This workshop is aimed at presenting recent developments in marine robotics collecting keynote talks from experts of the main research institutions. Research presentations and discussions will complete the agenda. The scope of the workshop is intentionally broad with talks concerning single AUVs, multiple, coordinate marine robots, sensor networks, cooperative adaptive sampling, Underwater Vehicle-Manipulator Systems and biomimetic underwater robotics. An interesting overview with fruitful discussion and ideas for research directions are the expected outcomes of the workshop.
Following the creation of a new IEEE RAS Technical Committe in Marine Robotics, the objective of the workshop is to bring together several experts in different disciplines interested in marine robotics, but who normally do not interact with each other. This will reinforce the community of roboticists interested in marine theory and applications. Several topics are of interest, from homing to coordinated AUVs, coordinated control of multiple marine vehicles up to underwater intervention with AUVs equipped with a manipulator. This workshop will feature keynote talks, research presentations and discussions in these areas to report on the state-of-the-art in different related areas and to identify new directions for research.
Gianluca Antonelli was born in Roma, Italy, on December 19, 1970. He received the Laurea degree in Electronic Engineering and the Research Doctorate degree in Electronic Engineering and Computer Science at the University of Naples in 1995 and 2000, respectively. He currently is an Associate Professor at the University of Cassino. From September 1, 2005 is Associate Editor of the IEEE Transactions on Robotics. He is Senior Member IEEE since June 2006. From October, 2007 he is Editor of the Springer Journal of Intelligent Service Robotics. From June, 2008 he is Chair of the IEEE Robotics and Automation Society Technical Committee in Marine Robotics. His research interests include simulation and control of underwater robotic systems, force/motion control of robot manipulators, multi-robot systems, identfication. He has published 23 international journal papers and more than 60 conference papers, he is author of the book ``Underwater Robots'' (Springer-Verlag, 2003, 2006). (back to schedule)
Andreas Birk is a professor (associate) in Electrical Engineering and Computer Science at Jacobs University Bremen where he leads the robotics group. He started at Jacobs University in Fall 2001 while rejecting an offer for a professorship (C3) at the University of Rostock. Before he joined Jacobs University, he held a research-mandate of the Flemish Society for Applied Research, IWT. He was in addition from October 1997 on a visiting professor (docent) at the Vrije Universiteit Brussel (VUB). He also worked as a visiting professor (C3) at the Universitat Koblenz-Landau in the winter-semester of 1999/2000. During the almost six years at the VUB, Andreas Birk was a member of the Artificial Intelligence Lab, which he joined as Postdoc in April 1996. In 1995 he received his doctorate from the Universitat des Saarlandes, Saarbrucken, where he previously studied Computer Science from fall 1989 to spring 1993. Andreas Birk's research focuses on autonomous systems. On the engineering side, he is working on the design and construction of complete systems. This includes the design and construction of embedded hardware and mechatronics as well as software development up to full autonomy. On the basic research side, he is interested in a constructive understanding of intelligence. He published three books as editor and more than 90 journal articles, book-chapters and peer-reviewed conference papers. (back to schedule)
Giuseppe Casalino was born in Genova, Italy, in 1949. He received the ``Laurea'' degree in Electronic Engineering from the University of Genova in 1975. Currently he is full professor at the Department of Communication, Computer and System Science (DIST) of the University of Genova, holding the chair ``Industrial Robotics'' and also serving as the Director of the Laboratory of Robotics and Automation. He is also the President of the Scientific and Technological Board of SIIT (Integrated Intelligent System Tecnologies): a Scientific and Technological District in the Ligurian Region, incorporating the University of Genova, major LE's and a large number of SME', with the mission of performing coordinated RD and technology transfer activities in the field of Intelligent Automation Systems. Previous positions covered were at University of Pisa (full. prof., chair of ``Industrial Robotics''), University of Calabria (full. prof., chair of ``Automatic Control'') and originally at University of Genova (associate prof. of ``Multivariable Control Theory'' and ``Industrial Robotics''). Also he has been the director of DIST, other than the Deputy Director of the Technology Transfer Department of the whole University of Genova. Moreover he also served, at National level, as President of the Italian Academic Association of Automation (CIRA) His research activities are since many years in the field of Robotics and Automation, with general interests in all aspects involving planning, motion and interaction control problems within multi-robot cooperating structures; and focused interests in the field of marine and space robotic applications. He is, and has been, the responsible scientist of different EEC funded collaborative research projects; as well as of many Nationally funded ones, all in the field of Robotics and Automation. (back to schedule)
