diff --git a/docs/showcases.md b/docs/showcases.md
index 60cd66f..be0934b 100644
--- a/docs/showcases.md
+++ b/docs/showcases.md
@@ -12,8 +12,22 @@
     </iframe>
 </div>
 
-## Dynamic Obstacle Avoidance & Kinematic-aware control
+|   Pluto Robot  |  Botanical Garden Osnabrück |
+|:-------------------------:|:-------------------------:|
+| ![Pluto 1](/media/pluto_botanical_garden1.jpg)  |  ![Pluto 2](/media/pluto_botanical_garden2.jpg) |
+
 
+<div style="position: relative; padding-bottom: 56.3%; height: 0; overflow: hidden;" >
+    <iframe src="https://www.youtube.com/embed/qAUWTiqdBM4?si=Blrm6xjGsNOD6xwu?autoplay=1&mute=1" 
+            title="YouTube Video" 
+            frameborder="0" 
+            allow="autoplay; encrypted-media" 
+            allowfullscreen 
+            style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;">
+    </iframe>
+</div>
+
+## Dynamic Obstacle Avoidance & Kinematic-aware control
 
 <div style="position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden;" >
     <iframe src="https://www.youtube.com/embed/4OgxDKcWIXM?si=NyoUc1iDYFN_6Pte?autoplay=1&mute=1" 
diff --git a/docs/theory/index.md b/docs/theory/index.md
index 3494f68..46c48bb 100644
--- a/docs/theory/index.md
+++ b/docs/theory/index.md
@@ -12,13 +12,8 @@ Since a mesh inherently induces a graph structure, it integrates seamlessly with
 In short, meshes combine geometric fidelity with computational efficiency, avoiding the discretization artifacts of raster-based maps while providing a natural substrate for terrain-aware navigation.
 These properties make them especially well-suited for ground vehicle path planning in complex, uneven environments, and form the theoretical foundation of the MeshNav library.
 
-|   Pluto Robot  |  Botanical Garden Osnabrück |
-|:-------------------------:|:-------------------------:|
-| ![Pluto 1](/media/pluto_botanical_garden1.jpg)  |  ![Pluto 2](/media/pluto_botanical_garden2.jpg) |
-
-
 <div style="position: relative; padding-bottom: 56.3%; height: 0; overflow: hidden;" >
-    <iframe src="https://www.youtube.com/embed/qAUWTiqdBM4?si=Blrm6xjGsNOD6xwu?autoplay=1&mute=1" 
+    <iframe src="https://www.youtube.com/embed/aHHiyi8iqv4?si=jYkzOZMmJ0HIHS0V?autoplay=1&mute=1" 
             title="YouTube Video" 
             frameborder="0" 
             allow="autoplay; encrypted-media" 
@@ -26,3 +21,4 @@ These properties make them especially well-suited for ground vehicle path planni
             style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;">
     </iframe>
 </div>
+
diff --git a/docs/tutorials/mesh_cost_layers.md b/docs/tutorials/mesh_cost_layers.md
index 1f9724d..4a7a477 100644
--- a/docs/tutorials/mesh_cost_layers.md
+++ b/docs/tutorials/mesh_cost_layers.md
@@ -69,7 +69,7 @@ It removes points of a LiDAR scan that are close to the map. The remaining point
 Enable this pre-segmentation by calling
 
 ```bash
-ros2 launch mesh_navigation_tutorials mesh_navigation_tutorials_launch.py world_name:=tray obtacle_segmentation:=rmcl_seg
+ros2 launch mesh_navigation_tutorials mesh_navigation_tutorials_launch.py world_name:=tray obstacle_segmentation:=rmcl_seg
 ```
 
 After repeating the steps 1. and 2. from above, the ball is detected as dynamic obstacle, projected onto the obstacle, and finally considered during planning.