Teaching and Research
High school and post-graduate research can benefit from reduced setup time and focus on teaching the concepts that matter.
Open AI gym compatible (alpha). Run Tensorflow on-board in real time for offline or online machine learning.
Swarm and Vision
Supports 10+ Tracked Quad simultaneous and Raspberry Pi compatible with OpenCV.
Video shows an example of a step velocity input.
- Otus tracker and software
- Pixhawk®, Dronecode and Dronekit environment
- Raspberry Pi
- 250 size quadcopter
- Complete dynamical characterization
- Open platform
- Complete, detailed documentation
- Amazing support
Pixhawk is a trademark of Lorenz Meier. Dronecode is a trademark of the Dronecode Project, Inc.Download Datasheet
5 m x 5 m x 5 m
Simple, Fast, and Open Development
Step 1: Implement
Write simple Python or C++ code* so you can focus on the algorithm and quickly iterate. You have full control over the quadcopter programmatically for low and high-level functions.*Or implement your algorithms in C++ in the PX4 firmware for high performance applications (above 100 hz)
Step 2: Simulate
Test your you code in a few seconds before you fly**.**Or simulate using the powerful PX4 simulation tools with SITL or HITL, Gazebo or JMAVSim and more.
Step 3: Fly!
Fly almost anywhere with the precision motion capture tool that is the fastest to set up and the most compact on the market.
Example 1: Drone Following a Ground Robot.
Two Otus are used in this example. The Quadcopter is controlled manually, and the vehicle automatically targets the quadcopters.
Example 2: Neural Network Trained With Reinforcement Learning.
The Otus Quadcopter model, compatible with OpenAi Gym, was trained to target a location using the PPO reinforcement learning algorithm. The initial pose is random.