Peter Drewes received is Mse and Phd from the University of Central Florida in Computer Engineering. He is currently a Principal research engineer at the Advanced Technology Laboratories for Lockheed Martin Corporation. His scientific research focuses on the development of new solutions for autonomous and semi-autonomous surface and underwater platforms. He has published over 30 conference and journal papers in simulation, training and usage of intelligent agents and unmanned systems. (back to schedule)
Matthew Dunbabin graduated from the Royal Melbourne Institute of Technology with a Bachelors Degree in Aerospace Engineering in 1995. He then worked as a research engineer at Roaduser Research, a Melbourne based consultancy before commencing his PhD in nonlinear vibration control at the Queensland University of Technology. In 2002 he received his PhD and joined the CSIRO field robotics team, which later became the ICT Centre Autonomous Systems Laboratory. He currently has the roles of Senior Research Scientist as well as Research Stream Leader for the development of advanced solutions to monitor and understand coastal marine environments. His research interests are in the area of field robotics, in particular underwater and mining robots, with focus on dynamics and control, underwater vision-based navigation, cooperative robotics, novel AUV and ASV designs, as well as robot and sensor network interactions. (back to schedule)
Franz Hover received the Sc.D. from the Woods Hole Oceanographic Institution/Massachusetts Institute of Technology Joint Program in Oceanographic and Mechanical Engineering. He completed a postdoctoral fellowship with the Monterey Bay Aquarium Research Institute, and was also a consultant to industry and academia. He is currently an Assistant Professor of Mechanical Engineering at MIT, with research interests in large-scale systems, robotics and automation, and design. (back to schedule)
Kazuo Ishii received B.E, M.E. and Ph.D degrees from the department of Naval Architecture and Ocean Engineering at the University of Tokyo in 1991, 1993, and 1996, respectively. He is currently an associate professor at the department of Brain Science and Engineering, Kyushu Institute of Technology. His research interests include field robotics, underwater robotics, brain-inspired technology and its application to robotics. (back to schedule)
Hayato Kondo graduated from Waseda University with Bachelor and Master Degrees in mechanical engineering. He graduated from the University of Tokyo and received his Ph.D. in naval architecture in 2002. After doing research as a post doctoral fellow (JSPS) at the Institute of Industrial Science, the University of Tokyo, he started teaching, as an associate professor, at Tokyo University of Mercantile Marine, which later became TUMSAT after a merger in 2003. He specializes in intelligent ocean vehicles such as Autonomous Underwater Vehicles. He is an Asia-Pacific vice-chair of the technology committee of Ocean Engineering Society, IEEE on Unmanned Maritime Vehicles and Submersibles. (back to schedule)
Giacomo Marani was born in Chieti, Italy. He received the ``Laurea'' degree in Electronic Engineering and the PhD degree in Robotics and Automation from the University of Pisa, Italy, in 1997 and 2000 respectively. From September 2000 he is with the University of Hawaii at Manoa where he is the deputy director of the Autonomous Systems Laboratory. He serves as acting PI of the SAUVIM project, involving the design and development of an intervention-class AUV, managing the overall technical coordination between the project entities. From October, 2007 he is with the editorial board of the Springer Journal of Intelligent Service Robotics. From June, 2008 he is Co-Chair of the IEEE Robotics and Automation Society Technical Committee in Marine Robotics. His scientific researches focus on the development of new solutions for autonomous manipulation with robotic devices, applied to underwater vehicles, dynamic simulation and control of robotic structures, graphic applications for telerobotics, real-time programming and hardware applications. (back to schedule)
Vera Zaychik Moffitt is a lead engineer for the Artificial Intelligence Laboratory at Lockheed Martin Advanced Technology Laboratories in Cherry Hill, New Jersey. Ms. Moffitt is the technical lead and principal investigator on the Supervision of UxV Mission Management by Interactive Teams (SUMMIT) program and was the chief Human-Machine Interface designer for the Lockheed Martin team on the Intelligent Control and Autonomous Replanning of Unmanned Systems (ICARUS) program in the Office of Naval Research. Ms. Moffitt’s current research interests include human-robot interaction, collaboration interfaces, and unmanned system intelligent autonomy. Ms. Moffitt received her education from Drexel University where she studied computer science and artificial intelligence. (back to schedule)
