biblio.bib

@book{Sutton:1998:IRL:551283,
 author = {Sutton, Richard S. and Barto, Andrew G.},
 title = {Introduction to Reinforcement Learning},
 year = {1998},
 isbn = {0262193981},
 edition = {1st},
 publisher = {MIT Press},
 address = {Cambridge, MA, USA},
}
@INPROCEEDINGS{4124872,
author={Alves, J. and Oliveira, P. and Oliveira, R. and Pascoal, A. and Rufino, M. and Sebastiao, L. and Silvestre, C.},
booktitle={Control and Automation, 2006. MED '06. 14th Mediterranean Conference on},
title={Vehicle and Mission Control of the DELFIM Autonomous Surface Craft},
year={2006},
pages={1-6},
keywords={data acquisition;marine vehicles;mobile robots;Atlantic;Azores islands;DELFIM autonomous surface craft;ISR/IST;acoustic relay;automatic marine data acquisition;mission control;submerged craft;support vessel;vehicle control;Acoustic testing;Automatic control;Control systems;Data acquisition;Mobile robots;Navigation;Relays;Remotely operated vehicles;Sea surface;Underwater acoustics},
doi={10.1109/MED.2006.328689},
month={June},}
@article{Ettinger2003,
author = {Ettinger, Sm and Nechyba, Mc and Ifju, Pg and M},
file = {:home/pulver/Desktop/PhD/Papers/Flying Robots/Towards flight autonomy$\backslash$: vision-based horizon detection for micro air vehicles.pdf:pdf},
journal = {Florida Conference on},
number = {17},
pages = {617--640},
title = {{Towards flight autonomy: Vision-based horizon detection for micro air vehicles}},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.16.6951{\&}amp;rep=rep1{\&}amp;type=pdf},
volume = {7},
year = {2003}
}
@ARTICLE{4082128,
      author={Hart, P.E. and Nilsson, N.J. and Raphael, B.},
      journal={Systems Science and Cybernetics, IEEE Transactions on},
      title={A Formal Basis for the Heuristic Determination of Minimum Cost Paths},
      year={1968},
      volume={4},
      number={2},
      pages={100-107},
      keywords={Automatic control;Automatic programming;Chemical technology;Costs;Functional programming;Gradient methods;Instruction sets;Mathematical programming;Minimax techniques;Minimization methods},
      doi={10.1109/TSSC.1968.300136},
      ISSN={0536-1567},
      month={July},}

      @book{Gonzalez:2001:DIP:559707,
       author = {Gonzalez, Rafael C. and Woods, Richard E.},
       title = {Digital Image Processing},
       year = {2001},
       isbn = {0201180758},
       edition = {2nd},
       publisher = {Addison-Wesley Longman Publishing Co., Inc.},
       address = {Boston, MA, USA},
      }
      @misc{Hough_1962, place={United States}, title={Method and Means for recognizing Complex Patterns}, abstractNote={This patent relates to a method and means for recognizing a complex pattern in a picture.  The picture is divided into framelets, each framelet being sized so that any segment of the complex pattern therewithin is essentially a straight line. Each framelet is scanned to produce an electrical pulse for each point scanned on the segment therewithin. Each of the electrical pulses of each segment is then transformed into a separate strnight line to form a plane transform in a pictorial display. Each line in the plane transform of a segment is positioned laterally so that a point on the line midway between the top and the bottom of the pictorial display occurs at a distance from the left edge of the pictorial display equal to the distance of the generating point in the segment from the left edge of the framelet. Each line in the plane transform of a segment is inclined in the pictorial display at an angle to the vertical whose tangent is proportional to the vertical displacement of the generating point in the segment from the center of the framelet. The coordinate position of the point of intersection of the lines in the pictorial display for each segment is determined and recorded. The sum total of said recorded coordinate positions being representative of the complex pattern. (AEC)}, author={Hough, P.V.C.}, year={1962}, month={Dec}}
      @article{Maini2009,
      abstract = {Automatic image annotation techniques that try to identify the objects in images usually need the images to be segmented first, especially when specifically annotating image regions. The purpose of segmentation is to separate different objects in images from each other, so that objects can be processed as integral individuals. Therefore, annotation performance is highly influenced by the effectiveness of segmentation. Unfortunately, automatic segmentation is a difficult problem, and most of the current segmentation techniques do not guarantee good results. A multiple segmentations algorithm is proposed by Russell et al. [12] to discover objects and their extent in images. In this paper, we explore the novel use of multiple segmentations in the context of image auto-annotation. It is incorporated into a region based image annotation technique proposed in previous work, namely the training image based feature space approach. Three different levels of segmentations were generated for a 5000 image collection. Experimental results show that image auto-annotation achieves better performance when using all three segmentation levels together than using any single one on its own.},
