对齐场地几何与运行模式基础设施

agent/agent.py

@@ -1,10 +1,7 @@
-from dataclasses import Field
 import logging
-from typing import Mapping
 
 import numpy as np
 from utils.math_ops import MathOps
-from world.commons.field import FIFAField, HLAdultField, Soccer7vs7Field
 from world.commons.play_mode import PlayModeEnum, PlayModeGroupEnum
 
 
@@ -19,36 +16,6 @@ class Agent:
     based on the current state of the world and game conditions.
     """
 
-    BEAM_POSES: Mapping[type[Field], Mapping[int, tuple[float, float, float]]] ={
-        FIFAField: {
-            1: (2.1, 0, 0),
-            2: (22.0, 12.0, 0),
-            3: (22.0, 4.0, 0),
-            4: (22.0, -4.0, 0),
-            5: (22.0, -12.0, 0),
-            6: (15.0, 0.0, 0),
-            7: (4.0, 16.0, 0),
-            8: (11.0, 6.0, 0),
-            9: (11.0, -6.0, 0),
-            10: (4.0, -16.0, 0),
-            11: (7.0, 0.0, 0),
-        },
-        HLAdultField: {
-            1: (7.0, 0.0, 0),
-            2: (2.0, -1.5, 0),
-            3: (2.0, 1.5, 0),
-        },
-        Soccer7vs7Field: {
-            1: (2.1, 0, 0),
-            2: (22.0, 12.0, 0),
-            3: (22.0, 4.0, 0),
-            4: (22.0, -4.0, 0),
-            5: (22.0, -12.0, 0),
-            6: (15.0, 0.0, 0),
-            7: (4.0, 16.0, 0)
-        }
-    }
-
     def __init__(self, agent):
         """
         Creates a new DecisionMaker linked to the given agent.
@@ -76,9 +43,10 @@ class Agent:
             PlayModeGroupEnum.ACTIVE_BEAM,
             PlayModeGroupEnum.PASSIVE_BEAM,
         ):
+            beam_pose = self.agent.world.field.get_beam_pose(self.agent.world.number)
             self.agent.server.commit_beam(
-                pos2d=self.BEAM_POSES[type(self.agent.world.field)][self.agent.world.number][:2],
-                rotation=self.BEAM_POSES[type(self.agent.world.field)][self.agent.world.number][2],
+                pos2d=beam_pose[:2],
+                rotation=beam_pose[2],
             )
 
         if self.is_getting_up or self.agent.skills_manager.is_ready(skill_name="GetUp"):
@@ -95,7 +63,12 @@ class Agent:
 
     def carry_ball(self):
         """
-        Basic example of a behavior: moves the robot toward the goal while handling the ball.
+        Minimal catch-ball behavior.
+
+        All players share the same logic:
+        1. approach a point behind the ball
+        2. reposition with a lateral offset if they are close but not yet behind it
+        3. push the ball forward once they are aligned
         """
         their_goal_pos = self.agent.world.field.get_their_goal_position()[:2]
         ball_pos = self.agent.world.ball_pos[:2]
@@ -103,12 +76,23 @@ class Agent:
 
         ball_to_goal = their_goal_pos - ball_pos
         bg_norm = np.linalg.norm(ball_to_goal)
-        if bg_norm == 0:
-            return
-        ball_to_goal_dir = ball_to_goal / bg_norm
+        if bg_norm <= 1e-6:
+            ball_to_goal_dir = np.array([1.0, 0.0])
+        else:
+            ball_to_goal_dir = ball_to_goal / bg_norm
 