Kristin Y. Pettersen received her MSc and PhD degree in Electrical Engineering at the Norwegian University of Science and Technology (NTNU), Trondheim, Norway, in 1992 and 1996 respectively. She became Associate Professor in 1996 and in 2002 Professor at the Department of Engineering Cybernetics, NTNU. In 1999 she was a Visiting Fellow at the Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ and in 2008 Visiting Professor at Section for Automation and Control, University of Aalborg, Denmark. She has published more than 90 conference and journal papers. In 2006 she received the IEEE Transactions on Control Systems Technology Outstanding Paper Award. She is a senior member of IEEE and at the editorial board of Simulation Modelling Practice and Theory. She furthermore holds several board positions in industrial and research companies. Her research interests include nonlinear control of mechanical systems with applications to robotics, satellites, AUVs and ships. (back to schedule)
Gaurav Sukhatme is an Associate Professor of Computer Science (joint appointment in Electrical Engineering) at the University of Southern California (USC). He received his undergraduate education at IIT Bombay in Computer Science and Engineering, and M.S. and Ph.D. degrees in Computer Science from USC. He is the co-director of the USC Robotics Research Laboratory and the director of the USC Robotic Embedded Systems Laboratory which he founded in 2000. His research interests are in multi-robot systems and sensor/actuator networks. He has published extensively in these and related areas. Sukhatme has served as PI on numerous NSF, DARPA and NASA grants. He is a Co-PI on the Center for Embedded Networked Sensing (CENS), an NSF Science and Technology Center. He is a senior member of IEEE, and a member of AAAI and the ACM. He is a receipient of the NSF CAREER award and the Okawa foundation research award. He has served on many conference program committees, and is one of the founders of the Robotics: Science and Systems conference. He is one of the program chairs of the 2008 IEEE International Conference on Robotics and Automation. He is the Editor-in-Chief of Autonomous Robots. He has served as Associate Editor of the IEEE Transactions on Robotics and Automation, the IEEE Transactions on Mobile Computing, and on the editorial board of IEEE Pervasive Computing.
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Cognitive Cooperative Control of AUVs. The EU-project "Cooperative Cognitive Control for Autonomous Unterwater Vehicles (Co3-AUVs)" is a joined endeavour of the Robotics group of Jacobs University, the Interuniversity Center Integrated Systems for Marine Environment (ISME), the Institute for Systems and Robotics of the Instituto Superior Tecnico (ISR/IST), and the SME Graal Tech. The aim of the Co3-AUVs project is to develop, implement and test advanced cognitive systems for coordination and cooperative control of multiple AUVs. Several aspects are investigated including 3D perception and mapping, cooperative situation awareness, deliberation and navigation as well as behavioral control strictly linked with the underwater communication challenges. As a result, the team of AUVs will cooperate in challenging scenarios in the execution of missions where all data is processed online. In doing so, the team will be robust with respect to failures and environmental changes. These key features will be tested in a harbor scenario where additional difficulties with respect to open sea applications arise and in a human diver assistance scenario that also illustrates human robot interaction issues. The talk presents current achievements of the project partners and the roadmap of the project to toward its final ambitious goals. (back to schedule)
Toward a Tecnolgical and Methodological Framework or Underwater Cooperative Adaptive Sampling: Current Experience with the Folaga Vehicle. The paper describes the main developments regarding the realization of a modular technological and methodological framework, specifically conceived for integrating and fully exploiting the space and temporal sensing capabilities offered by the use of multiple cooperating low-cost, light-weight autonomous underwater vehicles (AUV), within the field of coastal oceanographic applications. Following a brief description of the general architecture of the overall system, the paper will then focus on the following fundamental aspects regarding its current developments:
1- Vehicle class
The vehicles belong to the low-cost, low-weight class named Folaga (the Italian name of an aquatic bird) specifically tailored for oceanographic sampling missions The main design characteristics of the most recent vehicle of the class will be reviewed; and its navigation and control system design will be also described in terms its innovative functional and algorithmic architecture
2- Communication and Localization
Aspects concerning acoustics underwater communication among the vehicle team, between the team and the surface stations to be used for data collection, as well as for real-time localization, will be discussed. This in terms of both the advanced communication technology currently in use, as well as in terms of the employment of novel algorithmic techniques for efficient and reliable underwater localization.
