RRT and PRM explore how a robot can find a route through a two-dimensional environment.
01 · Sample
Explore before you commit.
RRT grows a tree through sampled points, steering toward new configurations and checking the connecting edges.
02 · Connect
Build a map of what is possible.
PRM constructs a roadmap. A* searches its connections for a route between a start and a goal.
03 · Follow
One route. Many decisions.
The highlighted route explains the planning idea. This new illustration is separate from the repository’s recorded output.
04 · Evaluate
54 configurations. Better questions.
The harness covers 27 RRT and 27 PRM parameter configurations. Historical benchmark results have not been independently reproduced.
ROBOT MOTION PLANNINGIllustrative planning scene · not a recorded benchmark Read the case study
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Course project · ROBO 201 / 202 · Fall 2025
Robot Motion Planning
Exploring how a robot finds its way through obstacles.
PythonRRTPRMA* search
01 / Purpose
What we set out to do.
Explore how sampling-based algorithms construct a collision-free route between a start and a goal in a two-dimensional environment.
02 / Approach
How it came together.
The RRT implementation grows a tree through sampled points, steering and checking connections for collisions. PRM constructs a roadmap and uses A* to search for a route. A test harness covers 27 RRT and 27 PRM parameter configurations.
03 / Contribution
The work, and the team.
I implemented planning code within a course assignment structure. Repository history records my work, while the assignment’s provided interface remains part of that foundation.
04 / Demonstration
What the visuals show.
Planner visualizations show sampled nodes, connections, obstacles, and the selected path. The 3D extension demonstrates free-space planning; it does not include a demonstrated 3D obstacle-avoidance model.
View image Recorded RRT visualization from the coursework repository, with a sampled tree and highlighted path.Husain Altelly, ROBO 201/202 coursework; assignment interface/template credited to the course.View image Recorded PRM visualization showing the roadmap and a highlighted path found using A* search.Husain Altelly, ROBO 201/202 coursework; assignment interface/template credited to the course.
05 / Results
What we can say.
The repository records benchmark outputs across 54 configurations. Those records have not been independently reproduced for this portfolio. They are parameter configurations, not 54 repeated controlled trials.
06 / Limitations
Where the limits are.
The inspected harness does not establish seeded repeated runs or identical obstacle environments across all parameter settings. Some plotting values are hardcoded, so these figures do not support statistical or average-performance claims.
View image Recorded output from the RRT extension in three-dimensional free space.Husain Altelly, ROBO 201/202 coursework.
07 / Lessons learned
What I’m taking forward.
Algorithm visualizations help explain behavior, while fair performance comparisons also require reproducible environments and repeated measurements.
Blender visuals explain the projects. Balancing-robot geometry, the planning environment, Flowra interface and AURORA lander are original illustrative concepts made for this portfolio. Their movement, example text and layouts are not recorded results, original course CAD, production screenshots or gameplay.
R. R. Ma, L. U. Odhner, A. M. Dollar, “A Modular, Open-Source 3D Printed Underactuated Hand,” ICRA 2013.
The Yale OpenHand Project is an initiative to advance the design and use of robotic hands designed and built through rapid-prototyping techniques in order to encourage more variation and innovation in mechanical hardware. Please visit the Yale OpenHand site for more details.
