Implementation of a Cooperative Navigation Algorithm on a Platoon of Autonomous Underwater Vehicles

Maczka, Darren K.; Gadre, A.S.; Stilwell, Daniel J.

doi:10.1109/oceans.2007.4449404

Cited by 37 publications

(34 citation statements)

References 7 publications

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“…A similar arrangement has also been termed the Egocentric Extended Kalman Filter (EEKF) which assumes independence among the vehicles ranging by neglecting the cross covariance terms. 25 Motivated by these prior results, the algorithmic simplicity, and low acoustic bandwidth necessary, we use a similar method during the transmitting of beacon state uncertainties. Here, the vehicles covariance v is augmented with the beacon's covariance b to form the augmented covariance matrix aug to process the range measurement:…”

Section: Range Measurement and Augmentationmentioning

confidence: 99%

“…Approximations to this approach have been presented which compress prior measurements 24 or which avoid transmitting the correlation terms of the covariance matrix. 25 Despite the aforementioned advantages, successful postprocessing of experimental results using OWTT range measurements are rare. Salient examples include centralized 15 and decentralized 26 filters, approximate filters for bandwidth limited communications, 25 cooperative navigation with kayaks 23 and cooperative ranging and bathymetric navigation.…”

Section: Introductionmentioning

confidence: 99%

“…25 Despite the aforementioned advantages, successful postprocessing of experimental results using OWTT range measurements are rare. Salient examples include centralized 15 and decentralized 26 filters, approximate filters for bandwidth limited communications, 25 cooperative navigation with kayaks 23 and cooperative ranging and bathymetric navigation. 27 Rarer are results that incorporate odometry from non-DVL sources such as the recent work with underwater glider tracking over short distances 21 and long distances under ice.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Closed‐loop one‐way‐travel‐time navigation using low‐grade odometry for autonomous underwater vehicles

Claus

Kepper

Suman

et al. 2017

Journal of Field Robotics

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This paper extends the progress of single beacon one-way-travel-time (OWTT) range measurements for constraining XY position for autonomous underwater vehicles (AUV). Traditional navigation algorithms have used OWTT measurements to constrain an inertial navigation system aided by a Doppler Velocity Log (DVL). These methodologies limit AUV applications to where DVL bottom-lock is available as well as the necessity for expensive strap-down sensors, such as the DVL. Thus, deep water, mid-water column research has mostly been left untouched, and vehicles that need expensive strap-down sensors restrict the possibility of using multiple AUVs to explore a certain area. This work presents a solution for accurate navigation and localization using a vehicle's odometry determined by its dynamic model velocity and constrained by OWTT range measurements from a topside source beacon as well as other AUVs operating in proximity. We present a comparison of two navigation algorithms: an Extended Kalman Filter (EKF) and a Particle Filter(PF). Both of these algorithms also incorporate a water velocity bias estimator that further enhances the navigation accuracy and localization. Closed-loop online field results on local waters as well as a real-time implementation of two days field trials operating in Monterey Bay, California during the Keck Institute for Space Studies oceanographic research project prove the accuracy of this methodology with a root mean square error on the order of tens of meters compared to GPS position over a distance traveled of multiple kilometers.

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Section: Range Measurement and Augmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Closed‐loop one‐way‐travel‐time navigation using low‐grade odometry for autonomous underwater vehicles

Claus

Kepper

Suman

et al. 2017

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…Techy et al [106] implemented a strategy for coordination based on speed modulation to synchronize two autonomous UAVs for tracking long-distance movement of plant pathogens above crop fields. Maczka et al [107] demonstrated an efficient method for cooperative navigation of underwater vehicles from time-synchronized acoustic data transmissions. Hollinger et al [108] demonstrated on a single AUV in the Southern California Bight a probabilistic planner that uses uncertainty in ocean current prediction based on an interpolation variance.…”

Section: Recent Developments and Future Directionsmentioning

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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“…In ref. [22], the egocentric KF is proposed where the state error correlation 1 , measured by the covariance, is ignored. As a result, the element types for exchanging are reduced.…”

Section: Introductionmentioning

confidence: 99%

A Small-Sized Locating Message for Cooperative Localization under Communication Constraint

Wang

Zhang

Liang

2016

International Journal of Advanced Robotic Systems

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When one agent simultaneously measures multiple teammates in cooperative localization, how to share these relative measurements with low-cost communication is interesting. In this chapter, different communication requirements for sharing absolute measurement and relative measurement are analyzed under extended Kalman filtering, whereby we develop an algorithm to convert multiple relative measurements into one virtual absolute measurement. By this, less number of elements are required to be transmitted across the agents. The utilization of multi-relative measurements is changed to the mode of one round communication from conventional mode where the measurements are processed and broadcast one by one. Simulation results demonstrate that, using the virtual absolute measurement, the almost equal localization performance with real relative measurements is obtained.

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Implementation of a Cooperative Navigation Algorithm on a Platoon of Autonomous Underwater Vehicles

Cited by 37 publications

References 7 publications

Closed‐loop one‐way‐travel‐time navigation using low‐grade odometry for autonomous underwater vehicles

Closed‐loop one‐way‐travel‐time navigation using low‐grade odometry for autonomous underwater vehicles

Cooperative Vehicle Environmental Monitoring

A Small-Sized Locating Message for Cooperative Localization under Communication Constraint

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