X']
+
+ self.draw_grid_solution(initial_guess_state)
+
+ print(f"initial_guess_state shape = {initial_guess_state.shape}")
+ print(f"initial_guess_state = {initial_guess_state}")
+
+ # for each robot in conflict, reroute its reference trajectory to match the grid solution
+ num_robots_in_conflict = len(c)
+ import copy
+ old_paths = copy.deepcopy(self.paths)
+
+ self.paths = []
+ for i in range(num_robots_in_conflict):
+ r = c[i]
+ new_ref = initial_guess_state[i*3:i*3+3, :]
+ print(f"Robot {r} rerouting to {new_ref}")
+
+ # plan from the last point of the ref path to the robot's goal
+ # plan an RRT path from the current state to the goal
+ x_start = (new_ref[:, -1][0], new_ref[:, -1][1])
+ x_goal = (old_paths[i][:, -1][0], old_paths[i][:, -1][1])
+
+ print(f"x_start = {x_start}, x_goal = {x_goal}")
+
+ rrtstar2 = RRTStar(self.env,x_start, x_goal, 0.5, 0.05, 1000, r=2.0)
+ rrtstarpath2,tree = rrtstar2.run()
+ rrtstarpath2 = list(reversed(rrtstarpath2))
+ xs = new_ref[0, :].tolist()
+ ys = new_ref[1, :].tolist()
+ for node in rrtstarpath2:
+ xs.append(node[0])
+ ys.append(node[1])
+
+ wp = [xs,ys]
+
+ # Path from waypoint interpolation
+ self.paths.append(compute_path_from_wp(wp[0], wp[1], 0.05))
+
+ targets = []
+ for i in range(self.num_robots):
+ ref = get_ref_trajectory(np.array(state[i]), np.array(self.paths[i]), self.target_v, self.T, self.DT,0)
+
+ print(f"Robot {i} reference trajectory = {ref}")
+ targets.append(ref)
+ self.trajs = targets
+
+ if grid_solution is None:
+ # if there isnt a grid solution, the most likely scenario is that the robots
+ # are not close enough together to place down a subproblem
+ # in this case, we just allow the robts to continue on their paths and resolve
+ # the conflict later
+ print("No grid solution found, proceeding with the current paths")
+
+
+
+
+ # dynamycs w.r.t robot frame
+ # curr_state = np.array([0, 0, self.state[2], 0])
+ curr_states = np.zeros((self.num_robots, 3))
+ x_mpc, u_mpc = self.mpc.step(
+ curr_states,
+ targets,
+ self.control
)
- plt.axis("equal")
+
+ # only the first one is used to advance the simulation
+ # self.control[:] = [u_mpc[0, 0], u_mpc[1, 0]]
+
+ self.control = []
+ for i in range(self.num_robots):
+ self.control.append([u_mpc[i*2, 0], u_mpc[i*2+1, 0]])
+
+ # if len(self.all_conflicts) > 0:
+ # # update the reference paths for each robot
+ # if grid_solution:
+ # self.update_reference_paths()
+
+ return x_mpc, self.control
+ def update_reference_paths(self):
+ """
+ Update the reference paths for each robot.
