add laubloomis exmaple

demo/dryvr_demo/dryvr_agent.py

@@ -1,4 +1,5 @@
 # Example agent.
+from re import L
 from typing import Tuple, List
 
 import numpy as np
@@ -8,6 +9,30 @@
 from verse.map import LaneMap
 from verse.parser import ControllerIR
 
+class LaubLoomisAgent(BaseAgent):
+    def __init__(self, id, code = None, file_name = None):
+        self.decision_logic: ControllerIR = ControllerIR.empty()
+        self.id = id
+        self.init_cont = None 
+        self.init_disc = None
+        self.static_parameters = None 
+        self.uncertain_parameters = None
+
+    def dynamics(self, x, t):
+        x1, x2, x3,\
+        x4, x5, x6,\
+        x7 = x 
+
+        dx1 = 1.4*x3-0.9*x1 
+        dx2 = 2.5*x5-1.5*x2 
+        dx3 = 0.6*x7-0.8*x2*x3 
+        dx4 = 2-1.3*x3*x4 
+        dx5 = 0.7*x1-x4*x5 
+        dx6 = 0.3*x1-3.1*x6 
+        dx7 = 1.8*x6-1.5*x2*x7  
+
+        return [dx1,dx2,dx3,dx4,dx5,dx6,dx7]
+
 class QuadrotorAgent(BaseAgent):
     def __init__(self, id, code = None, file_name = None):
         self.decision_logic: ControllerIR = ControllerIR.empty()
@@ -57,12 +82,3 @@ def dynamics(self, x, t):
         du1 = 0 
 
         return [dx1,dx2,dx3,dx4,dx5,dx6,dx7,dx8,dx9,dx10,dx11,dx12,du1]
-
-    def TC_simulate(self, mode, initialSet, time_horizon, time_step, map=None):
-        y0 = initialSet 
-        t = np.round(np.arange(0.0, time_horizon+time_step/2, time_step), 8)
-        trace = odeint(func = self.dynamics, y0 = y0, t = t)
-        t = t.reshape((-1,1))
-        trace = np.hstack((t, trace))
-        return trace
-

demo/dryvr_demo/laubloomis_demo.py

@@ -0,0 +1,40 @@
+from dryvr_agent import LaubLoomisAgent 
+from verse import Scenario, ScenarioConfig
+from verse.plotter.plotter2D import * 
+
+import plotly.graph_objects as go 
+from enum import Enum, auto 
+
+class AgentMode(Enum):
+    Default = auto()
+
+if __name__ == "__main__":
+    scenario = Scenario(ScenarioConfig(parallel=False))
+    W = 0.1
+
+    agent = LaubLoomisAgent('laub')
+    scenario.add_agent(agent)
+    # The initial position of the quadrotor is uncertain in 
+    # all directions within [−0.4, 0.4] [m] and also the velocity 
+    # is uncertain within [−0.4, 0.4] [m/s] for all directions
+
+    # The inertial (north) position x1, the inertial (east) position x2, 
+    # the altitude x3, the longitudinal velocity x4, 
+    # the lateral velocity x5, the vertical velocity x6, 
+    # the roll angle x7, the pitch angle x8, 
+    # the yaw angle x9, the roll rate x10, 
+    # the pitch rate x11, and the yaw rate x12.
+    scenario.set_init(
+        [
+            [[1.2-W, 1.05-W, 1.5-W, 2.4-W, 1-W, 0.1-W, 0.45-W],
+             [1.2+W, 1.05+W, 1.5+W, 2.4+W, 1+W, 0.1+W, 0.45+W]]
+        ],
+        [
+            (AgentMode.Default, )
+        ]
+    )
+    traces = scenario.verify(20, 0.01)
+
+    fig = go.Figure() 
+    fig = reachtube_tree(traces, None, fig, 0, 4, [1,3], "lines", "trace")
+    fig.show()

verse/agents/base_agent.py

@@ -1,6 +1,6 @@
 from verse.parser.parser import ControllerIR
 import numpy as np
-from scipy.integrate import ode
+from scipy.integrate import ode, odeint
 import copy
 
 
@@ -85,15 +85,9 @@ def TC_simulate(self, mode, initialSet, time_horizon, time_step, map=None):
             map: LaneMap, optional
                 Provided if the map is used
         """
-        num_points = int(np.ceil(time_horizon / time_step))
-        trace = np.zeros((num_points + 1, 1 + len(initialSet)))
-        trace[1:, 0] = [round(i * time_step, 10) for i in range(num_points)]
-        trace[0, 1:] = initialSet
-        for i in range(num_points):
-            result = ode(self.dynamic)
-            result.set_initial_value(initialSet)
-            res: np.ndarray = result.integrate(result.t + time_step)
-            initialSet = res.flatten()
-            trace[i + 1, 0] = time_step * (i + 1)
-            trace[i + 1, 1:] = initialSet
-        return np.array(trace)
+        y0 = initialSet 
+        t = np.round(np.arange(0.0, time_horizon+time_step/2, time_step), 8)
+        trace = odeint(func = self.dynamics, y0 = y0, t = t)
+        t = t.reshape((-1,1))
+        trace = np.hstack((t, trace))
+        return trace