Project title: NeuroSwarm: Multi-Agent Neural 3D Scene Reconstruction and Segmentation with UAV for Optimal Navigation of Quadruped Robot

Description:
Quadruped robots have the distinct ability to adapt their body and step height to navigate through cluttered environments. Nonetheless, for these robots to utilize their full potential in real-world scenarios, they require awareness of their environment and obstacle geometry. We propose a novel multi-agent robotic system that incorporates cutting-edge technologies. The proposed solution features a 3D neural reconstruction algorithm that enables navigation of a quadruped robot in both static and semi-static environments. The prior areas of the environment are also segmented according to the quadruped robots’ abilities to pass them. Moreover, we have developed an adaptive neural field optimal motion planner (ANFOMP) that considers both collision probability and obstacle height in 2D space. Our navigation and mapping approach enable quadruped robots to adjust their height and behavior to navigate under arches and push through obstacles with smaller dimensions. Interns will work on control of a team of quadruped robot and UAV.

Candidates requirements:
➔ basic programming skills in Python or ROS / Gazebo

Supervisor: Associate Professor Dzmitry Tsetserukou

Internship duration: 1-2 months

The start date of the internship: July 12

End day: August 31

Contact person: Dzmitry Tsetserukou d.tsetserukou@skoltech.ru