+ """
+ # create copy of current self.paths
+ import copy
+ old_paths = copy.deepcopy(self.paths)
- plt.tight_layout()
+ self.paths = []
+ for i in range(self.num_robots):
+ # plan an RRT path from the current state to the goal
+ x_start = (self.states[i][0], self.states[i][1])
+ x_goal = (old_paths[i][:, -1][0], old_paths[i][:, -1][1])
+ rrtstar2 = RRTStar(self.env,x_start, x_goal, 0.5, 0.05, 1000, r=2.0)
+ rrtstarpath2,tree = rrtstar2.run()
+ rrtstarpath2 = list(reversed(rrtstarpath2))
+ xs = []
+ ys = []
+ for node in rrtstarpath2:
+ xs.append(node[0])
+ ys.append(node[1])
- plt.draw()
- plt.pause(0.1)
- input()
+ wp = [xs,ys]
-if __name__ == "__main__":
+ # Path from waypoint interpolation
+ self.paths.append(compute_path_from_wp(wp[0], wp[1], 0.05))
+
+def main():
+ import os
+ import numpy as np
+ import random
+
+ # load the settings
+ file_path = "settings_files/settings.yaml"
+ import yaml
+ with open(file_path, 'r') as file:
+ settings = yaml.safe_load(file)
+
+ seed = 1123
+ print(f"***Setting Python Seed {seed}***")
+ os.environ['PYTHONHASHSEED'] = str(seed)
+ np.random.seed(seed)
+ random.seed(seed)
- initial_pos_1 = np.array([0.0, 0.1, 0.0, 0.0])
+
+ initial_pos_1 = np.array([6.0, 2.0, 2.2])
target_vocity = 3.0 # m/s
- T = 1 # Prediction Horizon [s]
- DT = 0.2 # discretization step [s]
+ T = .5 # Prediction Horizon [s]
+ DT = 0.1 # discretization step [s]
- x_start = (0, 0) # Starting node
- x_goal = (10, 3) # Goal node
+ x_start = (6, 2) # Starting node
+ x_goal = (6.5, 8) # Goal node
env = Env([0,10], [0,10], [], [])
- rrtstar = RRTStar(env, x_start, x_goal, 0.5, 0.05, 500, r=2.0)
- rrtstarpath = rrtstar.run()
- rrtstarpath = list(reversed(rrtstarpath))
+ dynamics = Roomba(settings)
+
+ rrtstar1 = RRTStar(env, x_start, x_goal, 0.5, 0.05, 500, r=2.0)
+ rrtstarpath1,tree = rrtstar1.run()
+ rrtstarpath1 = list(reversed(rrtstarpath1))
xs = []
ys = []
- for node in rrtstarpath:
+ for node in rrtstarpath1:
+ print(node)
+ print()
xs.append(node[0])
ys.append(node[1])
wp_1 = [xs,ys]
- sim = PathTracker(initial_position=initial_pos_1, target_v=target_vocity, T=T, DT=DT, waypoints=wp_1)
- x1,y1,h1 = sim.run(show_plots=False)
- path1 = sim.path
+
+ print(f"wp_1 = {wp_1}")
+ # sim = PathTracker(initial_position=initial_pos_1, dynamics=dynamics,target_v=target_vocity, T=T, DT=DT, waypoints=wp_1, settings=settings)
+ # x1,y1,h1 = sim.run(show_plots=False)
+ # path1 = sim.path
- initial_pos_2 = np.array([10.0, 5.1, 0.0, 0.0])
+ initial_pos_2 = np.array([6.0, 8.0, 4.8])
target_vocity = 3.0 # m/s
- x_start = (10, 5) # Starting node
- x_goal = (1, 1) # Goal node
- rrtstar = RRTStar(env,x_start, x_goal, 0.5, 0.05, 500, r=2.0)
- rrtstarpath = rrtstar.run()
- rrtstarpath = list(reversed(rrtstarpath))
+ x_start = (6, 8) # Starting node
+ x_goal = (6.5, 2) # Goal node
+ rrtstar2 = RRTStar(env,x_start, x_goal, 0.5, 0.05, 500, r=2.0)
+ rrtstarpath2,tree = rrtstar2.run()
+ rrtstarpath2 = list(reversed(rrtstarpath2))
xs = []
ys = []
- for node in rrtstarpath:
+ for node in rrtstarpath2:
xs.append(node[0])
ys.append(node[1])
wp_2 = [xs,ys]
-
- sim2 = PathTracker(initial_position=initial_pos_2, target_v=target_vocity, T=T, DT=DT, waypoints=wp_2)
- x2,y2,h2 = sim2.run(show_plots=False)
- path2 = sim2.path
+ lib_2x3, lib_3x3, lib_2x5 = initialize_libraries()
- # for
+ sim = MultiPathTrackerDatabase(env, [initial_pos_1, initial_pos_2], dynamics, target_vocity, T, DT, [wp_1, wp_2], settings, lib_2x3, lib_3x3, lib_2x5)
+ xs, ys, hs = sim.run(show_plots=False)
+ paths = sim.paths
- plot([x1,x2], [y1,y2], [h1,h2], [path1, path2])
+ print(f"path length here = {len(paths)}")
+ # plot(xs, ys, hs, paths, [rrtstar1.sampled_vertices, rrtstar2.sampled_vertices],2)
-
-
\ No newline at end of file
+ # plot_sim(xs, ys, hs, paths)
+
+def plot_roomba(x, y, yaw, color, fill, radius):
+ """
+
+ Args:
+ x ():
+ y ():
+ yaw ():
+ """
+ fig = plt.gcf()
+ ax = fig.gca()
+ if fill: alpha = .3
+ else: alpha = 1
+ circle = plt.Circle((x, y), radius, color=color, fill=fill, alpha=alpha)
+ ax.add_patch(circle)
+
+ # Plot direction marker
+ dx = 1 * np.cos(yaw)
+ dy = 1 * np.sin(yaw)
+ ax.arrow(x, y, dx, dy, head_width=0.1, head_length=0.1, fc='r', ec='r')
+
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/guided_mrmp/controllers/path_tracker.py b/guided_mrmp/controllers/path_tracker.py
index 29fedd80b582e9c53cc5364d1f0de0d3730d9528..5f7b987265f5ba1160a0d8be6952af09d9267663 100644
--- a/guided_mrmp/controllers/path_tracker.py
+++ b/guided_mrmp/controllers/path_tracker.py
@@ -7,7 +7,7 @@ from guided_mrmp.utils import Roomba
# Classes
class PathTracker:
- def __init__(self, initial_position, dynamics, target_v, T, DT, waypoints):
+ def __init__(self, initial_position, dynamics, target_v, T, DT, waypoints, settings):
"""
Initializes the PathTracker object.
