remove Params class

mpc_pybullet_demo/cvxpy_mpc/utils.py

@@ -59,7 +59,7 @@ def get_nn_idx(state, path):
     return target_idx
 
 
-def get_ref_trajectory(state, path, target_v, double T, double DT):
+def get_ref_trajectory(state, path, target_v, T, DT):
     """
     Reinterpolate the trajectory to get a set N desired target states
     :param state:
@@ -78,7 +78,7 @@ def get_ref_trajectory(state, path, target_v, double T, double DT):
     cdist = np.clip(cdist, cdist[0], cdist[-1])
 
     start_dist = cdist[ind]
-    interp_points = [d * P.DT * target_v + start_dist for d in range(1, K + 1)]
+    interp_points = [d * DT * target_v + start_dist for d in range(1, K + 1)]
     xref[0, :] = np.interp(interp_points, cdist, path[0, :])
     xref[1, :] = np.interp(interp_points, cdist, path[1, :])
     xref[2, :] = target_v

mpc_pybullet_demo/mpc_demo_nosim.py

@@ -112,7 +112,7 @@ class MPCSim:
             # dynamycs w.r.t robot frame
             curr_state = np.array([0, 0, self.state[2], 0])
             # Get Reference_traj -> inputs are in worldframe
-            target = get_ref_trajectory(self.state, self.path, TARGET_VEL)
+            target = get_ref_trajectory(self.state, self.path, TARGET_VEL, T, DT)
 
             x_mpc, u_mpc = self.mpc.step(
                 curr_state,

mpc_pybullet_demo/mpc_demo_pybullet.py

@@ -224,7 +224,7 @@ def run_sim():
     R = [10, 10]  # input cost [acc ,steer]
     P = [10, 10]  # input rate of change cost [acc ,steer]
 
-    mpc = MPC(VehicleModel(), Q, Qf, R, P)
+    mpc = MPC(VehicleModel(), T, DT, Q, Qf, R, P)
     x_history = []
     y_history = []
 
@@ -250,7 +250,7 @@ def run_sim():
 
         # Get Reference_traj
         # NOTE: inputs are in world frame
-        target = get_ref_trajectory(state, path, TARGET_VEL)
+        target = get_ref_trajectory(state, path, TARGET_VEL, T, DT)
 
         # for MPC base link frame is used:
         # so x, y, yaw are 0.0, but speed is the same