      author = {Maini, Raman and Aggarwal, H},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Image processing/Study and comparison of various imahe edge detection techniques.pdf:pdf},
      journal = {International Journal of Image Processing {\ldots}},
      keywords = {digital image processing,edge detection,noise},
      number = {3},
      pages = {1--12},
      title = {{Study and comparison of various image edge detection techniques}},
      url = {http://wwwmath.tau.ac.il/{~}turkel/notes/Maini.pdf},
      volume = {147002},
      year = {2009}
      }

      @article{Fischler1981,
      abstract = {A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/ smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing and analysis conditions. Implementation details and computational examples are also presented.},
      author = {Fischler, Martin a and Bolles, Robert C},
      doi = {10.1145/358669.358692},
      file = {:home/pulver/Desktop/PhD/Papers/Obstacle detection/ransac.pdf:pdf},
      isbn = {0934613338},
      issn = {0001-0782},
      journal = {Communications of the ACM},
      keywords = {automated cartography,camera calibration,computer{\_}vision,epipolar,geometry,image matching,keypoints,location determination,match-rejection,model fitting,motion-estimation,multi-view,rpc,scene analysis,sift,stereo,structure{\_}from{\_}motion,tracking},
      number = {6},
      pages = {381 -- 395},
      title = {{Random Sample Consensus: A Paradigm for Model Fitting with Applicatlons to Image Analysis and Automated Cartography}},
      volume = {24},
      year = {1981}
      }

      @article{EWD:NumerMath59,
      author = {Edsger. W. Dijkstra},
      title = {A note on two problems in connexion with graphs.},
      year = {1959},
      journal = {Numerische Mathematik},
      volume = {1},
      pages = {269--271},
      }


      @article{Naeem2009,
      abstract = {An adaptive navigation and control algorithm is presented in this paper based on fuzzy logic and optimal control techniques and applied on an unmanned surface vehicle platform. The navigation system consists of an extended Kalman filter with time-varying parameters. Whilst the autopilots include a fuzzy logic based linear quadratic Gaussian controller and a model predictive controller optimized using a genetic algorithm. Both the controllers use the output of the adaptive navigation system as their feedback and therefore creates an integrated system. A multiple waypoint following scenario is considered and tested in real time. Experimental results are shown that demonstrate the efficacy of the proposed system.},
      author = {Naeem, W. and Sutton, R.},
      doi = {10.1049/cp.2009.1686},
      file = {:home/pulver/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Naeem, Sutton - 2009 - An intelligent integrated navigation and control solution for an unmanned surface craft.pdf:pdf},
      isbn = {978 1 84919 213 2},
      journal = {IET Irish Signals and Systems Conference (ISSC 2009)},
      keywords = {Kalman filter,LQG,covariance matrices,guidance,model predictive control,unmanned surface vehicles},
      pages = {9--9},
      title = {{An intelligent integrated navigation and control solution for an unmanned surface craft}},
      url = {http://ieeexplore.ieee.org/xpls/abs{\_}all.jsp?arnumber=5524711' escapeXml='false'/>},
      year = {2009}
      }
      @article{Bertram2008,
      abstract = {The survey of marine Unmanned Surface Vehicles (USVs) covers both actually built and projected USVs. Most USV developments are found in the USA. USVs remain so far limited to small to medium vessels with limited autonomy.},
      author = {Bertram, Volker},
      file = {:home/pulver/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Bertram - 2008 - Unmanned Surface Vehicles – A Survey.pdf:pdf},
      journal = {Skibsteknisk Selskab, Copenhagen, Denmark},
      pages = {1--14},
      title = {{Unmanned Surface Vehicles : A Survey}},
      year = {2008}
      }
      @article{Khatib1985,
      author = {Khatib, O.},
      doi = {10.1109/ROBOT.1985.1087247},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Path Planning/Real-time Obstacle Avoidance for manipulatos and mobile robots.pdf:pdf},
      journal = {Proceedings. 1985 IEEE International Conference on Robotics and Automation},
      pages = {500--505},
      title = {{Real-time obstacle avoidance for manipulators and mobile robots}},
      volume = {2},
      year = {1985}
      }

      @inproceedings{Tan2010,
      abstract = {In this paper, an obstacle avoidance solution based on rules and criteria is presented. The solution has two parts. In the first part, a set of candidate maneuvers is generated to track a given path. Each candidate differs in speed and in lateral offset to the tracked path. Thus, the USV has the option to change speeds or switch "lanes", or do both. In the second part, the most appropriate trajectory is selected for execution. High priority objectives, such as safety clearance, are represented quantitatively as rules. Candidate maneuvers that flout any of these rules are immediately rejected. Lower priority objectives, such as minimizing travel time, are represented as a priority list of selection criteria. From the remaining candidates, an iterative process of short listing maneuvers is performed. At each step, a single criterion is used to rank and select the top trajectories. The process is repeated for criteria of decreasing priority until only one maneuvers remains. The presented solution has been implemented and tested in simulation. Some of these simulation results will be presented.},
