====== Lab 10 ======


===== Planning =====


  * During this lab, you should get a bit deeper understanding of the planning concepts presented during the lectures. 
  * We will work with simple sampling-based planner, the code is not connected to ROS, as our main goal is to play with the planner itself, not with all the robotic infrastructure 
  * Students do not necessarily need to code in this lab, but it's welcome to look at provided source codes to see connection between theory and (programming) reality
===== Prepare your computer =====

  * Download {{courses:aro:tutorials:lab10-planning.zip | ZIP}}, unpack to some working directory
  * Since it need several Python libraties, it is good to create virtual environment for it

<code bash>
cd go_to_your_folder_with_unzipped_file
python3 -m venv venv
. venv/bin/activate && pip install -r requirements.txt
</code>

requirements.txt
<code bash>
contourpy
cycler
fonttools
importlib-resources
kiwisolver
matplotlib
numpy
packaging
pillow
pyparsing
python-dateutil
scipy
shapely
six
zipp
</code>
==== Planner structure ====

  * ''rrtPlanner.py'' contains basic implementation of RRT planner 
  * ''polygonalMap.py'' environment (map) where obstacles are represented as polygons (using Shapely library)
  * ''pointRobot.py'' basic holonomic robot without shape
  * ''shapeRobot.py'' basic holonomic robot with a polygonal (arbirtary) shape
  * ''carLikeRobot.py'' implementation of CarLike robot of rectangular shape
  * ''planningExample.py'' our main working file which creates planning instances, you can run it within venv:

<code bash>
(venv) > python3 planningExample.py
</code>

===== Holonomic planning =====

  * In ''planningExample.py'', set up the holonomic planner in the main block:

<code python>
if __name__ == "__main__":
  setup = prepareStraightLinePlanner
  #or 
  setup = prepareStraightLinePlannerShapeRobot
</code>

  * Run the script, it will plot the test scenario and results of planning



===== Non-holonomic planning =====

  * In non-holonomic planning, robot cannot move arbitrarily in the space, but its motion is limited by internal 'kinematics'
  * In ''planningExample.py'', set up the holonomic planner in the main block:

<code python>
 setup = prepareCarLikePlanner
</code>

  * Run the script, it will plot scneario and result of planning
  * Investigate how the kinematic expansion is planner, look at robot's method 'expand'


===== Path optimization =====
  * You are provided with a non-optimal planner. 
  * Discuss how to get 'better' plans even with non-optimal planners.
  * How are your suggestions compatible with holonomic or non-holonomic movements?
  * Discuss pros/cons of using optimal vs. non-optimal planners for certain tasks

===== Planner testing =====
   * We will create success rate graphs and discuss how different planning settings influene performance, for example:
     * how size of the sampling-space (c-space) changes behavior
     * the presence of 'hard' obstacles
     * is planning with convex/non-cnvex obstacles same?



===== Troubleshooting ===== 
  - How to test new (uknown) planner, which mistakes to avoid