-        dist_from_ball_to_start_carrying = 0.30
-        carry_ball_pos = ball_pos - ball_to_goal_dir * dist_from_ball_to_start_carrying
+        lateral_dir = np.array([-ball_to_goal_dir[1], ball_to_goal_dir[0]])
+
+        back_offset = 0.40
+        side_offset = 0.35
+        push_distance = 0.80
+        approach_distance = 0.90
+        push_start_distance = 0.55
+        behind_margin = 0.08
+        angle_tolerance = np.deg2rad(20.0)
+
+        behind_point = ball_pos - ball_to_goal_dir * back_offset
+        push_target = ball_pos + ball_to_goal_dir * push_distance
 
         my_to_ball = ball_pos - my_pos
         my_to_ball_norm = np.linalg.norm(my_to_ball)
@@ -120,25 +104,36 @@ class Agent:
         cosang = np.dot(my_to_ball_dir, ball_to_goal_dir)
         cosang = np.clip(cosang, -1.0, 1.0)
         angle_diff = np.arccos(cosang)
+        aligned = (my_to_ball_norm > 1e-6) and (angle_diff <= angle_tolerance)
 
-        ANGLE_TOL = np.deg2rad(7.5)
-        aligned = (my_to_ball_norm > 1e-6) and (angle_diff <= ANGLE_TOL)
-
-        behind_ball = np.dot(my_pos - ball_pos, ball_to_goal_dir) < 0
+        behind_ball = np.dot(my_pos - ball_pos, ball_to_goal_dir) < -behind_margin
         desired_orientation = MathOps.vector_angle(ball_to_goal)
 
-        if not aligned or not behind_ball:
-            self.agent.skills_manager.execute(
-                "Walk",
-                target_2d=carry_ball_pos,
-                is_target_absolute=True,
-                orientation=None if np.linalg.norm(my_pos - carry_ball_pos) > 2 else desired_orientation
-            )
-        else:
-            self.agent.skills_manager.execute(
-                "Walk",
-                target_2d=their_goal_pos,
-                is_target_absolute=True,
-                orientation=desired_orientation
-            )
+        lateral_sign = np.sign(np.cross(ball_to_goal_dir, my_to_ball_dir))
+        if lateral_sign == 0:
+            lateral_sign = 1.0 if (my_pos[1] - ball_pos[1]) >= 0 else -1.0
 
+        reposition_point = behind_point + lateral_dir * lateral_sign * side_offset
+
+        if my_to_ball_norm > approach_distance:
+            target_2d = behind_point
+            orientation = None
+        elif not behind_ball:
+            target_2d = reposition_point
+            orientation = None if np.linalg.norm(my_pos - reposition_point) > 0.8 else desired_orientation
+        elif not aligned and my_to_ball_norm > push_start_distance:
+            target_2d = behind_point
+            orientation = desired_orientation
+        else:
+            target_2d = push_target
+            orientation = desired_orientation
+
+        if np.linalg.norm(target_2d - my_pos) <= 1e-4:
+            target_2d = my_pos + ball_to_goal_dir * 0.30
+
+        self.agent.skills_manager.execute(
+            "Walk",
+            target_2d=target_2d,
+            is_target_absolute=True,
+            orientation=orientation,
+        )

agent/base_agent.py

@@ -5,6 +5,7 @@ from world.robot import T1, Robot
 from behaviors.behavior_manager import BehaviorManager
 from world.world import World
 from communication.server import Server
+from communication.monitor_client import MonitorClient
 from communication.world_parser import WorldParser
 
 logger = logging.getLogger(__file__)
@@ -35,6 +36,7 @@ class Base_Agent:
         self.server: Server = Server(
             host=host, port=port, world_parser=self.world_parser
         )
+        self.monitor: MonitorClient = MonitorClient(host=host, port=port + 1)
         self.robot: Robot = T1(agent=self)
         self.skills_manager: BehaviorManager = BehaviorManager(agent=self)
         self.decision_maker: Agent = Agent(agent=self)
@@ -53,6 +55,7 @@ class Base_Agent:
              - Sends the next set of commands to the server.
         """
         self.server.connect()
+        self.monitor.connect()
 
