Normalizing Each Objective Class's motionCost()

local_pathfinding/objectives.py

@@ -1,5 +1,6 @@
 """Our custom OMPL optimization objectives."""
 
+import itertools
 import math
 from enum import Enum, auto
 
@@ -63,19 +64,71 @@ class Objective(ob.StateCostIntegralObjective):
     - This class inherits from the OMPL class StateCostIntegralObjective:
         https://ompl.kavrakilab.org/classompl_1_1base_1_1StateCostIntegralObjective.html
     - Camelcase is used for functions that override OMPL functions, as that is their convention.
+    - Also computes the maximum motion cost for normalization purposes.
 
     Attributes:
         space_information (StateSpacePtr): Contains all the information about
             the space planning is done in.
+        max_motion_cost (float): The maximum motion cost between any two states in the state space.
     """
 
-    def __init__(self, space_information):
+    def __init__(self, space_information, num_samples: int):
         super().__init__(si=space_information, enableMotionCostInterpolation=True)
         self.space_information = space_information
 
+        states = self.sample_states(num_samples)
+        # initialize to 1 so that motionCost is not normalized when finding the maximum motion cost
+        self.max_motion_cost = 1.0
+        self.max_motion_cost = self.find_maximum_motion_cost(states)
+
     def motionCost(self, s1: ob.SE2StateSpace, s2: ob.SE2StateSpace) -> ob.Cost:
         raise NotImplementedError
 
+    def find_maximum_motion_cost(self, states: list[ob.SE2StateSpace]) -> float:
+        """Finds the maximum motion cost between any two states in `states`.
+
+        Args:
+            states (list[ob.SE2StateSpace]): OMPL states.
+
+        Returns:
+            float: Maximum motion cost.
+        """
+        return max(
+            self.motionCost(s1, s2).value()
+            for s1, s2 in itertools.combinations(iterable=states, r=2)
+        )
+
+    def sample_states(self, num_samples: int) -> list[ob.SE2StateSpace]:
+        """Samples `num_samples` states from the state space.
+
+        Args:
+            num_samples (int): Number of states to sample.
+
+        Returns:
+            list[ob.SE2StateSpace]: OMPL states.
+        """
+        sampler = self.space_information.getStateSpace().allocDefaultStateSampler()
+
+        sampled_states = []
+
+        for _ in range(num_samples):
+            state = self.space_information.getStateSpace().allocState()
+            sampler.sampleUniform(state)
+            sampled_states.append(state)
+
+        return sampled_states
+
+    def normalization(self, cost: float) -> float:
+        """Normalizes cost using max_motion_cost and caps it at 1.
+
+        Args:
+            cost (float): motionCost value from an objective function.
+        Returns:
+            float: normalized cost between 0 to 1.
+        """
+        normalized_cost = cost / self.max_motion_cost
+        return min(normalized_cost, 1.0)
+
 
 class DistanceObjective(Objective):
     """Generates a distance objective function
@@ -94,13 +147,14 @@ def __init__(
         method: DistanceMethod,
         reference=HelperLatLon(latitude=0.0, longitude=0.0),
     ):
-        super().__init__(space_information)
         self.method = method
         if self.method == DistanceMethod.OMPL_PATH_LENGTH:
-            self.ompl_path_objective = ob.PathLengthOptimizationObjective(self.space_information)
+            self.ompl_path_objective = ob.PathLengthOptimizationObjective(space_information)
         elif self.method == DistanceMethod.LATLON:
             self.reference = reference
 
+        super().__init__(space_information, num_samples=100)
+
     def motionCost(self, s1: ob.SE2StateSpace, s2: ob.SE2StateSpace) -> ob.Cost:
         """Generates the distance between two points
 
@@ -109,7 +163,7 @@ def motionCost(self, s1: ob.SE2StateSpace, s2: ob.SE2StateSpace) -> ob.Cost:
             s2 (SE2StateInternal): The ending point of the local goal state
 
         Returns:
-            ob.Cost: The distance between two points object
+            ob.Cost: The normalized distance between two points
 
