minor update

README.md

@@ -2,12 +2,12 @@
 
 This is a programming exercise for the lecture **Foundations of Artificial Intelligence** (WS20) delivered at the Department of Informatics, TUM. The task in this exercise is to implement a heuristic function and/or a search algorithm with motion
 primitives to solve [CommonRoad](https://commonroad.in.tum.de/) scenarios. The following search algorithms have been implemented as examples:
-	1. Breadth First Search
-	2. Depth First Search
-	3. Depth-limited Search
-	4. Uniform Cost Search (aka Dijkstra's algorithm)
-	5. Greedy Best First Search
-	6. A* Search
+- Breadth First Search
+- Depth First Search
+- Depth-limited Search
+- Uniform Cost Search (aka Dijkstra's algorithm)
+- Greedy Best First Search
+- A* Search
 
 The code is written in Python 3.7 and has been tested on Ubuntu 18.04. As the first step, clone this repository with:
 
@@ -56,14 +56,14 @@ Then, install the dependencies with:
 $ pip install -r requirements.txt
 ```
 
-This will install related dependencies specified in the requirements.txt. 
+This will install related dependencies specified in the `requirements.txt`. 
 
 Next, we move on to the installation of [CommonRoad Drivability Checker](https://commonroad.in.tum.de/drivability_checker). This package provides functionalities such as collision checks, kinematic feasibility checks, road boundary checks, etc. Full installation commands are given below, other installation options can be found [here](https://commonroad.in.tum.de/docs/commonroad-drivability-checker/sphinx/installation.html).
 
 ```sh
 $ git clone https://gitlab.lrz.de/tum-cps/commonroad-drivability-checker.git
 $ cd commonroad-drivability-checker
-$ bash build.sh -e /path/to/your/anaconda3/envs/commonroad-py37 -v 3.X --cgal --serializer -i -j 4
+$ sudo bash build.sh -e /path/to/your/anaconda3/envs/commonroad-py37 -v 3.X --cgal --serializer -i -j 4
 ```
 
 `Note`: you need to substitute `/path/to/your/anaconda3/envs/commonroad-py37` with the path to your Anaconda environment, and `X` with your python version (e. g. setting X to 7 for 3.7).

SMP/batch_processing/batch_processing_config.yaml

@@ -65,7 +65,7 @@ scenario_loader:
   # DEFAULT: loads all scenarios from the specified scenario folder
   # RANDOM: loads randomly selected scenarios from the specified scenario folder
   # SPECIFIC: loads specified scenarios, see below
-  inputmode: DEFAULT
+  inputmode: RANDOM
 
   # number of scenarios to be randomly selected. this only works for inputmode set to RANDOM
   random_count: 3

SMP/maneuver_automaton/motion_primitive.py

 import warnings
-from typing import List
 from copy import deepcopy
+from typing import List
 
 import numpy as np
+from commonroad.common.solution import VehicleModel, VehicleType
 from commonroad.scenario.trajectory import Trajectory, State
-from commonroad.common.solution_writer import VehicleModel, VehicleType
 
 
 class MotionPrimitive:

SMP/route_planner/route_planner/route.py

@@ -14,8 +14,9 @@ from SMP.route_planner.route_planner.utils_route import chaikins_corner_cutting,
 try:
     import pycrccosy
 except ModuleNotFoundError as exp:
-    warnings.warn(f"""You won't be able to use the Curvilinear Coordinate System for the Navigator, 
-                      the calculations won't be precise. {exp}""")
+    pass
+    # warnings.warn(f"""You won't be able to use the Curvilinear Coordinate System for the Navigator,
+    #                   the calculations won't be precise. {exp}""")
 
 
 class RouteType(Enum):

SMP/route_planner/route_planner/route_planner.py

@@ -25,8 +25,9 @@ from SMP.route_planner.route_planner.route import RouteType, RouteCandidateHolde
 try:
     import pycrccosy
 except ModuleNotFoundError as exp:
-    warnings.warn(f"""You won't be able to use the Curvilinear Coordinate System for the Navigator, 
-                          the calculations won't be precise. {exp}""")
+    pass
+    # warnings.warn(f"""You won't be able to use the Curvilinear Coordinate System for the Navigator,
+    #                       the calculations won't be precise. {exp}""")
 
 
 class RoutePlanner:

tutorials/1_search_algorithms/tutorial_informed_search.ipynb

@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [

tutorials/1_search_algorithms/tutorial_uninformed_search.ipynb

@@ -68,10 +68,14 @@
 plt.gca().set_aspect('equal')
 plt.margins(0, 0)
 plt.show()
 ```
 
+%%%% Output: display_data
+
+    ![](data:image/png;base64,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)
+
 %% Cell type:markdown id: tags:
 
 ## 2. Generating a Maneuver Automaton
 
 In the following, we load the pre-generated motion primitives from an XML-File, and generate a **Maneuver Automaton** out of them.

tutorials/2_commonroad_search/tutorial_commonroad_search.ipynb

@@ -179,11 +179,11 @@
 ```
 
 %% Cell type:markdown id: tags:
 
 ### 3.4 Visualizing planned trajectory
-Given that we have constructed a feasible trajectory, we now visualize our planned trajectory.
+Given that we have constructed a feasible trajectory, we now visualize our solution (ego vehicle shown with green rectangle):
 
 %% Cell type:code id: tags:
 
 ``` python
 from SMP.motion_planner.utility import visualize_solution
@@ -201,12 +201,12 @@
 Documentation regarding the **Solution** object can be found [here](https://commonroad-io.readthedocs.io/en/latest/api/common/#solution-class). You may refer to [Vehicle Models and Cost Functions](https://commonroad.in.tum.de/models_cost_functions) for more information. In this tutorial the Kinematic Single-Track Model is used.
 
 %% Cell type:code id: tags:
 
 ``` python
-from commonroad.common.solution import Solution, PlanningProblemSolution
-from commonroad.common.solution_writer import VehicleModel, VehicleType, CostFunction
+from commonroad.common.solution import Solution, PlanningProblemSolution, \
+                                       VehicleModel, VehicleType, CostFunction
 
 # create PlanningProblemSolution object
 kwarg = {'planning_problem_id': planning_problem.planning_problem_id,
          'vehicle_model':VehicleModel.KS,                            # used vehicle model, change if needed
          'vehicle_type':VehicleType.BMW_320i,                        # used vehicle type, change if needed

tutorials/3_motion_primitive_generator/MotionPrimitiveGenerator.py

@@ -15,7 +15,7 @@ import xml.etree.ElementTree as et
 from xml.dom import minidom
 
 from commonroad.scenario.trajectory import State, Trajectory
-from commonroad.common.solution_writer import VehicleType
+from commonroad.common.solution import VehicleType
 from commonroad_dc.feasibility.vehicle_dynamics import VehicleDynamics
 import commonroad_dc.feasibility.feasibility_checker as feasibility_checker
 

tutorials/5_route_planner/tutorial_route_planner.ipynb

@@ -103,11 +103,11 @@
 
 # option 2: retrieve the best route by orientation metric
 # route = candidate_holder.retrieve_best_route_by_orientation()
 
 # print out coordinates of the vertices of the reference path
-print("\nCoordiantes [x, y]:")
+print("\nCoordinates [x, y]:")
 print(route.reference_path)
 ```
 
 %% Cell type:markdown id: tags: