update description, add goal-less scenarios, add planner, change CF

GSMP/motion_automata/automata/MotionPlanner_Astar.py

@@ -16,6 +16,7 @@ from typing import *
 import warnings
 import copy
 
+# example A* planner
 
 class MotionPlanner:
     def __init__(self, scenario, planningProblem, automata):

GSMP/motion_automata/automata/MotionPlanner_gbfs.py

@@ -15,6 +15,7 @@ import heapq
 import copy
 from typing import *
 
+# example Greedy Best First Search planner
 
 class MotionPlanner:
     def __init__(self, scenario, planningProblem, automata):

GSMP/motion_automata/automata/MotionPlanner_gbfs_only_time.py

@@ -15,6 +15,8 @@ import heapq
 import copy
 from typing import *
 
+# example Greedy Best First Search planner with a heuristic function 
+# that only considers the time_step of the goal region
 
 class MotionPlanner:
     def __init__(self, scenario, planningProblem, automata):
@@ -483,7 +485,7 @@ class MotionPlanner:
         :param path: The path for which you want to calculate the cost
         :param curPos: Last state of your current path. Every state has the following variables: position, velocity, orientation, time_step
         """
-        # a heuristic that considers time and goal lane (if any)
+        # a heuristic that only considers time of goal
         if hasattr(self.planningProblem.goal.state_list[0], 'time_step'):
             time_step_goal = (self.planningProblem.goal.state_list[0].time_step.start + \
                               self.planningProblem.goal.state_list[0].time_step.end) / 2

README.md

@@ -15,6 +15,7 @@ commonroad-search/
 		├batch_processing/
 		├motion_primitives_generator/
 		└tutorials/
+	├pdfs/	
 	├scenarios/
 		├exercise/
 		└tutorial/
@@ -36,7 +37,7 @@ docker run -it -p 9000:8888 --mount src="$(pwd)",target=/commonroad-search,type=
 
      and open the Jupyter Notebook by visiting `localhost:9000` in your web browser.
 
-After you have set up your environment, please further proceed with `notebooks/tutorials/0_Guide_for_Exercise.pdf`. 
+After you have set up your environment, please further proceed with `pdfs/0_Guide_for_Exercise.pdf`. 
 
 ## Installation guide
 
@@ -81,12 +82,12 @@ As documented in CommonRoad-io [Documentation](https://commonroad.in.tum.de/stat
 
 
 ### 2. CommonRoad-Collision-Checker
-Go to folder `GSMP/tools/commonroad-collision-checker/` and follow the instruction in README.rst. (You may navigate to it from this page by  for a better rendering of the .rst file)
+Go to folder `GSMP/tools/commonroad-collision-checker/` and follow the instruction in README.rst. (You may navigate to it from this page for a better rendering of the .rst file)
 
 A tutorial of CommonRoad Collision Checker can be found [here](https://commonroad.in.tum.de/tutorials/).
 
 ### 3. CommonRoad-Road-Boundary
-Go to folder `GSMP/tools/commonroad-road-boundary/` and follow the instruction in README.md. (You may navigate to it from this page by  for a better rendering of the .md file) In case you face an error, refer to troubleshooting section. 
+Go to folder `GSMP/tools/commonroad-road-boundary/` and follow the instruction in README.md. (You may navigate to it from this page for a better rendering of the .md file) In case you face an error, refer to troubleshooting section. 
 
