Circular motion

OL
Interpolation
No multiprocessing
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config/walk_parameters.yaml

@@ -6,7 +6,7 @@ robot:
     LOGGING: true  # Enable/disable logging during the experiment
     PLOTTING: true  # Enable/disable automatic plotting at the end of the experiment
     DEMONSTRATION: false  # Enable/disable demonstration functionalities
-    SIMULATION: true  # Enable/disable PyBullet simulation or running on real robot
+    SIMULATION: false  # Enable/disable PyBullet simulation or running on real robot
     enable_pyb_GUI: true  # Enable/disable PyBullet GUI
     envID: 0  # Identifier of the environment to choose in which one the simulation will happen
     use_flat_plane: true  # If True the ground is flat, otherwise it has bumps
@@ -24,7 +24,7 @@ robot:
     dt_wbc: 0.001  # Time step of the whole body control
     dt_mpc: 0.015  # Time step of the model predictive control
     type_MPC: 3  # Which MPC solver you want to use: 0 for OSQP MPC, 1, 2, 3 for Crocoddyl MPCs
-    save_guess: true # true to interpolate the impedance quantities between nodes of the MPC
+    save_guess: false # true to interpolate the impedance quantities between nodes of the MPC
     movement: "circle" # name of the movement to perform
     interpolate_mpc: true # true to interpolate the impedance quantities between nodes of the MPC
     interpolation_type: 3 # 0,1,2,3 decide which kind of interpolation is used

python/quadruped_reactive_walking/Controller.py

@@ -218,32 +218,32 @@ class Controller:
                 self.mpc_solved = False
 
             try:
-                self.mpc.solve(
-                    self.k,
-                    m["x_m"],
-                    self.target_footstep.copy(),
-                    self.gait,
-                    self.xs_init,
-                    self.us_init,
-                )
-                # if self.initialized:
-                #     self.mpc.solve(
-                #         self.k,
-                #         self.mpc_result.xs[1],
-                #         self.target_footstep.copy(),
-                #         self.gait,
-                #         self.xs_init,
-                #         self.us_init,
-                #     )
-                # else:
-                #     self.mpc.solve(
-                #         self.k,
-                #         m["x_m"],
-                #         self.target_footstep.copy(),
-                #         self.gait,
-                #         self.xs_init,
-                #         self.us_init,
-                #     )
+                # self.mpc.solve(
+                #     self.k,
+                #     m["x_m"],
+                #     self.target_footstep.copy(),
+                #     self.gait,
+                #     self.xs_init,
+                #     self.us_init,
+                # )
+                if self.initialized:
+                    self.mpc.solve(
+                        self.k,
+                        self.mpc_result.xs[1],
+                        self.target_footstep.copy(),
+                        self.gait,
+                        self.xs_init,
+                        self.us_init,
+                    )
+                else:
+                    self.mpc.solve(
+                        self.k,
+                        m["x_m"],
+                        self.target_footstep.copy(),
+                        self.gait,
+                        self.xs_init,
+                        self.us_init,
+                    )
             except ValueError:
                 self.error = True
                 print("MPC Problem")