Merge branch 'dev-qtgraph-rendering' into dev-model-scale

.github/workflows/ci.yml

@@ -11,16 +11,20 @@ permissions:
 
 jobs:
   build:
-
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
       matrix:
         python-version: ["3.9", "3.10", "3.11", "3.12"]
+    env:
+      QT_QPA_PLATFORM: offscreen
 
     steps:
+    - name: Update apt
+      run: sudo apt update
+
     - name: Install openGL
-      run: sudo apt install freeglut3-dev
+      run: sudo apt install -y freeglut3-dev libglib2.0-0 libsm6 libxrender1 libxext6 libxkbcommon-x11-0 libdbus-1-dev
 
     - uses: actions/checkout@v2
 

docs/customized_usage.rst

@@ -109,7 +109,7 @@ You can change the default paramters (identified on concrete floor with the defa
                    'v_max': 20.0,
                    'width': 0.31,
                    'length': 0.58}
-    env = gym.make('f110_gym:f110-v0', params=params_dict)
+    env = gym.make('f110_gym:f110-v0', config={'params': params_dict})
 
 2. Or you could update the parameters of a specific vehicle in the list of vehicles (or all vehicles):
 

f1tenth_gym/envs/observation.py

@@ -223,11 +223,13 @@ def observe(self):
             lap_count = self.env.lap_counts[i]
 
             x, y, theta = agent.state[xi], agent.state[yi], agent.state[yawi]
-            vx, vy = agent.state[vxi], 0.0
+            vlong = agent.state[vxi]
             delta = agent.state[deltai]
             beta = (
                 0.0 if len(agent.state) < 7 else agent.state[slipi]
             )  # set 0.0 when KST Model
+            vx = vlong * np.cos(beta)
+            vy = vlong * np.sin(beta)
             angvel = (
                 0.0 if len(agent.state) < 7 else agent.state[yaw_ratei]
             )  # set 0.0 when KST Model
@@ -284,5 +286,17 @@ def observation_factory(env, type: str | None, **kwargs) -> Observation:
             "beta",
         ]
         return FeaturesObservation(env, features=features)
+    elif type == "frenet_dynamic_state":
+        features = [
+            "pose_x",
+            "pose_y",
+            "delta",
+            "linear_vel_x",
+            "linear_vel_y",
+            "pose_theta",
+            "ang_vel_z",
+            "beta",
+        ]
+        return FeaturesObservation(env, features=features)
     else:
         raise ValueError(f"Invalid observation type {type}.")

f1tenth_gym/envs/rendering/rendering_pyqt.py

@@ -69,7 +69,7 @@ def __init__(
         self.render_fps = render_fps
 
         # create the canvas
-        self.app = QtWidgets.QApplication([])
+        self.app = QtWidgets.QApplication.instance() or QtWidgets.QApplication([])
         self.window = pg.GraphicsLayoutWidget()
         self.window.setWindowTitle("F1Tenth Gym")
         self.window.setGeometry(0, 0, self.render_spec.window_size, self.render_spec.window_size)
@@ -272,34 +272,33 @@ def render(self) -> Optional[np.ndarray]:
         Optional[np.ndarray]
             if render_mode is "rgb_array", returns the rendered frame as an array
         """
-        if self.draw_flag:
-
-            # draw cars
-            for i in range(len(self.agent_ids)):
-                self.cars[i].render()
+        # draw cars
+        for i in range(len(self.agent_ids)):
+            self.cars[i].render()
 
-            # call callbacks
-            for callback_fn in self.callbacks:
-                callback_fn(self)
+        # call callbacks
+        for callback_fn in self.callbacks:
+            callback_fn(self)
 
-            if self.follow_agent_flag:
-                ego_x, ego_y = self.cars[self.agent_to_follow].pose[:2]
-                self.canvas.setXRange(ego_x - 10, ego_x + 10)
-                self.canvas.setYRange(ego_y - 10, ego_y + 10)
-            else:
-                self.canvas.autoRange()
-                
-            agent_to_follow_id = (
-                self.agent_ids[self.agent_to_follow]
-                if self.agent_to_follow is not None
-                else None
-            )
-            self.bottom_info_renderer.render(
-                text=f"Focus on: {agent_to_follow_id}"
-            )
+        if self.follow_agent_flag:
+            ego_x, ego_y = self.cars[self.agent_to_follow].pose[:2]
+            self.canvas.setXRange(ego_x - 10, ego_x + 10)
+            self.canvas.setYRange(ego_y - 10, ego_y + 10)
+        else:
+            self.canvas.autoRange()
+
+        agent_to_follow_id = (
+            self.agent_ids[self.agent_to_follow]
+            if self.agent_to_follow is not None
+            else None
+        )
+        self.bottom_info_renderer.render(
+            text=f"Focus on: {agent_to_follow_id}"
+        )
 
         if self.render_spec.show_info:
             self.top_info_renderer.render(text=INSTRUCTION_TEXT)
+
         self.time_renderer.render(text=f"{self.sim_time:.2f}")
         self.clock.update()
         self.app.processEvents()
@@ -318,7 +317,7 @@ def render(self) -> Optional[np.ndarray]:
             ptr.setsize(height * width * 4)
             frame = np.array(ptr).reshape(height, width, 4)  #  Copies the data
 
-            return frame
+            return frame[:, :, :3] # remove alpha channel
 
     def render_points(
         self,
@@ -400,3 +399,4 @@ def close(self) -> None:
         Close the rendering environment.
         """
         self.app.exit()
+

f1tenth_gym/envs/track/cubic_spline.py

@@ -182,7 +182,7 @@ def calc_arclength_inaccurate(self, x: float, y: float) -> tuple[float, float]:
             + t * (self.s[min_dist_segment + 1] - self.s[min_dist_segment])
         )
 
-        return s, 0.0
+        return s, ey
 
     def _calc_tangent(self, s: float) -> np.ndarray:
         """

f1tenth_gym/envs/track/track.py

@@ -287,7 +287,7 @@ def cartesian_to_frenet(self, x, y, phi, s_guess=0):
             ey: lateral deviation
             ephi: heading deviation
         """
-        s, ey = self.centerline.spline.calc_arclength(x, y, s_guess)
+        s, ey = self.centerline.spline.calc_arclength_inaccurate(x, y)
         if s > self.centerline.spline.s[-1]:
             # Wrap around
             s = s - self.centerline.spline.s[-1]