         self.server.send_immediate(
             f"(init {self.robot.name} {self.world.team_name} {self.world.number})"
@@ -78,4 +81,5 @@ class Base_Agent:
         Logs a shutdown message and closes the server connection.
         """
         logger.info("Shutting down.")
-        self.server.shutdown()
+        self.monitor.close()
+        self.server.shutdown()

communication/monitor_client.py

@@ -0,0 +1,63 @@
+import logging
+import socket
+
+
+logger = logging.getLogger(__name__)
+
+
+class MonitorClient:
+    """
+    TCP client for the RCSSServerMJ monitor port.
+
+    Sends monitor commands via the length-prefixed S-expression protocol.
+    """
+
+    def __init__(self, host: str = "localhost", port: int = 60001):
+        self._host = host
+        self._port = port
+        self._socket: socket.socket | None = None
+
+    def connect(self) -> None:
+        self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        self._socket.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
+        self._socket.connect((self._host, self._port))
+        logger.info("Monitor connection established to %s:%d.", self._host, self._port)
+
+    def close(self) -> None:
+        if self._socket is not None:
+            self._socket.close()
+            self._socket = None
+
+    def send(self, msg: str) -> None:
+        data = msg.encode()
+        self._socket.send(len(data).to_bytes(4, byteorder="big") + data)
+
+    def place_ball(
+        self,
+        pos: tuple[float, float, float],
+        vel: tuple[float, float, float] | None = None,
+    ) -> None:
+        msg = f"(ball (pos {pos[0]} {pos[1]} {pos[2]})"
+        if vel is not None:
+            msg += f" (vel {vel[0]} {vel[1]} {vel[2]})"
+        msg += ")"
+        self.send(msg)
+
+    def drop_ball(self) -> None:
+        self.send("(dropBall)")
+
+    def kick_off(self, side: str = "Left") -> None:
+        self.send(f"(kickOff {side})")
+
+    def set_play_mode(self, mode: str) -> None:
+        self.send(f"(playMode {mode})")
+
+    def place_player(
+        self,
+        unum: int,
+        team_name: str,
+        pos: tuple[float, float, float],
+    ) -> None:
+        self.send(
+            f"(agent (unum {unum}) (team {team_name}) (pos {pos[0]} {pos[1]} {pos[2]}))"
+        )

readme.md

@@ -41,7 +41,14 @@ CLI parameter (a usage help is also available):
 - `--port <port>` to specify the agent port (default: 60000)
 - `-n <number>` Player number (1–11) (default: 1)
 - `-t <team_name>` Team name (default: 'Default')
+- `-f <field>` Field profile (default: `fifa`)
 
+### Field profiles
+
+There are two supported ways to run Apollo3D:
+
+- Official rules test: use the server with `--rules ssim`, and run agents with `-f fifa`. This matches the current `rcssservermj` default field for the SSIM rule book.
+- Apollo custom 7v7: run agents with `-f sim3d_7vs7`. This profile is kept for Apollo's custom small-field setup and should not be treated as the official SSIM geometry baseline.
 