      author = {Tan, Aaron and Wee, Wong Chee and Tan, Timothy Joe},
      booktitle = {2010 International WaterSide Security Conference},
      doi = {10.1109/WSSC.2010.5730288},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Criteria and rule based obstacle avoidaance for usv.pdf:pdf},
      isbn = {978-1-4244-8894-0},
      pages = {1--6},
      title = {{Criteria and rule based obstacle avoidance for USVs}},
      year = {2010}
      }

      @article{Sorbara2015,
      author = {Sorbara, Andrea and Odetti, Angelo and Bibuli, Marco and Zereik, Enrica and Bruzzone, Gabriele},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Design of an obstacle detection system for marine autonomous vehicles.pdf:pdf},
      isbn = {9781479987368},
      keywords = {marine vehicles,obstacle detection,optronic},
      title = {{Design of an Obstacle Detection System for Marine Autonomous Vehicles}},
      year = {2015}
      }
      @article{Schuster2014,
      author = {Schuster, Michael and Blaich, Michael and Reuter, Johannes},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Collision Avoidance for Vessels using a Low-Cost Radar Sensor.pdf:pdf},
      keywords = {collision avoidance,colregs,interacting multiple model filter,path planning,raster grid,ship navigation},
      number = {2009},
      pages = {9673--9678},
      title = {{Collision Avoidance for Vessels using a Low-Cost Radar Sensor}},
      year = {2014}
      }
      @inproceedings{Achanta2009,
      abstract = {Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. We compare our algorithm to five state-of-the-art salient region detection methods with a frequency domain analysis, ground truth, and a salient object segmentation application. Our method outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.},
      author = {Achanta, Radhakrishna and Hemami, Sheila and Estrada, Francisco and Susstrunk, S},
      booktitle = {Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on},
      doi = {10.1109/CVPR.2009.5206596},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Obstacle detection/Frequency-tuned Salient Region Detection.pdf:pdf},
      isbn = {1063-6919 VO -},
      issn = {1063-6919},
      keywords = {Biological system modeling,Biology computing,Frequency domain analysis,Frequency estimation,Image analysis,Image coding,Image segmentation,Object detection,Object recognition,Object segmentation,color,edge detection,frequency-tuned salient region detection,full resolution saliency map,image colour analysis,image resolution,luminance,visual saliency},
      number = {Ic},
      pages = {1597--1604},
      pmid = {5206596},
      title = {{Frequency-tuned salient region detection}},
      year = {2009}
      }

      @article{Harris1988,
      abstract = {Consistency of image edge filtering is of prime importance for 3D interpretation of image sequences using feature tracking algorithms. To cater for image regions containing texture and isolated features, a combined corner and edge detector based on the local auto-correlation function is utilised, and it is shown to perform with good consistency on natural imagery.},
      author = {Harris, C. and Stephens, M.},
      doi = {10.5244/C.2.23},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Obstacle detection/A combined corner and edge detector.pdf:pdf},
      issn = {09639292},
      journal = {Procedings of the Alvey Vision Conference 1988},
      pages = {147--151},
      pmid = {20130988},
      title = {{A Combined Corner and Edge Detector}},
      year = {1988}
      }
      @article{Bouguet1999,
      abstract = {Pyramid LK $\Upsilon$르},
      archivePrefix = {arXiv},
      arxivId = {3629719},
      author = {Bouguet, Jean-yves},
      doi = {10.1016/j.tim.2005.08.009},
      eprint = {3629719},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Obstacle detection/Pyramidal Implementation of the Lucas Kanade feature tracker.pdf:pdf},
      isbn = {0966-842X (Print)$\backslash$n0966-842X (Linking)},
      issn = {0966842X},
      journal = {In Practice},
      number = {2},
      pages = {1--9},
      pmid = {16140533},
      title = {{Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm}},
      volume = {1},
      year = {1999}
      }


      @article{Almeida2009,
      abstract = {Abstract- This work presents the integration of obstacle detection and analysis capabilities in a coherent and advanced C{\&}C framework allowing mixed-mode control in unmanned surface systems. The collision avoidance work has been successfully integrated in an operational autonomous surface vehicle and demonstrated in real operational conditions. We present the collision avoidance system, the ROAZ autonomous surface vehicle and the results obtained at sea tests. Limitations of current COTS radar systems are also discussed and further research directions are proposed towards the development and integration of advanced collision avoidance systems taking in account the different requirements in unmanned surface vehicles},
