# Project 3: Barking up a Random Tree

Original price was: \$35.00.Current price is: \$30.00.

Rate this product

Algorithmic Robotics
COMP/ELEC/MECH 450/550
Project 3: Barking up a Random Tree
The documentation for OMPL can be found at http://ompl.kavrakilab.org.
A broad class of motion planning algorithms consider only geometric constraints (e.g., bug algorithms,
visibility graph methods, PRM). These algorithms compute paths that check only whether the robot is in
collision with itself or its environment. In the motion planning literature, this problem is also known as
rigid body motion planning or the piano mover’s problem. Physical constraints on the robot (e.g., velocity
and acceleration limits, steering speed) are ignored in this formulation. While considering only geometric
constraints may seem simplistic, many manipulators can safely ignore dynamical effects during planning due
to their relatively low momentum or high torque motors. Formally, this is known as a quasistatic assumption.
The goal of this project is to implement a simple sampling-based motion planner in OMPL to plan for a rigid
body and then systematically compare your planner to existing methods in the library.
Random Tree Planner
The algorithm that you will be implementing is the Random Tree Planner, or RTP. The RTP algorithm is a
simple sampling-based method that grows a tree in the configuration space of the robot. RTP is loosely based
on a random walk of the configuration space and operates as follows:
1. Select a random configuration qa from the existing Random Tree.
2. Sample a random configuration qb from the configuration space. With a small probability, select the
goal configuration as qb instead of a random one.
3. Check whether the straight-line path between qa and qb in the C-space is valid (i.e., collision free). If
the path is valid, add the path from qa to qb to the tree.
4. Repeat steps 1 – 3 until the goal state is added to the tree. Extract the final motion plan from the tree.
The tree is initialized with the starting configuration of the robot. You might notice that this procedure seems
very similar to other sampling-based algorithms that have been presented in class and in the reading. Many
sampling-based algorithms employ a similar core loop that utilizes the basic primitives of selection, sampling,
and local planning (checking). RTP is one of the simplest possible approaches, with no additional intelligence
in how configurations are sampled, selected, or checked. Improvements to this algorithm are out of scope for
this project, simply implement RTP as described above.
Collision Checking
Sampling-based methods make use of a collision checking routine (also known as validity checker) that
checks whether a given state is collision-free or not. For this project, we have provided two of these
collision checking routines for environments made up of axis-aligned rectangular obstacles and either
1
a point robot or a square robot. Function isValidStatePoint(const ompl::base::State* state,
const std::vector<Rectangle>& obstacles) will return true if the given state is valid given the
vector of rectangles obstacles for a point robot. Similarly, function isValidStateSquare(const
ompl::base::State* state, double sideLen, const std::vector<Rectangle>& obstacles)
will return true if the given state is valid given the vector of rectangles obstacles for a square robot of
side sideLen. The collision checker for the square robot considers the robot’s center as the reference point in
the robot’s body. A rectangular obstacle is defined with the coordinate (x, y) of its lower left corner, its width
and its height. An environment will be a vector that contains several rectangles (including the boundaries)
See CollisionChecking.h file for more details.
Benchmarking Motion Planners
As you have already experienced with Project 1, sampling-based motion planners (including RTP) require
more than one run before you can draw statistically meaningful conclusions about the performance of your
planner or any others. To help with evaluation, you will be using OMPL benchmarking functionalities that
execute one or more planners many times while recording performance metrics in a database.
The ompl::tools::Benchmark class operates in two phases. First is the planning phase, where all of the
planners are executed on the same problem for the given number of runs. Second is the analysis phase,
where the log file emitted by the benchmark class is post-processed into a SQLite database, and statistics for
many common metrics are plotted to a PDF using ompl benchmark statistics.py, or using the online
analysis available through http://plannerarena.org/. The benchmark facilities are extensively documented at
http://ompl.kavrakilab.org/benchmark.html#benchmark code.
Note: ompl benchmark statistics.py requires matplotlib v1.2+ for Python, which can be installed
through your favorite package manager or through Python’s pip program. The Docker container should
already have this installed. The script will produce box plots for continuously-valued performance metrics.
If you are unfamiliar with these plots, Wikipedia provides a good reference. The script will merge with
any existing SQLite database with the same name, so take care to remove any previously existing database
files before running the script. You can also upload your SQLite database to http://plannerarena.org/ to
interactively view your benchmark data.
Project exercises
1. Implement RTP for rigid body motion planning. At a minimum, your planner must derive from
ompl::base::Planner and correctly implement the solve(), clear(), and getPlannerData()
functions. Solve should emit an exact solution path when one is found. If time expires, solve should
also emit an approximate path that ends at the closest state to the goal in the tree.
• Your planner does not need to know the geometry of the robot or the environment, or the exact
C-space it is planning in. These concepts are abstracted away in OMPL so that planners can be
implemented generically.
2. Compute valid motion plans for a point robot within the plane and a square robot with known side
length that translates and rotates in the plane using OMPL. Note, instead of manually constructing the
state space for the square robot, OMPL provides a default implementation of the configuration space
R
2 ×S
1
, called ompl::base::SE2StateSpace.
• Develop at least two interesting environments for your robot to move in. Bounded environments
with axis-aligned rectangular obstacles are sufficient.