         Raises:
             ValueError: If the distance method is not supported
@@ -118,17 +172,16 @@ def motionCost(self, s1: ob.SE2StateSpace, s2: ob.SE2StateSpace) -> ob.Cost:
         s2_xy = cs.XY(s2.getX(), s2.getY())
         if self.method == DistanceMethod.EUCLIDEAN:
             distance = DistanceObjective.get_euclidean_path_length_objective(s1_xy, s2_xy)
-            cost = ob.Cost(distance)
         elif self.method == DistanceMethod.LATLON:
             distance = DistanceObjective.get_latlon_path_length_objective(
                 s1_xy, s2_xy, self.reference
             )
-            cost = ob.Cost(distance)
         elif self.method == DistanceMethod.OMPL_PATH_LENGTH:
-            cost = self.ompl_path_objective.motionCost(s1_xy, s2_xy)
+            distance = self.ompl_path_objective.motionCost(s1, s2).value()
         else:
-            ValueError(f"Method {self.method} not supported")
-        return cost
+            raise ValueError(f"Method {self.method} not supported")
+
+        return ob.Cost(self.normalization(distance))
 
     @staticmethod
     def get_euclidean_path_length_objective(s1: cs.XY, s2: cs.XY) -> float:
@@ -177,20 +230,17 @@ class MinimumTurningObjective(Objective):
     """
 
     def __init__(
-        self,
-        space_information,
-        simple_setup,
-        heading_degrees: float,
-        method: MinimumTurningMethod,
+        self, space_information, simple_setup, heading_degrees: float, method: MinimumTurningMethod
     ):
-        super().__init__(space_information)
         self.goal = cs.XY(
             simple_setup.getGoal().getState().getX(), simple_setup.getGoal().getState().getY()
         )
         assert -180 < heading_degrees <= 180
         self.heading = math.radians(heading_degrees)
         self.method = method
 
+        super().__init__(space_information, num_samples=100)
+
     def motionCost(self, s1: ob.SE2StateSpace, s2: ob.SE2StateSpace) -> ob.Cost:
         """Generates the turning cost between s1, s2, heading or the goal position
 
@@ -199,7 +249,7 @@ def motionCost(self, s1: ob.SE2StateSpace, s2: ob.SE2StateSpace) -> ob.Cost:
             s2 (SE2StateInternal): The ending point of the local goal state
 
         Returns:
-            ob.Cost: The minimum turning angle in degrees
+            ob.Cost: The normalized minimum turning angle in degrees
 
         Raises:
             ValueError: If the minimum turning method is not supported
@@ -213,8 +263,9 @@ def motionCost(self, s1: ob.SE2StateSpace, s2: ob.SE2StateSpace) -> ob.Cost:
         elif self.method == MinimumTurningMethod.HEADING_PATH:
             angle = self.heading_path_turn_cost(s1_xy, s2_xy, self.heading)
         else:
-            ValueError(f"Method {self.method} not supported")
-        return ob.Cost(angle)
+            raise ValueError(f"Method {self.method} not supported")
+
+        return ob.Cost(self.normalization(angle))
 
     @staticmethod
     def goal_heading_turn_cost(s1: cs.XY, goal: cs.XY, heading: float) -> float:
@@ -302,10 +353,11 @@ class WindObjective(Objective):
     """
 
     def __init__(self, space_information, wind_direction_degrees: float):
-        super().__init__(space_information)
         assert -180 < wind_direction_degrees <= 180
         self.wind_direction = math.radians(wind_direction_degrees)
 
+        super().__init__(space_information, num_samples=100)
+
     def motionCost(self, s1: ob.SE2StateSpace, s2: ob.SE2StateSpace) -> ob.Cost:
         """Generates the cost associated with the upwind and downwind directions of the boat in
         relation to the wind.
@@ -315,11 +367,13 @@ def motionCost(self, s1: ob.SE2StateSpace, s2: ob.SE2StateSpace) -> ob.Cost:
             s2 (SE2StateInternal): The ending point of the local goal state
 
         Returns:
-            ob.Cost: The cost of going upwind or downwind
+            ob.Cost: The normalized cost of going upwind or downwind
         """
         s1_xy = cs.XY(s1.getX(), s1.getY())
         s2_xy = cs.XY(s2.getX(), s2.getY())
-        return ob.Cost(WindObjective.wind_direction_cost(s1_xy, s2_xy, self.wind_direction))
+        wind_cost = WindObjective.wind_direction_cost(s1_xy, s2_xy, self.wind_direction)
+
+        return ob.Cost(self.normalization(wind_cost))
 