      author = {Almeida, Carlos and Franco, Tiago and Ferreira, Hugo and Martins, Alfredo and Santos, Ricardo and Almeida, Jos{\'{e}} Miguel and Silva, Eduardo},
      doi = {10.1109/OCEANSE.2009.5278238},
      file = {:home/pulver/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Almeida et al. - 2009 - Radar Based Collision detection developments on USV ROAZ II.pdf:pdf},
      isbn = {978-1-4244-2522-8},
      journal = {Oceans09 Bremen},
      pages = {1--6},
      title = {{Radar Based Collision detection developments on USV ROAZ II}},
      year = {2009}
      }
      @article{Wang2011,
      abstract = {This paper describes a vision-based obstacle detection system for Unmanned Surface Vehicle (USV) towards the aim of real-time and high performance obstacle detection on the sea surface. By using both the monocular and stereo vision methods, the system offers the capacity of detecting and locating multiple obstacles in the range from 30 to 100 meters for high speed USV which runs at speeds up to 12 knots. Field tests in the real scenes have been taken and the obstacle detection system for USV is proven to provide stable and satisfactory performance.},
      author = {Wang, Han and Wei, Zhuo and Wang, Sisong and Ow, Chek Seng and Ho, Kah Tong and Feng, Benjamin},
      doi = {10.1109/RAMECH.2011.6070512},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/A vision based obstacle detection system for unmanned surface Vehicle.pdf:pdf},
      isbn = {9781612842509},
      issn = {2158219X},
      journal = {IEEE Conference on Robotics, Automation and Mechatronics, RAM - Proceedings},
      keywords = {Unmanned Surface Vehicle (USV),computer vision,obstacle detection},
      pages = {364--369},
      title = {{A vision-based obstacle detection system for unmanned surface vehicle}},
      year = {2011}
      }
      @article{Azzabi,
      author = {Azzabi, Tarek},
      doi = {10.1109/CISTEM.2014.7076748},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Obstacle detection for unmanned surface vehicle.pdf:pdf},
      isbn = {9781479973002},
      keywords = {calculate the distance,experimentation results,flow,horizon line detection,matlab simulink,obstacle detection,optical,part will summarize the,the approaches used to,usv,while the forth},
      number = {5},
      pages = {1--7},
      title = {{Obstacle detection for Unmanned Surface Vehicle}},
      year = {2014}
      }
      @article{Wang2012,
      author = {Wang, Han and Wei, Zhuo},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Improvement in real time obstacle detection system for usv.pdf:pdf},
      isbn = {9781467318723},
      keywords = {benjamin feng,chek seng ow,computer vision,junjie,kah tong ho,obstacle detection,surface vehicle,unmanned,usv},
      number = {December},
      pages = {5--7},
      title = {{Improvement in Real-time Obstacle Detection System for USV}},
      volume = {2012},
      year = {2012}
      }
      @article{Feng2011,
      abstract = {This paper presents a real-time obstacle detection system for obstacle detection on the sea surface for Unmanned Surface Vehicle (USV). The system employs both monocular and stereo vision-based methods and offers the capacity of detecting and locating multiple obstacles in the range from 30 to 100 meters for high speed USV. Field tests have been taken and shown that the real-time obstacle detection system for USV can provide stable and satisfactory performance.},
      author = {Feng, Benjamin},
      doi = {10.1109/DSR.2011.6026880},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Real time obstacle detection for unmanned surface vehicle.pdf:pdf},
      isbn = {978-1-4244-9276-3},
      journal = {2011 Defense Science Research Conference and Expo (DSR)},
      keywords = {1,achieve real-time processing,computer vision,created a graphical user,fig,for ease of use,interface for the system,obstacle detection,surface vehicle,unmanned,usv,we have},
      pages = {1--4},
      title = {{Real-time obstacle detection for Unmanned Surface Vehicle}},
      year = {2011}
      }

      @article{Azzabi,
      author = {Azzabi, Tarek},
      doi = {10.1109/CISTEM.2014.7076748},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Obstacle detection for unmanned surface vehicle.pdf:pdf},
      isbn = {9781479973002},
      keywords = {calculate the distance,experimentation results,flow,horizon line detection,matlab simulink,obstacle detection,optical,part will summarize the,the approaches used to,usv,while the forth},
      number = {5},
      pages = {1--7},
      title = {{Obstacle detection for Unmanned Surface Vehicle}}
      }
      @article{Blaich2015,
      author = {Blaich, Michael and Koehler, Steffen and Schuster, Michael and Reuter, Johannes and Tietz, Thomas},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Mission integrated collision avoidance for usv using laser range finder.pdf:pdf},
      isbn = {9781479987368},
      journal = {Oceans 2015 Mts/Ieee},
      pages = {0--5},