 ### Run a team
 You can also use a shell script to start the entire team, optionally specifying host and port:
@@ -55,6 +62,8 @@ Using **Poetry**:
 poetry run ./start_7v7.sh [host] [port]
 ```
 
+`start_7v7.sh` now launches agents explicitly with `-f sim3d_7vs7`.
+
 CLI parameter:
 
 - `[host]` Server IP address (default: 'localhost')
@@ -85,4 +94,4 @@ This project was developed and contributed by:
 - **Pedro Rabelo**
 - **Melissa Damasceno**
 
-Contributions, bug reports, and feature requests are welcome via pull requests.
+Contributions, bug reports, and feature requests are welcome via pull requests.

run_player.py

@@ -16,7 +16,7 @@ parser.add_argument("-t", "--team", type=str, default="Default", help="Team name
 parser.add_argument("-n", "--number", type=int, default=1, help="Player number")
 parser.add_argument("--host", type=str, default="127.0.0.1", help="Server host")
 parser.add_argument("--port", type=int, default=60000, help="Server port")
-parser.add_argument("-f", "--field", type=str, default='sim3d_7vs7', help="Field to be played")
+parser.add_argument("-f", "--field", type=str, default='fifa', help="Field to be played")
 
 args = parser.parse_args()
 

start_7v7.sh

@@ -5,5 +5,5 @@ host=${1:-localhost}
 port=${2:-60000}
 
 for i in {1..7}; do
-  python3 run_player.py --host $host --port $port -n $i -t SE &
+  python3 run_player.py --host $host --port $port -n $i -t SE -f sim3d_7vs7 &
 done

utils/math_ops.py

@@ -51,6 +51,7 @@ class MathOps():
         if size == 0: return vec
         return vec / size
 
+    @staticmethod
     def rel_to_global_3d(local_pos_3d: np.ndarray, global_pos_3d: np.ndarray,
                          global_orientation_quat: np.ndarray) -> np.ndarray:
         ''' Converts a local 3d position to a global 3d position given the global position and orientation (quaternion) '''

world/commons/field.py

@@ -1,62 +1,229 @@
-from abc import ABC, abstractmethod
-from typing_extensions import override
+from __future__ import annotations
+
+from abc import ABC
+from typing import Literal
+
+import numpy as np
+
 from world.commons.field_landmarks import FieldLandmarks
 
+GoalSide = Literal["our", "their", "left", "right"]
+Bounds2D = tuple[float, float, float, float]
+
 
 class Field(ABC):
+    FIELD_DIM: tuple[float, float, float]
+    LINE_WIDTH: float
+    GOAL_DIM: tuple[float, float, float]
+    GOALIE_AREA_DIM: tuple[float, float]
+    PENALTY_AREA_DIM: tuple[float, float] | None
+    PENALTY_SPOT_DISTANCE: float
+    CENTER_CIRCLE_RADIUS: float
+    DEFAULT_BEAM_POSES: dict[int, tuple[float, float, float]]
+
     def __init__(self, world):
         from world.world import World  # type hinting
+
         self.world: World = world
-        self.field_landmarks: FieldLandmarks = FieldLandmarks(world=self.world)
+        self.field_landmarks: FieldLandmarks = FieldLandmarks(field=self)
 
-    def get_our_goal_position(self):
-        return (-self.get_length() / 2, 0)
+    def _resolve_side(self, side: GoalSide) -> Literal["left", "right"]:
+        if side in ("our", "left"):
+            return "left"
+        if side in ("their", "right"):
+            return "right"
+        raise ValueError(f"Unknown field side: {side}")
 
-    def get_their_goal_position(self):
-        return (self.get_length() / 2, 0)
+    def get_width(self) -> float:
+        return self.FIELD_DIM[1]
 
-    @abstractmethod
-    def get_width(self):
-        raise NotImplementedError()
+    def get_length(self) -> float:
+        return self.FIELD_DIM[0]
 
-    @abstractmethod
-    def get_length(self):
-        raise NotImplementedError()
+    def get_goal_dim(self) -> tuple[float, float, float]:
+        return self.GOAL_DIM
+
+    def get_goal_half_width(self) -> float:
+        return self.GOAL_DIM[1] / 2.0
+
+    def get_center_circle_radius(self) -> float:
+        return self.CENTER_CIRCLE_RADIUS
+
+    def get_goal_position(self, side: GoalSide = "our") -> tuple[float, float]:
+        resolved_side = self._resolve_side(side)
+        x = -self.get_length() / 2.0 if resolved_side == "left" else self.get_length() / 2.0
+        return (x, 0.0)
+
+    def get_our_goal_position(self) -> tuple[float, float]:
+        return self.get_goal_position("our")
+
+    def get_their_goal_position(self) -> tuple[float, float]:
+        return self.get_goal_position("their")
+
+    def _build_box_bounds(self, depth: float, width: float, side: GoalSide) -> Bounds2D:
+        resolved_side = self._resolve_side(side)
+        field_half_x = self.get_length() / 2.0
+        half_width = width / 2.0
+
+        if resolved_side == "left":
+            return (-field_half_x, -field_half_x + depth, -half_width, half_width)
+        return (field_half_x - depth, field_half_x, -half_width, half_width)
+
+    def get_goalie_area_bounds(self, side: GoalSide = "our") -> Bounds2D:
+        return self._build_box_bounds(
+            depth=self.GOALIE_AREA_DIM[0],
+            width=self.GOALIE_AREA_DIM[1],
+            side=side,
+        )
+
+    def get_penalty_area_bounds(self, side: GoalSide = "our") -> Bounds2D:
+        if self.PENALTY_AREA_DIM is None:
+            raise ValueError(f"{type(self).__name__} does not define a penalty area")
+
+        return self._build_box_bounds(
+            depth=self.PENALTY_AREA_DIM[0],
+            width=self.PENALTY_AREA_DIM[1],
+            side=side,
+        )
+
+    def get_penalty_spot(self, side: GoalSide = "our") -> tuple[float, float]:
+        resolved_side = self._resolve_side(side)
+        x = (self.get_length() / 2.0) - self.PENALTY_SPOT_DISTANCE
+        return (-x, 0.0) if resolved_side == "left" else (x, 0.0)
+
+    def _is_inside_bounds(self, pos2d: np.ndarray | tuple[float, float], bounds: Bounds2D) -> bool:
+        x, y = float(pos2d[0]), float(pos2d[1])
+        min_x, max_x, min_y, max_y = bounds
+        return min_x <= x <= max_x and min_y <= y <= max_y
+
+    def is_inside_goalie_area(
+        self, pos2d: np.ndarray | tuple[float, float], side: GoalSide = "our"
+    ) -> bool:
+        return self._is_inside_bounds(pos2d, self.get_goalie_area_bounds(side))
+
+    def is_inside_penalty_area(
+        self, pos2d: np.ndarray | tuple[float, float], side: GoalSide = "our"
+    ) -> bool:
+        return self._is_inside_bounds(pos2d, self.get_penalty_area_bounds(side))
+
+    def is_inside_field(self, pos2d: np.ndarray | tuple[float, float]) -> bool:
+        field_half_x = self.get_length() / 2.0
+        field_half_y = self.get_width() / 2.0
+        return self._is_inside_bounds(pos2d, (-field_half_x, field_half_x, -field_half_y, field_half_y))
+
+    def get_beam_pose(self, number: int) -> tuple[float, float, float]:
+        try:
+            return self.DEFAULT_BEAM_POSES[number]
+        except KeyError as exc:
+            raise KeyError(f"No beam pose configured for player {number} on {type(self).__name__}") from exc
+
+    def get_default_beam_poses(self) -> dict[int, tuple[float, float, float]]:
+        return dict(self.DEFAULT_BEAM_POSES)
+
+    def get_canonical_landmarks(self) -> dict[str, np.ndarray]:
+        field_half_x = self.get_length() / 2.0
+        field_half_y = self.get_width() / 2.0
+        goal_half_y = self.get_goal_half_width()
+        penalty_marker_x = field_half_x - self.PENALTY_SPOT_DISTANCE
+        goalie_area_x = field_half_x - self.GOALIE_AREA_DIM[0]
+        goalie_marker_y = self.GOALIE_AREA_DIM[1] / 2.0
+
+        landmarks = {
+            "l_luf": np.array([-field_half_x, field_half_y, 0.0]),
+            "l_llf": np.array([-field_half_x, -field_half_y, 0.0]),
+            "l_ruf": np.array([field_half_x, field_half_y, 0.0]),
+            "l_rlf": np.array([field_half_x, -field_half_y, 0.0]),
+            "t_cuf": np.array([0.0, field_half_y, 0.0]),
+            "t_clf": np.array([0.0, -field_half_y, 0.0]),
+            "x_cuc": np.array([0.0, self.CENTER_CIRCLE_RADIUS, 0.0]),
+            "x_clc": np.array([0.0, -self.CENTER_CIRCLE_RADIUS, 0.0]),
+            "p_lpm": np.array([-penalty_marker_x, 0.0, 0.0]),
+            "p_rpm": np.array([penalty_marker_x, 0.0, 0.0]),
+            "g_lup": np.array([-field_half_x, goal_half_y, self.GOAL_DIM[2]]),
+            "g_llp": np.array([-field_half_x, -goal_half_y, self.GOAL_DIM[2]]),
+            "g_rup": np.array([field_half_x, goal_half_y, self.GOAL_DIM[2]]),
+            "g_rlp": np.array([field_half_x, -goal_half_y, self.GOAL_DIM[2]]),
+            "l_luga": np.array([-goalie_area_x, goalie_marker_y, 0.0]),
+            "l_llga": np.array([-goalie_area_x, -goalie_marker_y, 0.0]),
+            "l_ruga": np.array([goalie_area_x, goalie_marker_y, 0.0]),
+            "l_rlga": np.array([goalie_area_x, -goalie_marker_y, 0.0]),
+            "t_luga": np.array([-field_half_x, goalie_marker_y, 0.0]),
+            "t_llga": np.array([-field_half_x, -goalie_marker_y, 0.0]),
+            "t_ruga": np.array([field_half_x, goalie_marker_y, 0.0]),
+            "t_rlga": np.array([field_half_x, -goalie_marker_y, 0.0]),
+        }
+
+        if self.PENALTY_AREA_DIM is not None:
+            penalty_area_x = field_half_x - self.PENALTY_AREA_DIM[0]
+            penalty_marker_y = self.PENALTY_AREA_DIM[1] / 2.0
+            landmarks.update(
+                {
+                    "l_lupa": np.array([-penalty_area_x, penalty_marker_y, 0.0]),
+                    "l_llpa": np.array([-penalty_area_x, -penalty_marker_y, 0.0]),
+                    "l_rupa": np.array([penalty_area_x, penalty_marker_y, 0.0]),
+                    "l_rlpa": np.array([penalty_area_x, -penalty_marker_y, 0.0]),
+                    "t_lupa": np.array([-field_half_x, penalty_marker_y, 0.0]),
+                    "t_llpa": np.array([-field_half_x, -penalty_marker_y, 0.0]),
+                    "t_rupa": np.array([field_half_x, penalty_marker_y, 0.0]),
+                    "t_rlpa": np.array([field_half_x, -penalty_marker_y, 0.0]),
+                }
+            )
+
+        return landmarks
 
 
 class FIFAField(Field):
-    def __init__(self, world):
-        super().__init__(world)
-
-    @override
-    def get_width(self):
-        return 68
-
-    @override
-    def get_length(self):
-        return 105
+    FIELD_DIM = (105.0, 68.0, 40.0)
+    LINE_WIDTH = 0.1
+    GOAL_DIM = (1.6, 7.32, 2.44)
+    GOALIE_AREA_DIM = (5.5, 18.32)
+    PENALTY_AREA_DIM = (16.5, 40.32)
+    PENALTY_SPOT_DISTANCE = 11.0
+    CENTER_CIRCLE_RADIUS = 9.15
+    DEFAULT_BEAM_POSES = {
