Unmanned aircraft systems in maritime operations: Challenges addressed in the scope of the SEAGULL project

The SEAGULL project aims at the development of intelligent systems to support maritime situation awareness based on unmanned aerial vehicles. It proposes to create an intelligent maritime surveillance system by equipping unmanned aerial vehicles (UAVs) with different types of optical sensors. Optical sensors such as cameras (visible, infrared, multi and hyper spectral) can contribute significantly to the generation of situational awareness of maritime events such as (i) detection and georeferencing of oil spills or hazardous and noxious substances; (ii) tracking systems (e.g. vessels, shipwrecked, lifeboat, debris, etc.); (iii) recognizing behavioral patterns (e.g. vessels rendezvous, high-speed vessels, atypical patterns of navigation, etc.); and (iv) monitoring parameters and indicators of good environmental status. On-board transponders will be used for collision detection and avoidance mechanism (sense and avoid). This paper describes the core of the research and development work done during the first 2 years of the project with particular emphasis on the following topics: system architecture, automatic detection of sea vessels by vision sensors and custom designed computer vision algorithms; and a sense and avoid system developed in the theoretical framework of zero-sum differential games.

[1]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[2]  João Borges de Sousa,et al.  Formation control with collision avoidance , 2011, IEEE Conference on Decision and Control and European Control Conference.

[3]  John Lygeros,et al.  Control of multiple non-holonomic air vehicles under wind uncertainty using Model Predictive Control and decentralized navigation functions , 2008, 2008 47th IEEE Conference on Decision and Control.

[4]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[5]  Kun Liu,et al.  Rotation-Invariant HOG Descriptors Using Fourier Analysis in Polar and Spherical Coordinates , 2014, International Journal of Computer Vision.

[6]  Alexandre Bernardino,et al.  An algorithm for the detection of vessels in aerial images , 2014, 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[7]  Sanjiv Singh,et al.  Avoiding Collisions Between Aircraft: State of the Art and Requirements for UAVs operating in Civilian Airspace , 2008 .

[8]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[9]  J. Sousa,et al.  On the numerical synthesis of optimal feedback controllers , 2011 .

[10]  João Borges de Sousa,et al.  Dynamic programming based feedback control for systems with switching costs , 2012, 2012 IEEE International Conference on Control Applications.

[11]  Pedro Encarnação,et al.  Ground Target Tracking Control System for Unmanned Aerial Vehicles , 2013, J. Intell. Robotic Syst..

[12]  S. Shankar Sastry,et al.  Conflict resolution for air traffic management: a study in multiagent hybrid systems , 1998, IEEE Trans. Autom. Control..

[13]  Emanuele Trucco,et al.  Introductory techniques for 3-D computer vision , 1998 .

[14]  Claire J. Tomlin,et al.  Decentralized cooperative collision avoidance for acceleration constrained vehicles , 2008, 2008 47th IEEE Conference on Decision and Control.