      title = {{Mission Integrated Collision Avoidance for USVs using Laser Ranger}},
      year = {2015}
      }
      @article{Casalino2009,
      abstract = {The use of unmanned vehicles in the field of underwater and marine applications is increasing significantly in recent years. Autonomous vehicles (like AUVs and gliders) or teleoperated ones (like ROVs) are currently employed for executing a number of different underwater tasks, like inspecting submerged pipes, executing maintenance interventions on underwater gas- or oil-platforms, collecting environmental or oceanographic data, performing surveys on sites of archeological interest. In parallel with the development of underwater vehicles, unmanned surface vehicles (USVs), are they also witnessing an increasing interest from the robotic community, especially with the goal of performing surveillance applications, like patrolling and maintaining safeguarded against intruders harbours or other ldquocrucialrdquo sites. The potential benefits offered by automated vessels equipped with sensors such as cameras or sonars are quite evident, since they could be used to quickly identify the level of menace of unknown radar track without exposing any human operators to possible threats. However USVs, unlike in the underwater case, have to face the problem of avoiding other vessels which in most cases are manned ones. This is a crucial point especially in that kind of application, where the automated vessel has to move quickly towards a possible menace while at the same time avoiding all the other boats normally operating in the harbour area. Unfortunately, at the current state of art, a reliable methodology to avoid the other vessels and the availability of effective and accurate obstacle detection sensors is still missing. This paper focus its attention on the case of USV used for security applications within a harbour, devising a solution that can be real-time implemented for the obstacle avoidance problem under critical situations where the vehicle as to reach its target as fast as possible while guaranteeing the safety of the other vessels. The presented solution is based on- a three layered hierarchical architecture: the first layer computes a global path taking into account static obstacles known a priori, the second layer modifies this path in a locally optimal way (under certain assumptions) exploiting kinematic data of the moving obstacles, while the last layer reactively avoids obstacles for which such data is not available. The paper was therefore organized as follows: in the first section an introduction and state of art are presented, in the successive section the work discussed the first layer and the methods for the static obstacles avoidance, while in the third the paper focused on the moving obstacles and the proposed avoidance algorithm, while also presenting many different detailed simulation results regarding the performances achievable by the overall architecture. Finally a concluding section also indicate some still open problems and future work directions to be developed.},
      author = {Casalino, Giuseppe and Turetta, Alessio and Simetti, Enrico},
      doi = {10.1109/OCEANSE.2009.5278104},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/A three layered architecture for real time path planning and obstacle avoidance for surveillance usv operating in harbour fields.pdf:pdf},
      isbn = {978-1-4244-2522-8},
      journal = {Oceans 2009-Europe},
      keywords = {AUVs,Art,Marine vehicles,Mobile robots,Path planning,ROV,Radar tracking,Remotely operated vehicles,Sea surface,Surveillance,Underwater vehicles,Vehicle safety,autonomous vehicles,collision avoidance,gliders,harbour fields,kinematic data,marine application,marine safety,mobile robots,obstacle avoidance problem,obstacle detection sensors,radar,real time path planning,remotely operated vehicles,robot kinematics,robotic,security,security applications,surveillance,surveillance unmanned surface vehicles,teleoperated vehicles,telerobotics,three-layered architecture,underwater application,underwater vehicles},
      pages = {1--8},
      title = {{A three-layered architecture for real time path planning and obstacle avoidance for surveillance USVs operating in harbour fields}},
      year = {2009}
      }
      @article{Kuwata2014,
      abstract = {This paper presents an autonomous motion planning algorithm for unmanned surface vehicles (USVs) to navigate safely in dynamic, cluttered environments. The algorithm not only addresses hazard avoidance (HA) for stationary and moving hazards, but also applies the International Regulations for Preventing Collisions at Sea (known as COLREGS, for COLlision REGulationS). The COLREGS rules specify, for example, which vessel is responsible for giving way to the other and to which side of the 'stand-on' vessel to maneuver. Three primary COLREGS rules are considered in this paper: crossing, overtaking, and head-on situations. For autonomous USVs to be safely deployed in environments with other traffic boats, it is imperative that the USV's navigation algorithm obeys COLREGS. Furthermore, when other boats disregard their responsibility under COLREGS, the USV must fall back