Yoink Aerospace
Completed in 2026 at the age of 19
Overview
Yoink-Aerospace bridges the gap between realistic physics simulation and real-world hardware. The project uses Isaac Sim and Isaac Lab to train a robust Reinforcement Learning agent capable of navigating Mars-like terrain. The trained policy is deployed synchronously onto a physical Lynxmotion A4WD1 rover through a digital twin running on ROS 2 FastDDS.
ROS 2 Digital Twin Architecture
Hover over the components to understand the distributed node architecture bridging simulation to physical hardware.
Digital Twin Node
inference.py
Isaac Sim Environment
Runs the Stable Baselines 3 RL policy in real-time. It mirrors the real rover's state from ROS telemetry and outputs calculated drive commands.
ROS 2 FastDDS Network
Publish / Subscribe Middleware
DDS Middleware
Facilitates low-latency, real-time messaging between the high-compute Digital Twin machine and the onboard Raspberry Pi over the network.
Serial Bridge
serial_bridge_node.py
Hardware Comm Bridge
Bridges ROS 2 topics to the physical Arduino over Serial. Translates motor commands and ingests encoder/IMU telemetry.
LiDAR Bridge
lidar_bridge_node.py
Sensor Processing
Processes raw 2D LiDAR scans and publishes them to the ROS 2 network, giving the digital twin its environmental awareness.
Arduino Microcontroller
Low-level PID Control
C++ PlatformIO
Handles the high-frequency PID motor control loops, wheel encoder tick counting, and raw IMU data parsing directly on the rover chassis.
Agent Navigation Simulation
Click anywhere on the Martian terrain to set a new Goal. The rover (simulating a policy via potential field heuristics) will dynamically use its "LiDAR" to steer around obstacles.