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 |
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.
On Linux (tested with Ubuntu 14.04, 16.04, 18.04)
make
the rapid library in environment/rapid
directory
On MacOS
environment/rapid/Makefile
change TARGET=librapid.so
to TARGET=librapid.dylib
environment/rapid/Makefile
change -soname
to -install_name