Wildlife mission

NaviGator/mission_control/navigator_launch/config/pcodar.yaml

@@ -26,4 +26,4 @@ intensity_filter_min_intensity: 10
 intensity_filter_max_intensity: 100
 
 # yamllint disable-line rule:line-length
-classifications: ["white_cylinder", "black_cylinder", "red_cylinder", "green_cylinder", "UNKNOWN"]
+classifications: ["white_cylinder", "black_cylinder", "red_cylinder", "green_cylinder", "red_python_buoy", "blue_manatee_buoy", "green_iguana_buoy", "UNKNOWN"]

NaviGator/mission_control/navigator_launch/config/pcodar_vrx.yaml

@@ -28,4 +28,4 @@ intensity_filter_max_intensity: 1
 # yamllint disable-line rule:line-length
 # classifications: ["buoy", "dock", "stc_platform", "red_totem", "green_totem", "blue_totem", "yellow_totem", "black_totem", "surmark46104", "surmark950400", "surmark950410", "UNKNOWN"]
 # yamllint disable-line rule:line-length
-classifications: ["mb_marker_buoy_red", "mb_marker_buoy_green", "mb_marker_buoy_black", "mb_marker_buoy_white", "mb_round_buoy_black", "mb_round_buoy_orange", "stc_platform", "UNKNOWN"]
+classifications: ["mb_marker_buoy_red", "mb_marker_buoy_green", "mb_marker_buoy_black", "mb_marker_buoy_white", "mb_round_buoy_black", "mb_round_buoy_orange", "stc_platform", "red_python_buoy", "blue_manatee_buoy", "green_iguana_buoy", "UNKNOWN"]

NaviGator/mission_control/navigator_launch/config/poi.yaml

@@ -1,5 +1,6 @@
 ---
 global_frame: enu
 initial_pois:
+  wildlife: [0, 0, 0]
   start_gate: [0, 0, 0]
   entrance_gate: [0, 0, 0]

NaviGator/mission_control/navigator_launch/config/poi_sim.yaml

@@ -1,7 +1,7 @@
 ---
 global_frame: enu
 initial_pois:
-  circle_totems: [26.09821319580078, 59.91523361206055, 0.0]
+  wildlife: [26.09821319580078, 59.91523361206055, 0.0]
   dock: [108.05, -1.07, 0.0]
   entrance_gate: [51.28, -48.69, 0.0]
   obstacle_course: [-32.803, -83.41, 0.0]

NaviGator/mission_control/navigator_missions/navigator_missions/__init__.py

@@ -41,6 +41,9 @@
 from .teleop import Teleop
 from .wildlife import Wildlife
 
+# RobotX 2024 Missions
+from .wildlife_2024 import Wildlife2024
+
 # Currently breaks mission server, TODO: fix or delete
 # from .track_target import TrackTarget
 

NaviGator/mission_control/navigator_missions/navigator_missions/wildlife.py

@@ -34,4 +34,5 @@ async def run(self, parameters):
 
             print("going to nearest small object")
 
+        print(f"Spiraling the point: {t1}")
         await self.move.d_spiral_point(t1, 5, 4, 1, "ccw", theta_offset=-1.57)

