added publisher for analyzed points

dynamic_payload_analysis_core/dynamic_payload_analysis_core/core.py

@@ -69,7 +69,8 @@ def __init__(self, robot_description : Union[str, Path]):
         # compute main trees of the robot model
         self.compute_subtrees()
 
-
+        # array to store all analyzed points
+        self.analyzed_points = np.array([], dtype=object)
 
         # array to store all configurations for the robot model
         self.configurations = np.array([], dtype=object)
@@ -422,11 +423,17 @@ def compute_all_configurations(self, range : int, resolution : int, end_joint_id
 
         # Create an array to store all configurations
         configurations = []
+
+        # restore analyzed points array
+        self.analyzed_points = np.array([], dtype=object)
 
         # Iterate over the range to compute all configurations
         for x in np.arange(-range, range , resolution):
             for y in np.arange(-range, range , resolution):
                 for z in np.arange(-range/2, range , resolution):
+
+                    self.analyzed_points = np.append(self.analyzed_points, {"position" : [x, y, z]})
+
                     target_position = pin.SE3(np.eye(3), np.array([x, y, z]))
                     new_q = self.compute_inverse_kinematics(q, target_position, end_joint_id)
 
@@ -873,6 +880,15 @@ def get_zero_acceleration(self) -> np.ndarray:
         return a0
 
 
+    def get_analyzed_points(self) -> np.ndarray:
+        """
+        Get the analyzed points during the computation of all configurations.
+
+        :return: Array of analyzed points.
+        """
+
+        return self.analyzed_points
+
     def get_random_configuration(self) -> tuple[np.ndarray, np.ndarray]:
         """
         Get a random configuration for configuration and velocity vectors.

dynamic_payload_analysis_ros/dynamic_payload_analysis_ros/rviz_visualization_menu.py

@@ -63,10 +63,13 @@ def __init__(self):
         self.publisher_rviz_torque = self.create_publisher(MarkerArray, '/torque_visualization', 10)
 
         # Pusblisher for point cloud workspace area
-        self.publisher_workspace_area = self.create_publisher(MarkerArray, '/workspace_area', 10)
+        self.publisher_workspace_area = self.create_publisher(MarkerArray, '/workspace_area', qos_profile=rclpy.qos.QoSProfile( durability=rclpy.qos.DurabilityPolicy.TRANSIENT_LOCAL, depth = 1))
 
         # Pusblisher for point cloud of maximum payloads in the workspace area
-        self.publisher_maximum_payloads = self.create_publisher(MarkerArray, '/maximum_payloads', 10)
+        self.publisher_maximum_payloads = self.create_publisher(MarkerArray, '/maximum_payloads', qos_profile=rclpy.qos.QoSProfile( durability=rclpy.qos.DurabilityPolicy.TRANSIENT_LOCAL, depth = 1))
+
+        # Publisher for point cloud of all analyzed points
+        self.publisher_analyzed_points = self.create_publisher(MarkerArray, '/analyzed_points', qos_profile=rclpy.qos.QoSProfile( durability=rclpy.qos.DurabilityPolicy.TRANSIENT_LOCAL, depth = 1))
 
         # subscription to joint states
         self.joint_states_subscription = self.create_subscription(JointState, '/joint_states', self.joint_states_callback, 10)
@@ -103,6 +106,9 @@ def __init__(self):
         # variable to store if there is a selected configuration from the workspace menu to visualize
         self.selected_configuration = None
 
+        # variable to store analyzed points of the workspace area
+        self.marker_analyzed_points = None
+
         # timer to compute the valid workspace area 
         self.timer_workspace_calculation = self.create_timer(1.0, self.workspace_calculation)
         # timer to publish the selected configuration in joint states
@@ -161,6 +167,53 @@ def update_payload_selection(self):
 
             self.links = links  # Update the links to the current ones
 
+
+    def publisher_analyzed_point_markers(self):
+        """
+        Publish the analyzed points in the workspace area.
+        This will publish all the points in the workspace area where the robot can reach with the current configuration.
+        """
+        if self.menu is not None:
+
+            if len(self.robot_handler.get_analyzed_points()) == 0:
+                return
+            else:
+                if self.marker_analyzed_points is None:
+
+                    self.marker_analyzed_points = MarkerArray()
+
+                    # publish the analyzed points
+                    for i, analyzed_point in enumerate(self.robot_handler.get_analyzed_points()):
+                        point = Marker()
+                        point.header.frame_id = self.robot_handler.get_root_name()
+                        point.header.stamp = Time()
+                        point.ns = f"analyzed_points_ns"
+                        point.id = i
+                        point.type = Marker.SPHERE
+                        point.action = Marker.ADD
+                        point.scale.x = 0.01
+                        point.scale.y = 0.01
+                        point.scale.z = 0.01
+                        point.pose.position.x = analyzed_point['position'][0]
+                        point.pose.position.y = analyzed_point['position'][1]
+                        point.pose.position.z = analyzed_point['position'][2]
+                        point.pose.orientation.w = 1.0
+                        point.color.a = 1.0  # Alpha
+                        point.color.r = 1.0  # Red
+                        point.color.g = 1.0  # Green
+                        point.color.b = 1.0  # Blue
+
+                        self.marker_analyzed_points.markers.append(point)
+
+                    self.publisher_analyzed_points.publish(self.marker_analyzed_points)
+
+                else:
+                    # update with current time
+                    for marker in self.marker_analyzed_points.markers:
+                        marker.header.stamp = Time()
+
+                    self.publisher_analyzed_points.publish(self.marker_analyzed_points)
+
     def publish_payload_force(self):
         """
         Publish the gravity force on the frame with id `id_force`.
@@ -227,6 +280,9 @@ def workspace_calculation(self):
 
                 # compute the maximum payloads for the valid configurations
                 self.valid_configurations = self.robot_handler.compute_maximum_payloads(self.valid_configurations)
+
+                # publish the analyzed points in the workspace area
+                self.publisher_analyzed_point_markers()
 
                 # insert the valid configurations in the menu
                 self.menu.insert_dropdown_configuration(self.valid_configurations)