 ## Tutorials
 
@@ -95,7 +96,7 @@ Navigate your terminal to `commonroad-search/` folder, and start Jupyter Noteboo
   $ jupyter notebook
 ```
 
-In the prompt up page, navigate to `notebooks/tutorials/` and follow the tutorials `tutorial_commonroad-io.ipynb` and `tutorial_commonroad-search.ipynb`.  Remember to refer to `tutorials/0_Guide_for_Exercise.pdf` for additional explanation. The executed Jupyter notebooks for tutorials can also be found [here](https://commonroad.in.tum.de/tutorials/).
+In the prompt up page, navigate to `notebooks/tutorials/` and follow the tutorials `tutorial_commonroad-io.ipynb` and `tutorial_commonroad-search.ipynb`.  Remember to refer to `pdfs/0_Guide_for_Exercise.pdf` for additional explanation. The executed Jupyter notebooks for tutorials can also be found [here](https://commonroad.in.tum.de/tutorials/).
 
 ## Implement your own search algorithm
 

notebooks/batch_processing/.ipynb_checkpoints/batch_processing-checkpoint.ipynb

@@ -131,7 +131,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Start Processing"
+    "### Start Processing"
    ]
   },
   {
@@ -153,6 +153,7 @@
     "    \n",
     "    if not filename.endswith('.xml'):\n",
     "        continue\n",
+    "        \n",
     "    fullname = os.path.join(settings['input_path'], filename)\n",
     "\n",
     "    print(\"Started processing scenario {}\".format(filename))\n",

notebooks/batch_processing/batch_processing.ipynb

@@ -131,7 +131,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Start Processing"
+    "### Start Processing"
    ]
   },
   {
@@ -153,6 +153,7 @@
     "    \n",
     "    if not filename.endswith('.xml'):\n",
     "        continue\n",
+    "        \n",
     "    fullname = os.path.join(settings['input_path'], filename)\n",
     "\n",
     "    print(\"Started processing scenario {}\".format(filename))\n",

notebooks/batch_processing/batch_processing_config.yaml

@@ -25,4 +25,4 @@ default:
     # vehicle type, e.g, BMW 320i
   vehicle_type: BMW_320i
     # cost function
-  cost_function: JB1
+  cost_function: SM1

notebooks/batch_processing/batch_search.py

@@ -59,11 +59,14 @@ def execute_search_batch(scenario, planning_problem_set, veh_type_id=2,
 	motion_planner = MotionPlanner(scenario, planning_problem, automata)
 
 	print("Start search..")
+	time_start = time.process_time()
 	result = motion_planner.search_alg(initial_motion_primitive.Successors, max_tree_depth)
-
+	time_end = time.process_time()
+	print("Solving this scenario took {} seconds".format(round(time_end - time_start, 2)))
 	dict_result = {}
 	# result is in form of (final path, used_primitives)
 	if result is not None:
+		
 		result_path = result[0]
 
 		list_state = list()

notebooks/tutorials/.ipynb_checkpoints/tutorial_commonroad-search-checkpoint.ipynb

@@ -333,20 +333,13 @@
     "                               step_size=0.1,                           # time step of scenario, typically 0.1\n",
     "                               vehicle_type=veh_type,                   # vehicle type, change if needed\n",
     "                               vehicle_model=VehicleModel.KS,           # vehicle model, change if needed\n",
-    "                               cost_function=CostFunction.JB1)          # cost funtion, change if needed\n",
+    "                               cost_function=CostFunction.SM1)          # cost funtion, ** do not use JB1 **\n",
     "\n",
     "# add trajectory solution\n",
     "csw.add_solution_trajectory(trajectory=trajectory, planning_problem_id=planning_problem.planning_problem_id)\n",
     "\n",
     "csw.write_to_file(overwrite=True)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

notebooks/tutorials/tutorial_commonroad-search.ipynb

@@ -333,20 +333,13 @@
     "                               step_size=0.1,                           # time step of scenario, typically 0.1\n",
     "                               vehicle_type=veh_type,                   # vehicle type, change if needed\n",
     "                               vehicle_model=VehicleModel.KS,           # vehicle model, change if needed\n",
-    "                               cost_function=CostFunction.JB1)          # cost funtion, change if needed\n",
+    "                               cost_function=CostFunction.SM1)          # cost funtion, ** do not use JB1 **\n",
     "\n",
     "# add trajectory solution\n",
     "csw.add_solution_trajectory(trajectory=trajectory, planning_problem_id=planning_problem.planning_problem_id)\n",
     "\n",
     "csw.write_to_file(overwrite=True)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {