Team CYNET.ai - SubT Challenge Qualifier

On June 11, 2019, Team CYNET.ai was selected by DARPA (the Defense Advanced Research Projects Agency) as a qualifier in its ground-breaking SubTerranean Challenge (SubT). SubT is a multi-million dollar competition that will task teams of robots with autonomous exploration deep beneath the surface of the Earth.

After a few months of developing software for the qualification process Team CYNET.ai was one of the only 10 teams around the world chosen to continue through the virtual track of the competition. Other competitors include NASA, CalTech, MIT, Carnegie Mellon, Georgia Tech and several other robotics companies. Most of them are being funded by DARPA. Team CYNET is self-funded and doesn't benefit from the availability of university labs.

Want to be a part of the team’s success? The SubT Challenge is a multi-year, complex endeavour. Team CYNET is looking to form partnerships and welcomes any kind of financial and equipment assistance in order to compete with the major institutional teams. Sponsors will get exposure in our media, demos, videos and tech articles.


The DARPA Subterranean Challenge aims to develop innovative technologies that would augment operations underground. The SubT Challenge will explore new approaches to rapidly map, navigate, search, and exploit complex underground environments, including human-made tunnel systems, urban underground, and natural cave networks.
Check out more details about SubT.

Darpa SubT Challenge Tunnels
Tunnel Systems (2019)

Human-made tunnel systems, like mines. Current stage of the competition.

Subterranean Challenge underground robots
Urban Underground (2020)

Urban and municipal underground infrastructure, like subway stations.

DARPA SubT cave robotics
Cave Networks (2020)

Naturally formed networks of tunnels and chambers, with rugged terrain.

Programming the next generation of
autonomous underground robots



Team CYNET.ai is using advanced technologies in the field of robotics, computer vision and deep learning in order to deploy a swarm of robots through the current tunnel environment. These robots move auonomously, they build a map of the unknown environment, exmplore it, find artifacts, calculate their location, and then communicate their findings back to the Base Station.

System Components

Our robotics software project has three main components:

  • Mapping and localization

    The robots don't know the environment. As they explore it, they must both create a dynamic map and calculate their position by sensor fusion.

  • Robot motion control

    It involves both optimized path planning through the tunnel structure (as battery life is limited) and a PID controller to steer through the narrow passages.

  • Artifact detection

    Images from the onboard camera pass through a convolutional neural network to identify and localize known objects.

Technologies

These are just some of the technologies we're using for this project.

  • ROS framework
  • Gazebo Ignition simulation
  • C++ and Python nodes
  • Simultaneous Localization and Mapping (SLAM)
  • OpenCV image processing
  • Deep learning with PyTorch
  • YoloV3 object detector

Meet Our Team

Chris Fotache SubT Challenge
Chris Fotache

Team CYNET captain, Chris is the deep learning expert on the team, as well as an experienced Python developer and robotics engineer.

Subhas Das SubT Challenge
Subhas Das

Subhas is our strongest software developer, with deep knowledge of the ROS system, C, embedded systems and robot control.

Shakti Dhar Sharma SubT Challenge
Shakti Dhar Sharma

Shakti is a master roboticist with extensive experience in building and programming autonomous rovers and drones.

Snapshots from the Qualification Scenario

Darpa SubT images

Two rovers are heading into the tunnel network

SubT Challenge tunnels

At the first intersection, they split to cover both tunnels

DARPA SubT Challenge Robots

Cross-section view of the first intersection

Team CYNET robots in Darpa SubT Challenge

Approaching a fire extinguisher and a narrow passage

Team CYNET PyTorch Object Detection

The rover closing in on artifact #1

Darpa SubTerrenean Robotics competition

Decision time at a new intersection

Team CYNET robotics NJ research

A radio device is detected.

Darpa SubT Challenge qualifiers

Robots reunite to exit the tunnels

Darpa SubTerranean Challenge competitors

And there is light at the end of the tunnel

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