to its HA algorithms to prevent a collision. The proposed approach is based on velocity obstacles (VO) method, which generates a cone-shaped obstacle in the velocity space. Because VOs also specify on which side of the obstacle the vehicle will pass during the avoidance maneuver, COLREGS are encoded in the velocity space in a natural way. Results from several experiments involving up to four vessels are presented, in what we believe is the first on-water demonstration of autonomous COLREGS maneuvers without explicit intervehicle communication. We also show an application of this motion planner to a target trailing task, where a strategic planner commands USV waypoints based on high-level objectives, and the local motion planner ensures hazard avoidance and compliance with COLREGS during a traverse. © 2013 IEEE.},
      author = {Kuwata, Yoshiaki and Wolf, Michael T. and Zarzhitsky, Dimitri and Huntsberger, Terrance L.},
      doi = {10.1109/JOE.2013.2254214},
      file = {:home/pulver/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kuwata et al. - 2014 - Safe Maritime Autonomous Navigation With COLREGS, Using Velocity Obstacles.pdf:pdf},
      isbn = {978-1-61284-455-8},
      issn = {0364-9059},
      journal = {IEEE Journal of Oceanic Engineering},
      keywords = {COLREGS,unmanned surface vehicle (USV),velocity obstacles (VOs)},
      number = {1},
      pages = {110--119},
      title = {{Safe Maritime Autonomous Navigation With COLREGS, Using Velocity Obstacles}},
      volume = {39},
      year = {2014}
      }
      @article{Kuwata2014a,
      abstract = {This paper presents an autonomous motion planning algorithm for unmanned surface vehicles (USVs) to navigate safely in dynamic, cluttered environments. The algorithm not only addresses hazard avoidance (HA) for stationary and moving hazards, but also applies the International Regulations for Preventing Collisions at Sea (known as COLREGS, for COLlision REGulationS). The COLREGS rules specify, for example, which vessel is responsible for giving way to the other and to which side of the 'stand-on' vessel to maneuver. Three primary COLREGS rules are considered in this paper: crossing, overtaking, and head-on situations. For autonomous USVs to be safely deployed in environments with other traffic boats, it is imperative that the USV's navigation algorithm obeys COLREGS. Furthermore, when other boats disregard their responsibility under COLREGS, the USV must fall back to its HA algorithms to prevent a collision. The proposed approach is based on velocity obstacles (VO) method, which generates a cone-shaped obstacle in the velocity space. Because VOs also specify on which side of the obstacle the vehicle will pass during the avoidance maneuver, COLREGS are encoded in the velocity space in a natural way. Results from several experiments involving up to four vessels are presented, in what we believe is the first on-water demonstration of autonomous COLREGS maneuvers without explicit intervehicle communication. We also show an application of this motion planner to a target trailing task, where a strategic planner commands USV waypoints based on high-level objectives, and the local motion planner ensures hazard avoidance and compliance with COLREGS during a traverse. © 2013 IEEE.},
      author = {Kuwata, Yoshiaki and Wolf, Michael T. and Zarzhitsky, Dimitri and Huntsberger, Terrance L.},
      doi = {10.1109/JOE.2013.2254214},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Safe maritime navigation with COLREGS using velocity obstacles.pdf:pdf},
      isbn = {978-1-61284-455-8},
      issn = {0364-9059},
      journal = {IEEE Journal of Oceanic Engineering},
      keywords = {COLREGS,unmanned surface vehicle (USV),velocity obstacles (VOs)},
      number = {1},
      pages = {110--119},
      title = {{Safe Maritime Autonomous Navigation With COLREGS, Using Velocity Obstacles}},
      volume = {39},
      year = {2014}
      }
      @article{Larson2007,
      author = {Larson, Jacoby and Bruch, Michael and Ebken, John and Warfare, Naval and Diego, San and Diego, San},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Autonomous navigation and obstacle avoidance for unmanned surface vehicles.pdf:pdf},
      isbn = {6195539775},
      keywords = {autonomous,obstacle avoidance,path,robotics,unmanned surface vehicle,usv,waypoint navigation},
      pages = {17--20},
      title = {{Autonomous Navigation and Obstacle avoidance for unmanned surface vehicles}},
      year = {2007}
      }
      @article{Larson2007a,
      abstract = {The Space and Naval Warfare Systems Center, San Diego has been involved in the continuing development of obstacle avoidance for unmanned surface vehicles (USVs) towards the aim of a high level of autonomous navigation. An autonomous USV can fulfill a variety of missions and applications that are of increasing interest for the US Navy and other Department of Defense and Department of Homeland Security organizations. The USV obstacle avoidance package is being developed first by accurately creating a world model based on various sensors such as vision, radar, and nautical charts. Then, with this world model the USV can avoid obstacles with the use of a far-field deliberative obstacle avoidance component and a near-field reactive obstacle avoidance component. This paper addresses the advances made in USV obstacle avoidance during the last two years.},