Parameters:
@@ -44,7 +44,7 @@ class PathTracker:
Qf = [30, 30, 30] # state final error cost
R = [10, 10] # input cost
P = [10, 10] # input rate of change cost
- self.mpc = MPC(dynamics, T, DT, Q, Qf, R, P)
+ self.mpc = MPC(dynamics, T, DT, Q, Qf, R, P, settings['model_predictive_controller'])
# Path from waypoint interpolation
self.path = compute_path_from_wp(waypoints[0], waypoints[1], 0.05)
@@ -125,7 +125,7 @@ class PathTracker:
# start=time.time()
# Get Reference_traj -> inputs are in worldframe
- target = get_ref_trajectory(np.array(state), np.array(self.path), self.target_v, self.T, self.DT)
+ target = get_ref_trajectory(np.array(state), np.array(self.path), self.target_v, self.T, self.DT,0)
# dynamycs w.r.t robot frame
# curr_state = np.array([0, 0, self.state[2], 0])
@@ -137,7 +137,7 @@ class PathTracker:
)
# only the first one is used to advance the simulation
- self.control[:] = [u_mpc.value[0, 0], u_mpc.value[1, 0]]
+ self.control[:] = [u_mpc[0, 0], u_mpc[1, 0]]
# self.state = self.predict_next_state(
# self.state, [self.control[0], self.control[1]], DT
# )
@@ -174,10 +174,9 @@ class PathTracker:
# use the optimizer output to preview the predicted state trajectory
# self.optimized_trajectory = self.ego_to_global(x_mpc.value)
- if show_plots: self.optimized_trajectory = self.ego_to_global_roomba(x_mpc.value)
+ if show_plots: self.optimized_trajectory = self.ego_to_global_roomba(x_mpc)
if show_plots: self.plot_sim()
-
def plot_sim(self):
self.sim_time = self.sim_time + self.DT
# self.x_history.append(self.state[0])
@@ -291,19 +290,21 @@ def plot_roomba(x, y, yaw):
dy = 1 * np.sin(yaw)
ax.arrow(x, y, dx, dy, head_width=0.1, head_length=0.1, fc='r', ec='r')
-
-
-
if __name__ == "__main__":
# Example usage
+ file_path = "settings_files/settings.yaml"
+ import yaml
+ with open(file_path, 'r') as file:
+ settings = yaml.safe_load(file)
+
initial_pos = np.array([0.0, 0.5, 0.0, 0.0])
- dynamics = Roomba()
+ dynamics = Roomba(settings)
target_vocity = 3.0 # m/s
T = 1 # Prediction Horizon [s]
DT = 0.2 # discretization step [s]
wp = [[0, 3, 4, 6, 10, 12, 13, 13, 6, 1, 0],
[0, 0, 2, 4, 3, 3, -1, -2, -6, -2, -2]]
- sim = PathTracker(initial_position=initial_pos, dynamics=dynamics, target_v=target_vocity, T=T, DT=DT, waypoints=wp)
+ sim = PathTracker(initial_position=initial_pos, dynamics=dynamics, target_v=target_vocity, T=T, DT=DT, waypoints=wp, settings=settings)
x,y,h = sim.run(show_plots=True)
\ No newline at end of file