Vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments

• This work establishes a novel robocentric Non-linear Model Predictive Control (NMPC) framework for fast fully autonomous navigation of quadrotors in featureless dark tunnel environments. Additionally, this work leverages the processing of a single camera to generate direction commands along the tunnel axis, while regulating the platform's altitude.  The extracted visual dynamics are coupled in the sequel with the NMPC problem,  structured around the Proximal Averaged Newton-type method for Optimal Control (PANOC), which is a fast numerical optimization method that is not sensitive to ill conditioning and is suitable for embedded NMPC implementations. Multiple fully realistic simulation results demonstrate the effectiveness of the proposed method in challenging environments.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)

Keyword

Autonomous Vehicles

Robot Navigation

Non linear model predictive control

MAV

Robotik och artificiell intelligens

Robotics and Artificial Intelligence

Publication and Content Type

vet (subject category)

ovr (subject category)