TJU-Aerial-Robotics/YOPO

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1153 stars · 144 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

YOPO is a learning-based one-stage planner for quadrotor autonomous navigation in obstacle-dense environments, integrating perception, mapping, path searching, and trajectory optimization into a single neural network. It uses motion primitive anchors with a guidance-learning training strategy that back-propagates trajectory cost gradients instead of imitation or reinforcement learning.

Use cases

• plan trajectories for a quadrotor in obstacle-dense environments
• train a neural network drone planner with guidance learning
• build an autonomous navigation stack for a drone
• run agile tracking and navigation from perception to action
• replace classical front-end search and back-end optimization with a single network
• reproduce research on learning-based quadrotor planning

When to choose

• you need fast, one-stage trajectory planning for quadrotors in cluttered spaces
• you want a learning-based planner without simulator-in-the-loop RL training
• you are doing research on drone navigation and want a strong open-source baseline
• you want matching open-source drone hardware designs for real-world experiments

When to avoid

• you need a general-purpose planner for ground robots or manipulators
• you require a fully certified or safety-verified flight stack
• you want a plug-and-play commercial product rather than research code
• your project does not use ROS or C++

Facets

library · maturity active

machine-learning deep-learning robotics simulation robotics autonomous-vehicles machine-learning deep-learning cpp quadrotor path-planning drone-navigation guidance-learning motion-primitives obstacle-avoidance research-code linux ros

Member repositories

For agents

markdown · JSON · MCP: product_card(name="TJU-Aerial-Robotics/YOPO")