Vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments⁎This work has been funded by the European Unions Horizon 2020 Research and Innovation Programme under the Grant Agreement No. 730302 SIMS. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Kanellakis, Christoforos; Karvelis, Petros; Mansouri, Sina Sharif; Agha-mohammadi, Ali-akbar; Nikolakopoulos, George
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 9288
• Page End: 9294
• Publication Date: 2020
• Subjects: Autonomous Vehicles -- Robot Navigation -- Non linear model predictive control -- MAV; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.2382
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.