      author = {Larson, Jacoby and Bruch, Michael and Halterman, Ryan and Rogers, John and Webster, Robert},
      file = {:home/pulver/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Larson et al. - 2007 - Advances in Autonomous Obstacle Avoidance for Unmanned Surface Vehicles.pdf:pdf},
      isbn = {6195539775},
      journal = {Techniques},
      keywords = {autonomous,oa,obstacle avoidance,path planning,reactive,robotics,unmanned surface vehicle,usv},
      pages = {1--15},
      title = {{Advances in Autonomous Obstacle Avoidance for Unmanned Surface Vehicles}},
      year = {2007}
      }
      @article{Leng2013,
      abstract = {This paper presents an algorithm for online path planning of USVs to navigate safely in dynamic, sophisticated environments of oceans. The proposed algorithm is based on Mixed Integer Linear Programming (MILP) which integrated with Velocity Obstacle (VO) approach. MILP is an optimization method under multiple constraint conditions of objective function maximization or minimization. The constraints of environment and maneuverability all can be considered and appended to the constraints conditions expressed in the form of inequality. The objective function and constraint conditions are required in linear by MILP, however, the motion of USV and its path planning are nonlinear. So the principle problem is to transform the nonlinear problem into the linear problem. On the other hand, VO makes a linear prediction, which is well appended into the constraint conditions. MILP has advantages in astringency, optimization, real-time and VO also has the advantages in real-time. The combination of MILP and VO utilized the advantages of rapidity of computations, which is well suited for embedded system of robotic applications. The algorithm is demonstrated via simulation with more safety and acclimation in sophisticated environments of oceans.},
      author = {Leng, Jing and Liu, Jian and Xu, Hongli},
      file = {:home/pulver/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Leng, Liu, Xu - 2013 - Online path planning based on MILP for unmanned surface vehicles.pdf:pdf},
      isbn = {VO  -},
      journal = {Oceans - San Diego, 2013},
      keywords = {Collision avoidance,Heuristic algorithms,Linear programming,MILP,Mathematical model,Online path planning,Optimization,Trajectory,USV,Velocity Obstacle,integer programming,linear programming,marine vehicles,minimisation,mixed integer linear programming,nonlinear problem,objective function maximization,objective function minimization,oceans,online path planning,optimization method,path planning,remotely operated vehicles,unmanned surface vehicle,velocity obstacle},
      pages = {1--7},
      title = {{Online path planning based on MILP for unmanned surface vehicles}},
      year = {2013}
      }
      @article{Schuster2014,
      author = {Schuster, Michael and Blaich, Michael and Reuter, Johannes},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Collision Avoidance for Vessels using a Low-Cost Radar Sensor.pdf:pdf},
      keywords = {collision avoidance,colregs,interacting multiple model filter,path planning,raster grid,ship navigation},
      number = {2009},
      pages = {9673--9678},
      title = {{Collision Avoidance for Vessels using a Low-Cost Radar Sensor}},
      year = {2014}
      }
      @article{Simetti2014,
      author = {Simetti, Enrico and Torelli, Sandro and Casalino, Giuseppe and Turetta, Alessio},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Experimental results on obstacle avoidance for high speed unmanned surface vehicles.pdf:pdf},
      isbn = {9781479949182},
      pages = {0--5},
      title = {{Experimental Results on Obstacle Avoidance for High Speed Unmanned Surface Vehicles}},
      year = {2014}
      }
      @article{Sorbara2015,
      author = {Sorbara, Andrea and Odetti, Angelo and Bibuli, Marco and Zereik, Enrica and Bruzzone, Gabriele},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Design of an obstacle detection system for marine autonomous vehicles.pdf:pdf},
      isbn = {9781479987368},
      keywords = {marine vehicles,obstacle detection,optronic},
      title = {{Design of an Obstacle Detection System for Marine Autonomous Vehicles}},
      year = {2015}
      }
      @article{Tang2012,
      author = {Tang, Pingpeng and Zhang, Rubo and Liu, Deli and Zou, Qijie and Shi, Changting},
      doi = {10.1109/CCDC.2012.6244200},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Research od Neaf field obstacle avoidance for unmanned surface vehicle based on heading window.pdf:pdf},
      isbn = {9781457720727},
      journal = {Proceedings of the 2012 24th Chinese Control and Decision Conference, CCDC 2012},
      keywords = {"Divide and Conquer" Strategy,Dynamic Window method,Heading Window,Near-Field Obstacle-Avoidance,Tangent-Method,USV(Unmanned Surface Vehicle)},
      pages = {1262--1267},
      title = {{Research on near-field obstacle avoidance for unmanned surface vehicle based on heading window}},
