Drone trajectory planning

November 3, 2025 · 1 min read

Problem

Autonomous navigation in GPS-denied or dynamic environments requires robust local path planning that can react to obstacles in real-time while optimizing trajectory efficiency.

Approach

  • Implemented Model Predictive Control (MPC) for local trajectory optimization
  • Integrated with ROS 2 navigation stack with gazebo simulation and PX4 low levels controllers
  • Tested on a Quadcopter
  • Combined with SLAM

Technologies

  • Frameworks: ROS 2, MPC library CasADi
  • Sensors: LiDAR, IMU
  • Hardware: Quadcopter with pixhawk flight controller and Jetson Orin Nano used for higher computations
Path Planning MPC ROS2
Lorenzo Ortolani
Authors
Lorenzo Ortolani (he/him)
Roboticist · Physical AI & VLA for Humanoids
I build Physical AI — robots that perceive, reason, and act in the real world. My focus is advancing Vision-Language-Action (VLA) policies so that a single humanoid can learn a diverse library of skills and choose the right one for whatever industrial task it faces, generalizing far beyond a single demonstration. I co-founded TalOS Robotics AI to put this in operators’ hands, and I work across the full stack: locomotion and navigation, learned manipulation, sim-to-real, and the agent tooling that wraps it all together.