====== Lab10 - Data Collection Planning ((D)TSPN) ======


^ Motivations and Goals  ^
| Become familiar with approaches for for TSPN and (D)TSP |
| Be able to implement Noon-Bean transformation |
^ Tasks ([[courses:b4m36uir:internal:instructions:lab10|teacher]]) ^
| Familiarize with the provided codes and implement any version of a decoupled approach.  |
^ Resources ^
| {{:courses:b4m36uir:labs:lab10-tspn.zip|Lab10-Maze}} TSPN in an environment with obstacles  |
| {{:courses:b4m36uir:labs:lab10-dtsp-v2.zip|Lab10-Dubins}} Dubins TSP  |

===== Neighborhoods in maze scenario =====

  - **Decoupled approach**
    - First, find the goal sequence by the ETSP. Then, find the final trajectory.
    - First, find the goal sequence by the TSP utilizing the found shortest paths between centres of the given regions. Then, find the final trajectory.
  - **Sampling-based approach**
    - Create samples in the goal neighborhoods and all shortest paths. Then, transform the problem to the ATSP by the Noon-Bean transformation

{{:courses:b4m36uir:labs:noon_bean2.png?300|}}

=== Noon-Bean transformation ===

{{:courses:b4m36uir:labs:noon-bean.png?500|}}

===== Data Collection Planning with Curvature-Constrained Vehicles =====

[[https://github.com/AndrewWalker/pydubins]]

[[https://packaging.python.org/tutorials/installing-packages/| Here,]] you can find how to install a specific package into python.

Installation on school computers (without root):
<code bash>
# download script for pip installation
wget https://bootstrap.pypa.io/get-pip.py

# install pip into your home directory
python2 get-pip.py --user
rm get-pip.py
# set PATH in system
echo "PATH=\$PATH:~/.local/bin" >> ~/.bashrc
# use modified .bashrc file
source ~/.bashrc 

# install dubins library into your home directory
pip install dubins --user
</code>

Installation on your own computers (using root):
<code bash>
# install pip for installing Python packages
sudo apt-get install python-pip python-dev build-essential 
sudo pip install --upgrade pip 
sudo pip install --upgrade virtualenv

# install the dubins library
sudo pip install dubins</code>

The following methods are available. For more information, see [[https://github.com/AndrewWalker/pydubins/blob/master/dubins/dubins.pyx]]
<code python>
LSL = 0
LSR = 1
RSL = 2
RSR = 3
RLR = 4
LRL = 5

def path_type(q0, q1, rho):
    '''Identify which type of path is produced between configurations ... '''

def path_length(q0, q1, rho):
    '''Return the total length of a Dubins path ... '''

def path_sample(q0, q1, rho, step_size):
    '''Sample a Dubins' path at a fixed step interval .... '''
</code>