2
• Visualize the world and the path that the robot takes to ensure that your planner is implemented
correctly. You must include visualizations of your worlds as well as some example paths your
planner generated in your report.
3. Benchmark your implementation of RTP in the 3D Apartment and Home scenarios from OMPL.app.
Make sure to compare your planner against the PRM, EST, and RRT planners over at least 50 independent
runs. If all of the planners fail to find a solution, you will need to increase the computation time allowed.
Note: see the appropriate OMPL.app .cfg files for the mesh names, start and goal configurations, etc.
They can be found at /usr/local/share/ompl/resources/3D. Do not refer to the .cfg files used
in project 1, as values were tuned to be different. Use the results from your benchmarking to back up
claims in your report about RTP’s performance quantitatively.
• The SE3RigidBodyPlanning.cpp demo provided in project 1 shows a code example of how
to setup a problem using triangle meshes and benchmark planners. Feel free to use this as a
reference for the benchmark exercise. The ompl::app::SE3RigidBodyPlanning class derives
from ompl::geometric::SimpleSetup.
Protips
• It may be helpful to start from an existing planner, like RRT, rather than implementing RTP from scratch.
Check the files at https://github.com/ompl/ompl/tree/main/src/ompl, and on the online documentation
available at http://ompl.kavrakilab.org/.
Make sure you understand what each line of the RRT algorithm is doing. RRT is a much smarter
algorithm than RTP, and while a good starting point, requires you to remove what is not required. You
will be penalized if code that is not a part of the RTP algorithm remains in your implementation.
Additionally, although RRT has a variant that saves intermediate states, you should not implement a
variant of RTP that saves intermediate states.
• The getPlannerData function is implemented for all planners in OMPL. This method returns a
PlannerData object that contains the entire data structure (tree/graph) generated by the planner.
getPlannerData is very useful for visualizing and debugging your tree.
• If your clear() function implemented in RTP is incorrect, you might run into problems when
benchmarking as your planner’s internal data structures are not being refreshed.
• You do not need a nearest neighbor data structure for RTP. RTP has no need for neighbor proximity
queries, as it selects states to extend from at random.
• RTP is not an intelligent planner, and is a very simple algorithm compared to other sampling-based
planners. Keep this in mind when observing its performance.
• For benchmarking, make sure you give the planners a reasonable amount of time to solve the problem.
If the time limit is too low, then it is highly unlikely your planner will solve any instances, and the
resulting benchmark will not be informative. Make sure you understand when the planner returns an
exact solution (it has solved the problem) and when it has returned an approximate solution (it has not
solved the problem, and has returned a guess).
• Solution paths can be easily visualized using the printAsMatrix function from the PathGeometric
class. Use any software you want to visualize this path, but provide your script with your submission.
See http://ompl.kavrakilab.org/pathVisualization.html for more details.
• In exercise 3, make sure to link your program against libompl, libompl app, and libompl app base
against the problem instances. The provided Makefile given in the OMPL installation handout and the
Docker container does this.
3
Deliverables
This project must be completed in pairs. Submissions are due Thursday September 28th at 1pm.
To submit your project, clean your build space with make clean, zip up the project directory into a file
named Project3 <your NetID> <partner’s NetID>.zip, and submit to Canvas. Your code must
compile within a modern Linux environment. If your code compiled in the Docker container, then it will
be fine. Include a README with details on compiling and executing your code. In addition to the archive,
submit a short report that summarizes your experience from this project. The report should be anywhere from
1 to 5 pages in length in PDF format, and contain the following information:
• (2.5 points) A succinct statement of the problem that you solved.
• (2.5 points) A short description of the robots (their geometry) and configuration spaces you tested in
exercise 2.
• (5 points) Images of your environments and a description of the start-goal queries you tested in
exercise 2.
• (5 points) Images of paths generated by RTP in your environments you tested in exercise 2.
• (5 points) Summarize your experience in implementing the planner and testing in exercise 2. How
would you characterize the performance of your planner in these instances? What do the solution paths
look like?
• (15 points) Compare and contrast the solutions of your RTP with the PRM, EST, and RRT planners from
exercise 3. Elaborate on the performance of your RTP. Conclusions must be presented quantitatively
from the benchmark data. Consider the following metrics: computation time, path length, and the
number of states sampled (graph states).
• (5 points) Rate the difficulty of each exercise on a scale of 1–10 (1 being trivial, 10 being impossible).
Give an estimate of how many hours you spent on each exercise, and detail what was the hardest part
of the assignment. Additionally, describe your individual contribution to the project.
Take time to complete your write-up. It is important to proofread and iterate over your thoughts. Reports
will be evaluated for not only the raw content, but also the quality and clarity of the presentation. When
referencing benchmarking results you must include the referenced data as figures within your report.
Additionally, you will be graded upon your implementation. Your code must compile, run, and solve
the problem correctly. Correctness of the implementation is paramount, but succinct, well-organized,
well-written, and well-documented code is also taken into consideration. Visualization is an important
component of providing evidence that your code is functioning properly. The breakdown of the grading of
the implementation is as follows:
• (30 points) A correct implementation of RTP that generates valid motion plans.
• (30 points) Benchmarking of RTP against other planners in all prescribed environments.
Each pair need to provide only one submission.
4

## Reviews

There are no reviews yet.

Scroll to Top