3- Cooperation policy and algorithms
Distributed cooperation algorithms to be applied by a team of Folaga-like vehicles in adaptive oceanographic sampling applications will be described. The algorithms optimizes the area coverage while taking into account the accuracy in the reconstruction of the oceanographic field, as well as the maintenance of inter-vehicle communication within assigned range constraints. In particular it will be shown how the resulting dynamic programming based algorithms can be implemented in a distributed fashion among the team components.
4- Vessel, sea trials, and simulative experiments
For each one of the items listed above, the paper will present the relevant simulative experiments and, in particular for items 1, 2, 3, also the different associated validating experimental results obtained in occasion of various vessel and sea trials, all performed in correspondence of any achieved technological advance in development of the supporting components of the system. For what concerns instead item 4, for the time being only the available preliminary simulative experiments will be presented, actually encouraging toward further developments of the adopted, distributed dynamic programming based, approach.
The paper will be organized into five sections, with the first four just corresponding to the previously listed items, plus a fifth one, were some concluding remarks regarding the present development state and the future perspectives will be drawn.
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Collaborative Command and Control of Unmanned Surface Operations. Lockheed Martin Advanced Technology Laboratories (LM ATL) has recently performed experiments in cooperation with the U.S. Navy involving collaborative unmanned surface and unmanned air vehicle mission execution. Our multi-domain, collaborative research has focused on increasing unmanned vehicles’ ability to be useful partners in maritime security by effectively using intelligent onboard behaviors, collaborative control, and efficient human-system interfacing in environments that only support limited communications. The goal of this research is the collaborative command and control (C2) of heterogeneous autonomous unmanned vehicles and unattended sensors by small teams of operators. Our work has produced technology supporting the ability for an operator to command multiple unmanned assets from within a variety of environments and seamlessly transition control to distant users to share control responsibilities. This supports an operational concept in which the reduced teams of operators perform oversight of UxV operation at an adaptable level of autonomy, giving the UxVs mission-related commands by designating patrol areas, requesting imagery or video of objects of interest, and specifying rules of behavior should unexpected events occur during mission execution. This research strives to improve the effectiveness of controlling unmanned systems while avoiding the workload problems that teleoperation and other low level UxV control paradigms incur. This paper presents lessons learned from these experiments related to collaborative command and control of unmanned surface operations. (back to schedule)
Marine robot and sensor network interaction. Many scientifically relevant marine and inland water environments are too large and dynamic for efficient and cost effective data collection by robotic systems alone. Autonomous Underwater Vehicles (AUVs) and Autonomous Surface Vehicles (ASVs) offer continuous data collection capabilities at a more localised (transect) scale. Wireless surface and underwater sensor networks allow larger scale data collection although at a generally lower spatial resolution. A method for improving capture of relevant dynamic properties in these environments is to integrate the technologies in a way that they compliment each other. The CSIRO Autonomous Systems Laboratory has been developing solutions to allow robots and sensor networks to interact in an effort to detect and monitor particular hydrodynamic and biogeochemical events in real-time without human intervention. These include in-network event detection, distributed communication and data sharing, network calibration, multi-robot interaction, model-based path planning and resource management. This presentation will discuss recent research activities and present experimental results demonstrating integration of autonomous surface and underwater vehicles with in-situ sensor networks for improving science delivery and operational functionality. (back to schedule)
Path Planning for Data Assimilation in Mobile Environmental Monitoring Systems. By combining a low-order model of forecast errors, the extended Kalman filter, and classical continuous optimization, we develop an integrated methodology for planning mobile sensor paths to sample continuous fields. Agent trajectories are developed that specifically take into account the fact that available data will be used for near real-time assimilation. This aspect has significant implications because the trajectories generated are very different from those which do not take the assimilation step into account, and their performance in controlling error is notably better. (back to schedule)
