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Autonomous Navigation with RRT* and A* Path Planning in ROS 2

Motion Planning, Autonomous Navigation — State Estimation, Global Planning, Control

Autonomous Navigation

📋 Project Overview

Architected a ROS 2 autonomous navigation pipeline with clear separation between state estimation (pose), global planning, and control, enabling modular testing and parameter tuning.

Implemented A* on an inflated occupancy grid for baseline optimal planning and RRT* for continuous-space, sampling-based motion planning in cluttered environments. Developed a pure-pursuit path tracking controller with lookahead distance, proportional gains, and velocity saturation, validating performance via planned vs. executed trajectory analysis in RViz.

⚡ Key Highlights

  • Modular Architecture: State estimation, global planning, and control cleanly separated
  • A* Planner: Optimal path planning on inflated occupancy grid
  • RRT* Planner: Sampling-based motion planning for cluttered environments
  • Pure-Pursuit Controller: Lookahead distance, proportional gains, velocity saturation
  • Validation: Planned vs. executed trajectory comparison in RViz

Skills Demonstrated

ROS 2 A* RRT* Motion Planning Pure-Pursuit Occupancy Grid RViz Autonomous Navigation Path Planning

More details and images coming soon.