Autonomous-Systems

• Autonomous Parking: Parallel & Reverse
  June 1, 2025 · robotics ros2 autonomous-systems slam

  A ROS 2 project for autonomous parallel and reverse parking on a real car and in Gazebo simulation. The system combines SLAM-based mapping, EKF localization, custom path planners, and parking space detection to fully automate the parking maneuver.

  

  My Contributions

  • Extended Kalman Filter (EKF) Integration: Integrated robot_localization to fuse wheel odometry with IMU data, correcting odometry drift and providing a stable map → odom → base_footprint transform chain.
  • SLAM + Localization Launch: Created the main launch file combining slam_toolbox and ekf_node, configuring process noise, sensor timeout, and the odometry/IMU fusion matrix.
  • New TAS Car Hardware Integration: Configured hardware drivers, merged dual LiDAR scans, and added URDF models and Gazebo world assets for the new car platform.
  • Costmap & Obstacle Avoidance Tuning: Tuned inflation radius and cost scaling factors in the Nav2 costmap configuration for reliable obstacle avoidance in tight parking spaces.
  • Path Following Controller Tuning: Configured RegulatedPurePursuitController parameters (lookahead distance, max angular acceleration, desired linear velocity) for smooth and precise parking execution.
  • Parking Manager Launch: Consolidated the planner, controller, and parking manager nodes into a single unified launch file for both simulation and hardware deployments.
• Subterranean Sentinel: Autonomous MAV in Challenging 3D Environments
  January 1, 2025 · robotics ros2 autonomous-systems path-planning

  Subterranean Sentinel is a fully autonomous Micro Aerial Vehicle (MAV) system that explores unknown underground cave environments, detects semantic targets (lanterns), and returns safely: without any human intervention. The system runs on ROS 2 (Jazzy) with a Unity simulation and implements the full autonomous robotics stack: Perception → Mapping → Planning → Control.

  My Contributions

  • System Design & Architecture: Designed the full modular ROS 2 workspace, defining package boundaries, node responsibilities, topic/service interfaces, and the overall data flow from perception to control.
  • Mission FSM: Designed and implemented the mission Finite State Machine that orchestrates all behaviors: takeoff, cave entry, exploration, Z-retry recovery, checkpoint-graph-based return, and landing.
  • Exploration Pipeline: Built the frontier exploration manager that continuously requests candidate goals, evaluates them, blacklists failed ones, and keeps the drone progressing through unmapped space.
  • RRT* Path Planning: Implemented the RRT* global planner operating directly on the live OctoMap, producing collision-free paths through 3D voxel space.
  • Cloud Gating: Developed the pointcloud gate node that enables/disables OctoMap updates on FSM command, preventing map corruption during non-exploration phases.
  • ROSbag Recording & Analysis: Set up automated bag recording of key mission topics and performed post-flight analysis using Jupyter Notebook to evaluate trajectory quality, goal success rates, and lantern detection timing.
• Investigation of Area- and Path-Planning Algorithms using Panel-Method-Based Guidance
  March 1, 2024 · path-planning autonomous-systems robotics guidance uav

  A research project conducted at TUM’s Institute for Rotorcraft and Vertical Flight, integrating classical robot path-planning algorithms with aerodynamics-inspired Panel-Method-Based guidance to generate smooth, collision-free trajectories for UAVs in urban environments. The panel method, rooted in potential flow theory, treats obstacles as vortex panels to produce a continuous velocity field: eliminating the local-minima problem of classical potential fields. The system was demonstrated in a search-and-rescue scenario: an area-coverage UAV first locates a distress signal using grid-based sweeping guided by the panel method, then a second UAV navigates to the target using a classical path-planning algorithm.