Brain-inspired technology for AUVs. Autonomous Underwater Vehicles (AUVs) are attractive tools to survey earth science and oceanography, however, there exists a lot of problems to be solved such as motion control, acquisition of sensor data, decision-making, navigation without collision, self-localization and so on. In order to realize useful and practical robots, underwater vehicles should take their action by judging changing condition using their own sensors and actuators. We have been investigated applications of brain-inspired technologies such as Neural Networks (NNs), Self-Organizing Map (SOM) and extension mnSOM, Central Pattern Generator (CPG) into underwater robots. We discuss the applications of these technologies into underwater robots. (back to schedule)
Hovering AUVs and their applications. Autonomous Underwater Vehicles (AUVs) are often used for conducting ocean bottom surveys. Since they have no umbilical cable they can travel much longer distances than Remotely Operated Vehicles (ROVs). AUVs usually require high energy efficiency to prolong the operational time while achieving lighter weights. Typical AUVs have a low-drag, faired body and one propeller on its aft, and many of them have control surfaces i.e., wings. We refer to this style of AUVs as cruising-type AUVs. Becoming more common recently are AUVs with multiple thrusters that can move in various directions and hover around closely to underwater structures for visual imaging. Such vehicles are expected to replace small-size or low-cost ROVs, especially when free vehicle movements required for imaging lead to entangled umbilical codes. We refer to this type of AUVs as hovering-type AUVs. This presentation will focus on hovering AUV designs and their actual research applications. (back to schedule)
Advances in Autonomous Underwater Intervention for AUVs. Today's underwater intervention tasks are mostly performed with extensive human supervision, requiring high-bandwidth communication link, or in structured environments, which results in limited applications. Autonomous manipulation systems will make it possible to sense and perform mechanical work in areas that are hazardous to humans or inaccessible, such as natural or man-made disastrous regions, deep-ocean, and under-ice. Autonomous manipulation systems, unlike teleoperated manipulation systems, that are controlled by human operators with the aid of visual and other sensory feedback, must be capable of assessing a situation, including self-calibration based on sensory information, and executing or revising a course of manipulating action without continuous human intervention. The development of autonomous manipulation can be regarded as a gradual passage from human teleoperated manipulation. Within this passage, the most noticeable aspect is the increase of the level of information exchanged between the system and the human supervisor. In teleoperation with ROVs, the user sends and receives low level information in order to directly set the position of the manipulator with the aid of a visual feedback. Instead, as the system becomes more autonomous, the user may provide only a few higher level decisional commands, interacting with the task description layer. The management of lower level functions (i.e. driving the motors to achieve a particular task) is left to the onboard system. In this classification, the level of autonomy is related to the level of information needed by the system in performing the particular intervention. The main approach is layered into different levels, where different behaviours take place: a low-level layer which interacts with the robot hardware, a medium-level layer for describing the controls algorithms and finally a high-level layer where the task description is performed. At this task execution level, the system must be capable of acting and reacting to the environment with the extensive use of sensor data processing. This presentation introduces the solutions chosen to address the above issues for autonomous manipulation, developed during the course of the SAUVIM research project. SAUVIM has been jointly developed by the Autonomous Systems Laboratory (ASL) of the University of Hawaii, Marine Autonomous Systems Engineering (MASE), Inc. in Hawaii, and Naval Undersea Warfare Center Division Newport (NUWC) in Rhode Island. The experimental results of the first attempt of underwater autonomous manipulation, here also presented, are promising. The recovery operation consists in a sequence of autonomous tasks finalized to search for the target, and to securely hook a cable to it, in order to bring the target to the surface. To the best knowledge of the authors, no sea trials in underwater autonomous manipulation of this nature have been presented in the literature. The successful trial of the presented recovery experiment represents a positive testing of the above solution. (back to schedule)