+        1: (2.1, 0.0, 0.0),
+        2: (22.0, 12.0, 0.0),
+        3: (22.0, 4.0, 0.0),
+        4: (22.0, -4.0, 0.0),
+        5: (22.0, -12.0, 0.0),
+        6: (15.0, 0.0, 0.0),
+        7: (4.0, 16.0, 0.0),
+        8: (11.0, 6.0, 0.0),
+        9: (11.0, -6.0, 0.0),
+        10: (4.0, -16.0, 0.0),
+        11: (7.0, 0.0, 0.0),
+    }
 
 
 class HLAdultField(Field):
-    def __init__(self, world):
-        super().__init__(world)
+    FIELD_DIM = (14.0, 9.0, 40.0)
+    LINE_WIDTH = 0.05
+    GOAL_DIM = (0.6, 2.6, 1.8)
+    GOALIE_AREA_DIM = (1.0, 4.0)
+    PENALTY_AREA_DIM = (3.0, 6.0)
+    PENALTY_SPOT_DISTANCE = 2.1
+    CENTER_CIRCLE_RADIUS = 1.5
+    DEFAULT_BEAM_POSES = {
+        1: (5.5, 0.0, 0.0),
+        2: (2.0, -1.5, 0.0),
+        3: (2.0, 1.5, 0.0),
+    }
 
-    @override
-    def get_width(self):
-        return 9
-
-    @override
-    def get_length(self):
-        return 14
 
 class Soccer7vs7Field(Field):
-    def __init__(self, world):
-        super().__init__(world)
-
-    @override
-    def get_width(self):
-        return 36
-
-    @override
-    def get_length(self):
-        return 55
+    FIELD_DIM = (55.0, 36.0, 40.0)
+    LINE_WIDTH = 0.1
+    GOAL_DIM = (0.84, 3.9, 2.44)
+    GOALIE_AREA_DIM = (2.9, 9.6)
+    PENALTY_AREA_DIM = (8.6, 21.3)
+    PENALTY_SPOT_DISTANCE = 5.8
+    CENTER_CIRCLE_RADIUS = 4.79
+    DEFAULT_BEAM_POSES = {
+        1: (2.0, 0.0, 0.0),
+        2: (12.0, 8.0, 0.0),
+        3: (13.5, 0.0, 0.0),
+        4: (12.0, -8.0, 0.0),
+        5: (7.0, 9.5, 0.0),
+        6: (4.5, 0.0, 0.0),
+        7: (7.0, -9.5, 0.0),
+    }

world/commons/field_landmarks.py

@@ -1,14 +1,16 @@
+from __future__ import annotations
+
 import numpy as np
+
 from utils.math_ops import MathOps
 
 
 class FieldLandmarks:
-    def __init__(self, world):
-        from world.world import World  # type hinting
-
-        self.world: World = world
-
-        self.landmarks: dict = {}
+    def __init__(self, field):
+        self.field = field
+        self.world = field.world
+        self.landmarks: dict[str, np.ndarray] = {}
+        self.canonical_landmarks: dict[str, np.ndarray] = field.get_canonical_landmarks()
 
     def update_from_perception(self, landmark_id: str, landmark_pos: np.ndarray) -> None:
         """
@@ -21,14 +23,19 @@ class FieldLandmarks:
         global_pos_3d = MathOps.rel_to_global_3d(
             local_pos_3d=local_cart_3d,
             global_pos_3d=world.global_position,
-            global_orientation_quat=world.agent.robot.global_orientation_quat
+            global_orientation_quat=world.agent.robot.global_orientation_quat,
         )
 
         self.landmarks[landmark_id] = global_pos_3d
 
-    def get_landmark_position(self, landmark_id: str) -> np.ndarray | None:
+    def get_landmark_position(
+        self, landmark_id: str, use_canonical: bool = False
+    ) -> np.ndarray | None:
         """
-        Returns the calculated 2d global position for a given landmark ID.
-        Returns None if the landmark is not currently visible or processed.
+        Returns the current perceived or canonical global position for a landmark.
         """
-        return self.global_positions.get(landmark_id)
+        source = self.canonical_landmarks if use_canonical else self.landmarks
+        return source.get(landmark_id)
+
+    def get_canonical_landmark_position(self, landmark_id: str) -> np.ndarray | None:
+        return self.canonical_landmarks.get(landmark_id)