NaviGator/mission_control/navigator_missions/navigator_missions/wildlife_2024.py

@@ -0,0 +1,328 @@
+#!/usr/bin/env python3
+import asyncio
+from enum import Enum
+
+import numpy as np
+from mil_msgs.msg import ObjectsInImage
+from mil_msgs.srv import CameraToLidarTransform
+from mil_tools import rosmsg_to_numpy
+from navigator_msgs.srv import MessageWildlifeEncounter, MessageWildlifeEncounterRequest
+
+from .navigator import NaviGatorMission
+
+
+class MoveState(Enum):
+    NOT_STARTED = 1
+    RUNNING = 2
+    CANCELLED = 3
+    FINISHED = 4
+
+
+class Wildlife2024(NaviGatorMission):
+    animals_observed = {
+        "blue_manatee_buoy": False,  # Manatee => Counter clockwise
+        "green_iguana_buoy": False,  # Iguana => Clockwise (by choice)
+        "red_python_buoy": False,  # Python => Clockwise
+    }
+
+    @classmethod
+    async def setup(cls):
+        cls.camsub = cls.nh.subscribe("/bbox_pub", ObjectsInImage)
+        await cls.camsub.setup()
+
+        cls.camera_lidar_tf = cls.nh.get_service_client(
+            "/wamv/sensors/camera/front_right_cam/image_raw",
+            CameraToLidarTransform,
+        )
+
+    @classmethod
+    async def shutdown(cls):
+        await cls.camsub.shutdown()
+
+    def get_indices_of_type(self, objects, classifications):
+        if isinstance(classifications, str):
+            classifications = [classifications]
+        return [
+            i
+            for i, o in enumerate(objects)
+            if o.labeled_classification in classifications
+            and o.id not in self.objects_passed
+        ]
+
+    async def inspect_object(self, position):
+        # Go in front of the object, looking directly at it
+        try:
+            await self.move.look_at(position).set_position(position).backward(6.0).go()
+            await self.nh.sleep(5.0)
+        except asyncio.CancelledError:
+            print("Cancelled Inspection")
+
+    @staticmethod
+    def object_classified(objects, obj_id):
+        """
+        @objects list of object messages
+        @obj_id id of object
+        @return True of object with obj_id is classified
+        """
+        for i, obj in enumerate(objects):
+            if obj.id == obj_id and obj.labeled_classification != "UNKNOWN":
+                return i
+        return -1
+
+    def movement_finished(self, task):
+        if not task.cancelled():
+            print("THE MOVEMENT HAS FINISHED")
+            self.current_move_task_state = MoveState.FINISHED
+
+    # Explore until we find one of the animals we have not seen before
+    async def explore_closest_until(self, is_done, filter_and_sort) -> dict:
+        """
+        @condition func taking in sorted objects, positions
+        @object_filter func filters and sorts
+        """
+        move_id_tuple = None
+        service_req = None
+        investigated = set()
+        move_task = None
+
+        while True:
+            if move_id_tuple is not None:
+                service_req = self.database_query(name="all")
+
+                result = await service_req
+                if move_task is None:
+                    self.current_move_task_state = MoveState.RUNNING
+                    move_task = asyncio.create_task(move_id_tuple[0])
+                    move_task.add_done_callback(self.movement_finished)
+
+                # Database query succeeded
+                if self.current_move_task_state != MoveState.FINISHED:
+                    service_req = None
+                    objects_msg = result
+                    classification_index = self.object_classified(
+                        objects_msg.objects,
+                        move_id_tuple[1],
+                    )
+
+                    if classification_index != -1:
+                        self.send_feedback(
+                            f"{move_id_tuple[1]} identified. Canceling investigation",
+                        )
+                        move_task.cancel()
+
+                        move_task = None
+                        self.last_move_task_state = MoveState.CANCELLED
+                        self.current_move_task_state = MoveState.NOT_STARTED
+                        # move_id_tuple[0].cancel()
+
+                        await self.nh.sleep(1.0)
+
+                        self.send_feedback(f"Investigated {move_id_tuple[1]}")
+                        move_id_tuple = None
+
+                # Move succeeded:
+                else:
+                    self.send_feedback(f"Investigated {move_id_tuple[1]}")
+                    move_id_tuple = None
+                    self.last_move_task_state = MoveState.FINISHED
+                    self.current_move_task_state = MoveState.NOT_STARTED
+                    move_task = None
+            else:
+                print("\nAwaiting for data base\n")
+                objects_msg = await self.database_query(name="all")
+
+            objects = objects_msg.objects
+            positions = np.array(
+                [rosmsg_to_numpy(obj.pose.position) for obj in objects],
+            )
+            indices = [] if len(objects) == 0 else filter_and_sort(objects, positions)
+            if indices is None or len(indices) == 0:
+                self.send_feedback("No objects")
+                continue
+            objects = [objects[i] for i in indices]
+            positions = positions[indices]
+
+            # Exit if done
+            ret = is_done(objects, positions)
+            labels = [obj[0].labeled_classification for obj in ret]
+            self.send_feedback(f"Analyzing objects: {labels}")
+            if ret is not None:
+                if move_id_tuple is not None:
+                    self.send_feedback("Condition met. Canceling investigation")
+                    move_task.cancel()
+                    self.last_move_task_state = MoveState.NOT_STARTED
+                    self.current_move_task_state = MoveState.NOT_STARTED
+                    move_id_tuple = None
+                return ret
+
+            if move_id_tuple is not None:
+                continue
+
+            self.send_feedback(f"ALREADY INVEST {investigated}")
+
+            #### The following is the logic for how we decide what buoy to investigate next ####
+            potential_candidate = None
+            shortest_distance = 1000
+
+            for i in range(len(objects)):
+                if objects[i].id not in investigated:
+                    distance = np.linalg.norm(positions[i] - self.pose[0])