     @staticmethod
     def wind_direction_cost(s1: cs.XY, s2: cs.XY, wind_direction: float) -> float:
@@ -425,7 +479,6 @@ def __init__(
         wind_speed: float,
         method: SpeedObjectiveMethod,
     ):
-        super().__init__(space_information)
         assert -180 < wind_direction <= 180
         self.wind_direction = math.radians(wind_direction)
 
@@ -434,6 +487,7 @@ def __init__(
 
         self.wind_speed = wind_speed
         self.method = method
+        super().__init__(space_information, num_samples=100)
 
     def motionCost(self, s1: ob.SE2StateSpace, s2: ob.SE2StateSpace) -> ob.Cost:
         """Generates the cost associated with the speed of the boat.
@@ -443,18 +497,19 @@ def motionCost(self, s1: ob.SE2StateSpace, s2: ob.SE2StateSpace) -> ob.Cost:
             s2 (SE2StateInternal): The ending point of the local goal state
 
         Returns:
-            ob.Cost: The cost of going upwind or downwind
+            ob.Cost: The normalized cost of going upwind or downwind
         """
 
         s1_xy = cs.XY(s1.getX(), s1.getY())
         s2_xy = cs.XY(s2.getX(), s2.getY())
+        heading_s1_to_s2 = math.atan2(s2_xy.x - s1_xy.x, s2_xy.y - s1_xy.y)
 
         sailbot_speed = self.get_sailbot_speed(
-            self.heading_direction, self.wind_direction, self.wind_speed
+            heading_s1_to_s2, self.wind_direction, self.wind_speed
         )
 
         if sailbot_speed == 0:
-            return ob.Cost(10000)
+            return ob.Cost(1.0)
 
         if self.method == SpeedObjectiveMethod.SAILBOT_TIME:
             distance = DistanceObjective.get_euclidean_path_length_objective(s1_xy, s2_xy)
@@ -468,7 +523,7 @@ def motionCost(self, s1: ob.SE2StateSpace, s2: ob.SE2StateSpace) -> ob.Cost:
             cost = ob.Cost(self.get_continuous_cost(sailbot_speed))
         else:
             ValueError(f"Method {self.method} not supported")
-        return cost
+        return ob.Cost(self.normalization(cost.value()))
 
     @staticmethod
     def get_sailbot_speed(heading: float, wind_direction: float, wind_speed: float) -> float:
@@ -571,28 +626,23 @@ def get_sailing_objective(
     wind_speed: float,
 ) -> ob.OptimizationObjective:
     objective = ob.MultiOptimizationObjective(si=space_information)
-    objective.addObjective(
-        objective=DistanceObjective(space_information, DistanceMethod.LATLON),
-        weight=1.0,
-    )
-    objective.addObjective(
-        objective=MinimumTurningObjective(
-            space_information, simple_setup, heading_degrees, MinimumTurningMethod.GOAL_HEADING
-        ),
-        weight=100.0,
+    objective_1 = DistanceObjective(
+        space_information=space_information, method=DistanceMethod.LATLON
     )
-    objective.addObjective(
-        objective=WindObjective(space_information, wind_direction_degrees), weight=1.0
+    objective_2 = MinimumTurningObjective(
+        space_information, simple_setup, heading_degrees, MinimumTurningMethod.GOAL_HEADING
     )
-    objective.addObjective(
-        objective=SpeedObjective(
-            space_information,
-            heading_degrees,
-            wind_direction_degrees,
-            wind_speed,
-            SpeedObjectiveMethod.SAILBOT_TIME,
-        ),
-        weight=1.0,
+    objective_3 = WindObjective(space_information, wind_direction_degrees)
+    objective_4 = SpeedObjective(
+        space_information=space_information,
+        heading_direction=heading_degrees,
+        wind_direction=wind_direction_degrees,
+        wind_speed=wind_speed,
+        method=SpeedObjectiveMethod.SAILBOT_TIME,
     )
 
+    objective.addObjective(objective=objective_1, weight=0.25)
+    objective.addObjective(objective=objective_2, weight=0.25)
+    objective.addObjective(objective=objective_3, weight=0.25)
+    objective.addObjective(objective=objective_4, weight=0.25)
     return objective