Unmanned Surface Systems Experimentation. Lockheed Martin Advanced Technology Laboratories has been performing collaborative unmanned surface (USV) and unmanned air vehicle (UAV) experiments in cooperation with the U.S. Navy. This multi-domain collaborative research has been focused on extending unmanned vehicles into useful partners in maritime security utilizing intelligent onboard behaviors, collaborative control, and efficient human-system interfacing in situations involving narrow and medium communications areas. This experimentation based research has provided the ability to command unmanned assets from various user locations and transition control between distant users. This has been combined with advanced onboard autonomy allowing high level complex missions to be executed without human intervention. The Unmanned Surface Vehicles (USVs) started their life as commercial boats, and were quickly adapted for autonomous operations based on similar approaches that were used in the DARPA Urban Challenge event. The USVs are capable of adapting missions, reacting to obstacles in their environment, and providing video-based imagery of objects in the environment. The USVs are capable of carrying out their missions with or without communications from the command center. This paper is a lessons learned on the components of the collaborative unmanned surface operations. (back to schedule)
Path Following, Coordinated and Cooperative Control of Marine robots. This talk addresses path following, coordinated and cooperative control of marine robots. We discuss path following versus trajectory tracking and manoeuvring, and coordinated versus cooperative control as means for synchronization control. We particularly focus on control of underactuated marine robots, and the specific challenges the underactuation presents both when it comes to path following and formation control. We also address the problem of underactuated control of marine robots in the presence of environmental disturbances like currents. (back to schedule)
Adaptive Sampling, Models and Optimization for Aquatic Observation. We will describe an adaptive sampling algorithm for a robotic sensor network to estimate a scalar field (e.g., the spatial concentration of a particular nutrient in the water). The sensor network consists of static nodes and a mobile robot (a surface or underwater vehicle). The static nodes are able to make sensor measurements continuously in place, while the vehicle is able to move and make measurements at multiple locations. The measurements from the vehicle and the static nodes are used to reconstruct the underlying scalar field. The algorithm accepts the measurements made by the static nodes as inputs and produces an approximate solution to the following problem: With the constraint that the vehicle has limited energy, what path should it take so that the integrated mean square error of the reconstructed field is minimized? Our approach is to treat this problem in two parts. In the first part we determine the appropriate sampling bandwidth (i.e. how many additional samples should the vehicle take to reduce the error below a given threshold?). In the second part, we show how the order of sample collection influences the reconstruction and give a technique for optimal sample ordering. We will discuss the results of using this algorithm on 1. A system consisting of an autonomous surface vehicle and a set of static buoys operating in a lake over several km of traversed distance while reconstructing the temperature field of the lake surface; 2. A cable-driven mobile robot used to measure the fluorescence field in a vertical transect of the same lake. We will also discuss the applicability of the algorithm to a setting where cooperating multiple vehicles are available. We will conclude with an outline of ongoing work in our lab, which incorporates prior models, and examines the effects of communication uncertainty on the system. (back to schedule)
Cooperative exploration algorithms for underwater robotic sensor networks Ocean features below the surface are typically time varying and difficult to locate. We develop algorithms that combine measurements from multiple autonomous underwater vehicles to localize a class of ocean features. Vehicle movements are coordinated to track the features for in-situ observation. We design a cooperative Kalman filter to reduce measurement noise and spatial variations in sensor data to achieve results suitable for data assimilation. The motion coordination algorithm is able to take advantage of the nowcast or forecast made by ocean models to effectively navigate the vehicles. Simulation results are presented to demonstrate the effectiveness of our method in revealing structures of underwater temperature or salinity fields. (back to schedule)