+                    if distance < shortest_distance:
+                        shortest_distance = distance
+                        print(shortest_distance)
+                        print(positions[i])
+                        print(
+                            "POTENTIAL CANDIDATE: IDENTIFIED BY FINDING CLOSEST CONE TO INIT BOAT POS",
+                            distance,
+                        )
+                        potential_candidate = i
+                        print(positions[i])
+
+            if potential_candidate is not None:
+                # if there exists a closest buoy, go to it
+                self.send_feedback(f"Investigating {objects[potential_candidate].id}")
+                investigated.add(objects[potential_candidate].id)
+                move = self.inspect_object(positions[potential_candidate])
+                move_id_tuple = (move, objects[potential_candidate].id)
+                print("USING POTENTIAL CANDIDATE")
+
+    async def circle_animal(self, animal):
+        object = animal[0]
+        position = animal[1]
+        label = object.labeled_classification
+        self.send_feedback(f"Circling {label}...")
+
+        # Go to point and Circle animal
+        await self.move.d_spiral_point(
+            position,
+            6,
+            4,
+            1,
+            (
+                "ccw"
+                if label == "green_iguana_buoy" or label == "red_python_buoy"
+                else "cw"
+            ),
+            theta_offset=(
+                -1.57
+                if label == "green_iguana_buoy" or label == "red_python_buoy"
+                else 1.57
+            ),
+        )
+
+    def get_indices_of_most_confident_animals(
+        self,
+        objects,
+        classifications=["red_python_buoy", "blue_manatee_buoy", "green_iguana_buoy"],
+    ) -> dict:
+        """Pass in sorted list of objects by distance from boat and extract the first instance of classifications"""
+        animals_dict = {classif: -1 for classif in classifications}
+        for i, obj in enumerate(objects):
+            if (
+                obj.labeled_classification in classifications
+                and animals_dict[obj.labeled_classification] == -1
+            ):
+                animals_dict[obj.labeled_classification] = i
+        return animals_dict
+
+    async def find_wildlife(self):
+        robot_position = (await self.tx_pose())[0]
+        self.send_feedback("[wildlife] FINDING WILDLIFE")
+
+        def filter_and_sort(objects, positions):
+            distances = np.linalg.norm(positions - robot_position, axis=1)
+            argsort = np.argsort(distances)
+            return argsort
+
+        def is_done(objects, positions):
+            res = self.get_indices_of_most_confident_animals(objects)
+            # It is only done exploring if we detect an animal we have not circled or no objects were found
+            found_new_animals = []
+            for key in res:
+                if self.animals_observed[key] or res[key] == -1:
+                    continue
+                else:
+                    index = res[key]
+                    found_new_animals.append((objects[index], positions[index]))
+
+            if len(found_new_animals) > 0:
+                return found_new_animals
+            else:
+                return None
+
+        # Get the objects found in exploration
+        animals = await self.explore_closest_until(
+            is_done,
+            filter_and_sort,
+        )
+
+        # Go to each object and circle them accordingly
+        for animal in animals:
+            object = animal[0]
+            label = object.labeled_classification
+            # Update explore dict
+            self.animals_observed[label] = True
+            if label == self.chosen_animal:
+                self.send_feedback(
+                    f"[wildlife] starting circle of {self.chosen_animal}",
+                )
+                td_animal_labels = {
+                    "red_python_buoy": "P",
+                    "green_iguana_buoy": "I",
+                    "blue_manatee_buoy": "M",
+                }
+                self.send_feedback("[wildlife] sending feedback")
+                await self.td_feedback(
+                    MessageWildlifeEncounterRequest(
+                        circling_wildlife=td_animal_labels.get(label, "P"),
+                        clockwise=label == "blue_manatee_buoy",
+                        number_of_circles=2 if label == "red_python_buoy" else 1,
+                    ),
+                )
+                self.send_feedback("[wildlife] sent feedback!")
+                await self.circle_animal(animal)
+                if self.chosen_animal == "red_python_buoy":
+                    self.send_feedback("[wildlife] starting double circle of red")
+                    await self.circle_animal(animal)
+
+        # # Check if all wildlife has been circled
+        # IF not all wildlife has been found call this function again
+        count = 0
+        for found in self.animals_observed.values():
+            if not found:
+                break
+            count += 1
+
+        if count < 3:
+            await self.find_wildlife()
+        else:
+            print("ALL WILDLIFE OBSERVED!")
+
+    async def run(self, args):
+        # Check nearest objects
+        self.objects_passed = set()
+        try:
+            self.color_sequence = await self.nh.get_param("color_sequence", str)
+        except TypeError:
+            print("invalid color sequence type")
+            self.color_sequence = None
+        if not self.color_sequence:
+            self.color_sequence = "RGB"
+            self.send_feedback("Invalid color sequence, defaulting to RGB")
+        animals = {
+            "R": "red_python_buoy",
+            "G": "green_iguana_buoy",
+            "B": "blue_manatee_buoy",
+        }
+        self.chosen_animal = animals.get(self.color_sequence[0], "red_python_buoy")
+        self.send_feedback(f"[wildlife] circling {self.chosen_animal}")
+        self.td_feedback = self.nh.get_service_client(
+            "/wildlife_encounter_message",
+            MessageWildlifeEncounter,
+        )
+        await self.change_wrench("autonomous")
+        # Wait a bit for PCDAR to get setup
+        # await self.set_classifier_enabled.wait_for_service()
+        # await self.set_classifier_enabled(SetBoolRequest(data=True))
+        await self.nh.sleep(3.0)
+        await self.find_wildlife()