      year = {2012}
      }
      @article{Wang2011,
      abstract = {This paper describes a vision-based obstacle detection system for Unmanned Surface Vehicle (USV) towards the aim of real-time and high performance obstacle detection on the sea surface. By using both the monocular and stereo vision methods, the system offers the capacity of detecting and locating multiple obstacles in the range from 30 to 100 meters for high speed USV which runs at speeds up to 12 knots. Field tests in the real scenes have been taken and the obstacle detection system for USV is proven to provide stable and satisfactory performance.},
      author = {Wang, Han and Wei, Zhuo and Wang, Sisong and Ow, Chek Seng and Ho, Kah Tong and Feng, Benjamin},
      doi = {10.1109/RAMECH.2011.6070512},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/A vision based obstacle detection system for unmanned surface Vehicle.pdf:pdf},
      isbn = {9781612842509},
      issn = {2158219X},
      journal = {IEEE Conference on Robotics, Automation and Mechatronics, RAM - Proceedings},
      keywords = {Unmanned Surface Vehicle (USV),computer vision,obstacle detection},
      pages = {364--369},
      title = {{A vision-based obstacle detection system for unmanned surface vehicle}},
      year = {2011}
      }
      @article{Xie2014,
      author = {Xie, Shaorong and Wu, Peng and Peng, Yan and Luo, Jun and Gu, Jason},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/The obstacle avoidance planning of usv based on improved artificial potential field.pdf:pdf},
      isbn = {9781479941001},
      number = {12140500400},
      pages = {746--751},
      title = {{The Obstacle Avoidance Planning of USV Based on Improved Artificial Potential Field}},
      year = {2014}
      }
      @article{Zhang2014,
      author = {Zhang, Rubo and Tang, Pingpeng and Su, Yumin and Li, Xueyao and Yang, Ge and Shi, Changting},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/An adaptive obstacle avoidance algorithm for unmanned surface vehicle in complicated marine environments.pdf:pdf},
      journal = {IEEE/CAA Journal of Automatica Sinica},
      number = {4},
      pages = {385--396},
      title = {{An Adaptive Obstacle Avoidance Algorithm for Unmanned Surface Vehicle in Complicated Marine Environments}},
      volume = {1},
      year = {2014}
      }
      @article{Blaich2015,
      author = {Blaich, Michael and Koehler, Steffen and Schuster, Michael and Reuter, Johannes and Tietz, Thomas},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Mission integrated collision avoidance for usv using laser range finder.pdf:pdf},
      isbn = {9781479987368},
      journal = {Oceans 2015 Mts/Ieee},
      pages = {0--5},
      title = {{Mission Integrated Collision Avoidance for USVs using Laser Ranger}},
      year = {2015}
      }
      @article{Tang2012,
      author = {Tang, Pingpeng and Zhang, Rubo and Liu, Deli and Zou, Qijie and Shi, Changting},
      doi = {10.1109/CCDC.2012.6244200},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/Research od Neaf field obstacle avoidance for unmanned surface vehicle based on heading window.pdf:pdf},
      isbn = {9781457720727},
      journal = {Proceedings of the 2012 24th Chinese Control and Decision Conference, CCDC 2012},
      keywords = {"Divide and Conquer" Strategy,Dynamic Window method,Heading Window,Near-Field Obstacle-Avoidance,Tangent-Method,USV(Unmanned Surface Vehicle)},
      pages = {1262--1267},
      title = {{Research on near-field obstacle avoidance for unmanned surface vehicle based on heading window}},
      year = {2012}
      }
      @article{Zhang2014,
      author = {Zhang, Rubo and Tang, Pingpeng and Su, Yumin and Li, Xueyao and Yang, Ge and Shi, Changting},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/An adaptive obstacle avoidance algorithm for unmanned surface vehicle in complicated marine environments.pdf:pdf},
      journal = {IEEE/CAA Journal of Automatica Sinica},
      number = {4},
      pages = {385--396},
      title = {{An Adaptive Obstacle Avoidance Algorithm for Unmanned Surface Vehicle in Complicated Marine Environments}},
      volume = {1},
      year = {2014}
      }
      @article{Xie2014,
      author = {Xie, Shaorong and Wu, Peng and Peng, Yan and Luo, Jun and Gu, Jason},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/The obstacle avoidance planning of usv based on improved artificial potential field.pdf:pdf},
      isbn = {9781479941001},
      number = {12140500400},
      pages = {746--751},
      title = {{The Obstacle Avoidance Planning of USV Based on Improved Artificial Potential Field}},
      year = {2014}
      }
      @article{Conference2013,
      author = {Chen, Jie and Pan, Wei and Guo, Tinbin and Huang,Chaoxi and Wu,Haitao},
      file = {:home/pulver/Dropbox/Universit{\`{a}}/PhD/Marine Robotic Overview/Obstacle avoidance/An obstacle avoidance algorithm designed for usv based on single beam sonar and duzzy control.pdf:pdf},
      isbn = {9781479927449},
      number = {December},
      pages = {2446--2451},
      title = {{An Obstacle Avoidance Algorithm Designed for USV Based on Single Beam Sonar and Fuzzy Control}},
      year = {2013}
      }