An integrated approach to multiple AUV communications, navigation and docking

Singh, Hanumant; Catipovic, Josko; Eastwood, Robert L.; Freitag, Lee; Henriksen, H.; Hover, Franz S.; Yoerger, D.; Bellingham, James; Moran, B.A.

doi:10.1109/oceans.1996.572458

Cited by 43 publications

(20 citation statements)

References 4 publications

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“…In [28] the objective was to use multiple AUVs as an imaging system. Virtual chains of AUVs were considered by Triantafyllou and Streitlien in [29]; a technology for one vehicle to track another in a chain-like fashion using an Ultra Short BaseLine (USBL) acoustic tracking system was demonstrated by Singh et al [30] in Buzzards Bay off Woods Hole, MA in March 1996. In June 1996, two Odyssey vehicles were used along with an acoustic tomography network for mapping in the Haro Strait region of British Columbia [31].…”

Section: Background and Historymentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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Over the last decade, methodologies for automated cooperative control of robotic vehicles have been designed, deployed and proven to provide efficient, reliable, and sustained monitoring of the uncertain and inhospitable ocean environment. Unprecedented data sets have been collected from deployments of cooperative vehicles in the field, and both real-time and post-deployment analyses have led to new understanding of the environment. This first decade of success in cooperative vehicle environmental monitoring sets the stage for new opportunities and future gain, especially as the development of cooperative control methodologies can continue to leverage ongoing technological and scientific advances in underwater communication and sensing, energy and computational efficiency, vehicle size, speed, maneuverability and cost, and ocean modeling and prediction.Indeed, the demonstrated potential of cooperative vehicle control has led to increased demand for fleets of autonomous underwater vehicles (AUVs) for use in measuring ocean physics, biology, chemistry and geology to improve understanding of natural dynamics and human-influenced changes in the marine environment. Further, methodologies for cooperative control of robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there is much to be learned from developments in these other domains. The recent explosion in research on networks and complex systems, including investigation of mechanisms that explain a "collective intelligence" exhibited by animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.For environmental monitoring to be successful, physical, chemical, and biological variables must be measured across a range of spatial and temporal scales; in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space that is highly uncertain and dynamic. Small spatial and temporal scales associated with the measured variables typically make a stationary sensor array impractical because a very large number of sensors would be needed to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited to such a challenge since mobility can be exploited to dynamically distribute fewer sensors according to the spatial and temporal scales.The underlying principle of cooperative control of vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among the individual sensors to yield a collective behavior that performs better than the sum of the parts. If the vehicles can communicate their state or measure the relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically reconfigurable sensor array with a capability for high performance in environmental monitoring not available at the level of individual...

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Section: Background and Historymentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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“…USBL error is dominated by error in the measured bearing, which, although typically under 1 , leads to increasing xyz localization error with increasing distance [83] [89]. Because they require a ship to follow the vehicle, they can be inconvenient for long term deployments.…”

Section: Challenges In Underwater Navigationmentioning

confidence: 99%

Toward autonomous underwater mapping in partially structured 3D environments

VanMiddlesworth¹

2014

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Motivated by inspection of complex underwater environments, we have developed a system for multi-sensor SLAM utilizing both structured and unstructured environmental features. We present a system for deriving planar constraints from sonar data, and jointly optimizing the vehicle and plane positions as nodes in a factor graph. We also present a system for outlier rejection and smoothing of 3D sonar data, and for generating loop closure constraints based on the alignment of smoothed submaps. Our factor graph SLAM backend combines loop closure constraints from sonar data with detections of visual fiducial markers from camera imagery, and produces an online estimate of the full vehicle trajectory and landmark positions. We evaluate our technique on an inspection of a decomissioned aircraft carrier, as well as synthetic data and controlled indoor experiments, demonstrating improved trajectory estimates and reduced reprojection error in the final 3D map. My research supervisor, John Leonard, has provided invaluable advice, mentoring, and access to an overwhelming array of research projects and other opportunities. He would be the village wise man or chief, in constant contact with everyone, always available for advice both personal and professional, identifying problems to be overcome in our hunting and gathering activities, and coordinating trade with nearby villages to ensure we had adequate supplies for the winter.To my advisor Dana Yoerger, whose contagious energy sparked a passion for marine robotics, whose battle-tested wisdom guided me at land and at sea, I owe my entire career at MIT. I see him as the village shaman, dispensing sage advice while magically solving any problem with his beyond-mortal mastery of arcane technical systems.Michael Kaess has been absolutely essential to my academic progress, and I imagine him as the village's technical wizard. He would be master of emerging technologies like metal tools and mechanical advantage, and would share his knowledge generously to ensure the success of the entire community.Franz Hover, the chief of the village next door, has provided both long-term opportunities and day-to-day mentoring and advice. I am immensely grateful for his help, and have very much enjoyed our collaboration on both research and operations.My family is always close to my heart, and their support would be crucial to surviving the harsh alpine environment (although the closeness might be su↵ocating if we were holed up in a tiny yurt made of animal skins all winter, with only a sooty fire for warmth and the village passtime of pebble-tossing for entertainment). My mother Diane would certainly stay up all night helping me study the various edible roots and practice animal-skinning technique for my rite of passage into manhood, a 5 twelve-day journey into the icy mountains with nothing but a flint knife and alpacaskin blanket. Over meals of small game, my father Rex and I would discuss the finer points of glacier navigation, and occasionally get into heated debates about village politi...

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“…Consequently, it will be shown that the two-hydrophone sensor is valuable for certain formation geometries, but leads to poor performance in others. This fact is useful for choosing appropriate formation geometries, and may also explain why variations in formation are observed in leader-follower type experiments [9,10].…”

Section: Introductionmentioning

confidence: 97%

Two-Hydrophone Heading and Range Sensor Applied to Formation-Flying for AUVs

Reeder¹,

Odell²,

Okamoto³

et al. 2004

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-One form of cooperative behavior for a group of AUVs is to fly in formation while performing tasks. A necessary component for formation-flying is that the vehicles must sense their relative positions. Assuming that each vehicle is capable of sensing its inertial position using an acoustic long-baseline ranging system, the relative vehicle position can be determined by exchanging this data. The penalty for this approach is that exchanging inertial position data consumes communications bandwidth. Alternately, relative position may be obtained by intercepting acoustic ranging signals used to determine inertial position, obviating the need for exchange of position data. We explore the use of a two-hydrophone sensor to measure relative heading of two vehicles in a formation. It is assumed that a broad-band navigation signal emanating from one vehicle is intercepted by another vehicle containing the sensor. Relative heading is extracted from the time delay between the two hydrophones.Cross-correlation is used to determine time delay. A model is proposed that predicts stochastic precision and bias for the sensor. For a fixed ranging waveform, precision and bias are dependent upon signal-to-noise ratio, relative range and relative heading. This dependence means that the sensor will be most useful for certain combinations of range and heading. Measurements were performed to determine the precision of the two-hydrophone arrangement as a relative heading sensor. Simulations were used to explore the performance of formation-flying controllers that employ the two-hydrophone sensor.The controller used a saturating linear output feedback control law to simultaneously follow inertial waypoints and maintained formation. The simulations showed that this controller would be able to use relative heading provided by the two-hydrophone sensor to maintain formations in which the vehicles are approximately abreast.

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An integrated approach to multiple AUV communications, navigation and docking

Cited by 43 publications

References 4 publications

Cooperative Vehicle Environmental Monitoring

Cooperative Vehicle Environmental Monitoring

Toward autonomous underwater mapping in partially structured 3D environments

Two-Hydrophone Heading and Range Sensor Applied to Formation-Flying for AUVs

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