[Feature]: Cleaning up the github repository documentation

doc/03_research/01_acting/Readme.md

@@ -2,5 +2,10 @@
 
 This folder contains all the results of our research on acting:
 
+* **PAF22**
 * [Basics](./01_acting.md)
 * [Implementation](./02_acting_implementation.md)
+* **PAF23**
+* [PAF21_1 Acting](./03_paf21_1_Acting.md)
+* [PAF21_2 Acting & Pylot Control](./04_research_Acting_Pylot_and_PAF21_2.md)
+* [Autoware Control](./05_autoware_acting.md)

doc/03_research/research_PAF_23/Research_Pylot-Planning_PAF21-Perception.md → doc/03_research/02_perception/05_Research_PAF21-Perception.md

@@ -1,32 +1,5 @@
 # Sprint 0: Research Samuel Kühnel
 
-## Pylot
-
-## Planning
-
-- 4 different options
-- **Waypoint Planner**: Auto follows predefined waypoints. It recognizes traffic lights and stops at obstacles, but cannot avoid them
-- **Freenet-Optimal-Trajecotry-Planner**: CPP code with Python wrapper ([GitHub](https://github.com/erdos-project/frenet_optimal_trajectory_planner))
-→ Predefined line that is used for orientation → Can avoid obstacles!
-
-![freenet_gif](https://github.com/erdos-project/frenet_optimal_trajectory_planner/raw/master/img/fot2.gif)
-
-- **RRT\*-Planner**: RRT* algorithm for path planning ([GitHub](https://github.com/erdos-project/rrt_star_planner))
-  - Creates random nodes
-  - Adds nodes to the graph that are not blocked by objects on the road
-  - Generally terminates as soon as a node is found in the target area
-  - RRT*: Searches for the shortest path
-
-![rrt_star_gif](https://github.com/erdos-project/rrt_star_planner/raw/master/img/rrtstar.gif)
-
-- **Hybrid A\* planner**: Hybrid A* algorithm for path planning ([GitHub](https://github.com/erdos-project/hybrid_astar_planner))
-  - Calculates the shortest path between two nodes from a graph
-  - Similar to Dijkstra's algorithm
-  - Nodes are estimated based on their costs and promising nodes are selected first
-  - Hybrid A* algorithm: Not always optimal solution, but in the neighborhood of the optimal solution.
-
-![hybrid_astar_gif](https://github.com/erdos-project/hybrid_astar_planner/raw/master/img/straight_obstacle.gif)
-
 ## PAF 21-2
 
 ### Perception

doc/03_research/02_perception/Readme.md

@@ -6,4 +6,5 @@ This folder contains all the results of research on perception:
   * [Basics](./02_basics.md)
   * [First implementation plan](./03_first_implementation_plan.md)
 * **PAF23**
-  * [Pylot](./04_pylot.md)
+  * [Pylot Perception](./04_pylot.md)
+  * [PAF_21_1 Perception](./05_Research_PAF21-Perception.md)

doc/03_research/03_planning/09_Research_Pylot_Planning.md

@@ -0,0 +1,28 @@
+# Sprint 0: Research Samuel Kühnel
+
+## Pylot
+
+## Planning
+
+- 4 different options
+- **Waypoint Planner**: Auto follows predefined waypoints. It recognizes traffic lights and stops at obstacles, but cannot avoid them
+- **Freenet-Optimal-Trajecotry-Planner**: CPP code with Python wrapper ([GitHub](https://github.com/erdos-project/frenet_optimal_trajectory_planner))
+→ Predefined line that is used for orientation → Can avoid obstacles!
+
+![freenet_gif](https://github.com/erdos-project/frenet_optimal_trajectory_planner/raw/master/img/fot2.gif)
+
+- **RRT\*-Planner**: RRT* algorithm for path planning ([GitHub](https://github.com/erdos-project/rrt_star_planner))
+  - Creates random nodes
+  - Adds nodes to the graph that are not blocked by objects on the road
+  - Generally terminates as soon as a node is found in the target area
+  - RRT*: Searches for the shortest path
+
+![rrt_star_gif](https://github.com/erdos-project/rrt_star_planner/raw/master/img/rrtstar.gif)
+
+- **Hybrid A\* planner**: Hybrid A* algorithm for path planning ([GitHub](https://github.com/erdos-project/hybrid_astar_planner))
+  - Calculates the shortest path between two nodes from a graph
+  - Similar to Dijkstra's algorithm
+  - Nodes are estimated based on their costs and promising nodes are selected first
+  - Hybrid A* algorithm: Not always optimal solution, but in the neighborhood of the optimal solution.
+
+![hybrid_astar_gif](https://github.com/erdos-project/hybrid_astar_planner/raw/master/img/straight_obstacle.gif)

doc/03_research/03_planning/Readme.md

@@ -9,3 +9,4 @@ This folder contains all the results of research on planning:
   * [State machine design](./06_state_machine_design.md)
 * **PAF23**
   * [PAF21-1 Planning](./08_paf21-1.md)
+  * [Pylot Planning](./09_Research_Pylot_Planning.md)

doc/08_dev_talks/dev_talks_paf23/sprint_1.md

@@ -0,0 +1,37 @@
+# Sprint 1
+
+- [ ]  Namen auf Präsentationen packen
+- [ ]  cleaning up github repo {Robert, Leon}
+  - [ ]  Research issues PR / merge
+- [ ]  5- 25 h work for submission fix (Work for 2) {Samuel, Maxi}
+  - [ ]  constructing & updating docker image (archive into docker image)
+  - [ ]  update of branches
+- [ ]  Analyzation of projects AIM State and IS State (every group)
+- [ ]  Start working on code
+- [ ]  detailed understanding of components
+
+    Perception: {Leon, Maxi}
+
+  - [ ]  removing panopticsegmentation (if non breaking)
+    - [ ]  what does happen when its removed?
+  - [ ]  (→ replacing it with object detection by Pylot [issue for backlog])
+
+    Planning: {Julius, Samuel, Alex}
+
+  - [ ]  creating visual architecture
+  - [ ]  understanding of components
+  - [ ]  understanding of current Decision Tree
+    - [ ]  does the global routing by the Decision Tree work?
+    - [ ]  lane decisions correct? (holding lane, switching lanes)
+    - [ ]  how well does it perform
+    - [ ]  limitations?
+  - [ ]  (Decision Tree, (vs. ) State Machine [issue for backlog])
+    - [ ]  comparing to performance of State Machine by Driving Score (if Decision Tree hits its limitations)
+    - [ ]  Decision Tree might be better without needing to test State Machine
+  - [ ]  Composition of local planner
+
+    Acting: {Robert, Alex}
+
+  - [ ]  Detailed understanding of components
+  - [ ]  Comparing trajectory with actual actions
+  - [ ]  which new interfaces are needed? (if needed)