Index / Work / N° 02
Project N°02 of 24
CategoryRobotics · Control
Year2025

Path Planning with Localization Uncertainty (RRT + Kalman Filter)

Developed and validated a simulation framework for mobile robot navigation that generates collision-free paths while modeling and minimizing localization uncertainty, balancing tradeoffs between path length, sensing visibility, and collision risk.

Key Engineering Contributions

  1. 01
    Developed a complete RRT-based path planning algorithm, including nearest-neighbor search, bounded expansion, and collision checking for both nodes and path segments .
  2. 02
    Implemented a Kalman Filter framework to propagate localization uncertainty along each trajectory using a state-space model.
  3. 03
    Designed an adaptive sensing/measurement model that varies observability based on proximity to obstacles, linking environment geometry to estimation accuracy .
  4. 04
    Ran large-scale simulations (20,000 paths) to analyze tradeoffs between path length, uncertainty, and collision risk, with visualization using uncertainty ellipses.

Visual Documentation

Example RRT motion plan which is unique to each iteration
Figure 1
01.png
Example RRT motion plan which is unique to each iteration
Example RRT motion plan which is unique to each iteration
Shortest path obtained after 20,000 iterations of the RRT algorithm. The ellipse
Figure 2
02.png
Shortest path obtained after 20,000 iterations of the RRT algorithm. The ellipses were constructed based on projected localization uncertainty in the x and y directions.
Shortest path obtained after 20,000 iterations of the RRT algorithm. The ellipses were constructed based on projected localization uncertainty in the x and y directions.
Path with minimum uncertainty at the terminal state obtained after 20,000 iterat
Figure 3
03.png
Path with minimum uncertainty at the terminal state obtained after 20,000 iterations of the RRT algorithm. The ellipses were constructed based on projected localization uncertainty in the x and y dire
Path with minimum uncertainty at the terminal state obtained after 20,000 iterations of the RRT algorithm. The ellipses were constructed based on projected localization uncertainty in the x and y directions.