T3a-sampl - Randomized sampling-based algorithms - Probabilistic Roadmap

The main task is to implement the Probabilistic Roadmap (PRM).

Deadline 7. December 2019, 23:59 PST
Points 3
Label in BRUTE t3a-sampl
Files to submit archive with the PRMPlanner.py file
Resources T3a-sampl resource package


Assignment

In PRMPlanner.py implement the Probabilistic Roadmap (PRM) randomized sampling-based path planning algorithm according to the description and pseudocode presented in the Lecture 07. The algorithms shall provide a collision free path through an environment represented by a geometrical map.

The PRMPlanner.plan function has the following prescription

import SamplingPlanner as sp
 
...
 
class PRMPlanner(sp.SamplingPlanner):
 
    ...
 
    def plan(self, environment, start, goal, n_samples = 300):
        """
        Method to plan the path
 
        Parameters
        ----------
        environment: Environment
            Map of the environment that provides collision checking 
        start: numpy array (4x4)
            start configuration of the robot in SE(3) coordinates
        goal: numpy array (4x4)
            goal configuration of the robot in SE(3) coordinates
        n_samples: int
            number of C_free samples
 
        Returns
        -------
        list(numpy array (4x4))
            the path between the start and the goal Pose in SE(3) coordinates
        """
 
        #TODO: t3a-sampl - implement the PRM planner
 
        path = []
 
        print (start)
        print (goal)
 
        assert not environment.check_robot_collision(start), 'start collision'
        assert not environment.check_robot_collision(goal), 'goal collision'
 
        path = [start,goal]
 
        return(path)

The pose $\mathbf{P} \in SE(3)$ is given as $$ \mathbf{P} = \begin{bmatrix} \mathbf{R} & \mathbf{T}\\ [0, 0, 0] & 1 \end{bmatrix}, $$ where $\mathbf{R} \in \mathcal{R}^{3\times3}$ is the rotation matrix for which $\mathbf{R}\cdot\mathbf{R} = \mathbf{I}$ and $\det(\mathbf{R})=1$. $\mathbf{T} \in \mathcal{R}^3$ is the translation vector Therefore, the individual poses are the rigid body transformations in the global reference frame. Hence, the position of the robot $r$ is given as the transformation of the robot base pose $\mathbf{r}_b$ in homogeneous coordinates, given as:$$ \begin{bmatrix} \mathbf{r}\\ 1 \end{bmatrix} = \mathbf{P}\cdot\begin{bmatrix} \mathbf{r}_b\\ 1\end{bmatrix}, $$ which can be also written as$$ \mathbf{r} = \mathbf{R}\cdot\mathbf{r}_b + \mathbf{T}. $$

The boundaries for individual configuration variables are given during the initialization of the planner in SamplingPlanner.limits variable as a list of lower-bound and upper-bound limit tuples, i.e. list( (lower_bound, upper_bound) ), for each of the variables $(x,y,z,\phi_x,\phi_y,\phi_z)$. The $(x,y,z,\phi_x,\phi_y,\phi_z)$ vector can be transformed to and from $SE(3)$ using the sp.te_2_se3 and sp.se3_2_te functions, respectively.

The individual poses shall not be further than SamplingPlanner.max_norm_translation, and two consecutive path points' orientations shall not change more than SamplingPlanner.max_norm_rotation. The norms of translation and rotation may be computed using sp.norm_translation and sp.norm_rotation_R, respectively. Note, the configuration space sampling is not affected by this requirement. Individual random samples may be arbitrarily far away; however, their connection shall adhere to the given constraint on the maximum distance and rotation to ensure sufficient sampling of the configuration space and smooth motion of the robot.

The collision checking is performed using the environment.check_robot_collision function that takes on the input an $SE(3)$ pose matrix. The collision checking function returns True if there is collision between the robot and the environment and False if there is no collision. Moreover, the SamplingPlanner.is_valid_edge function can be used to check whether the connection from $SE(3)$ represented point start to $SE(3)$ represented point end is valid both in terms of the collision checker and maximum distances.


RAPID collision checking library installation notes

On Linux (tested with Ubuntu 14.04, 16.04, 18.04)

  1. Download the resource package and make the rapid library in environment/rapid directory

On MacOS

  1. On line 7 of environment/rapid/Makefile change TARGET=librapid.so to TARGET=librapid.dylib
  2. On line 11 of environment/rapid/Makefile change -soname to -install_name
courses/b4m36uir/hw/t3a-sampl.txt · Last modified: 2019/11/17 23:53 by pragrmi1