Oceanographic pursuit: Networked control of multiple vehicles tracking dynamic ocean features

Reed, Brooks L.; Hover, Franz S.

doi:10.1016/j.mio.2014.05.001

Cited by 16 publications

(3 citation statements)

References 51 publications

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“…Multi‐AUV coordination or collaboration was used in investigating tidal fronts (Bellingham, ), ocean fields around a coastal upwelling center (Fiorelli et al., ; Leonard et al., ), internal waves (Petillo & Schmidt, ), and joint estimation and pursuit of dynamic ocean features (Reed & Hover, ). In the context of this paper, we suggest a simple scheme of using multiple AUVs to improve the temporal resolution of front tracking.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Autonomous Four‐Dimensional Mapping and Tracking of a Coastal Upwelling Front by an Autonomous Underwater Vehicle

et al. 2015

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Coastal upwelling is a wind-driven ocean process that brings cooler, saltier, and nutrient-rich deep water upward to the surface. The boundary between the upwelling water and the normally stratified water is called the "upwelling front." Upwelling fronts support enriched phytoplankton and zooplankton populations, thus they have great influences on ocean ecosystems. Traditional ship-based methods for detecting and sampling ocean fronts are often laborious and very difficult, and long-term tracking of such dynamic features is practically impossible. In our prior work, we developed a method of using an autonomous underwater vehicle (AUV) to autonomously detect an upwelling front and track the front's movement on a fixed latitude, and we applied the method in scientific experiments. In this paper, we present an extension of the method. Each time the AUV crosses and detects the front, the vehicle makes a turn at an oblique angle to recross the front, thus zigzagging through the front to map the frontal zone. The AUV's zigzag tracks alternate in northward and southward sweeps, so as to track the front as it moves over time. This way, the AUV maps and tracks the front in four dimensions-vertical, cross-front, along-front, and time.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Autonomous Four‐Dimensional Mapping and Tracking of a Coastal Upwelling Front by an Autonomous Underwater Vehicle

et al. 2015

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“…Simulations using three model datasets demonstrate the proof-of-concept. This chapter is based on work published in [196] and [197].…”

Section: Summary Of Contributionsmentioning

confidence: 99%

Controller design for underwater vehicle systems with communication constraints

Reed¹

2015

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Real-time cooperation between autonomous vehicles can enable time-critical missions such as tracking and pursuit of a dynamic target or environmental feature, but relies on wireless communications. Underwater, communication over distances beyond about one hundred meters is almost exclusively accomplished through acoustics, which bring challenges such as propagation delays, low data rates, packet loss, and scheduling constraints due to interference and limited bandwidth. These limitations make underwater pursuit missions preeminent applications of networked control. Motivated by such applications, this thesis presents contributions towards multi-vehicle feedback control in the presence of severe communication constraints.The first major area of work considers the formulation and solution of new underwater multi-vehicle tracking and pursuit problems using closed-loop control. We begin with a centralized robust optimization approach for multicast routing and power control which is suitable for integration with vehicle control. Next, we describe field experiments in range-based target pursuit at high tracking bandwidths in a challenging shallow-water environment. Finally, we present a methodology for pursuit of dynamic ocean features such as fronts, which we validate using hindcast ocean model data. The primary innovation is a projection algorithm which carries out linearization of ocean model forecast dynamics and uncertainty directly in vehicle coordinates via a forward model technique. The resulting coupled linear stochastic system is suitable for networked control.The second area of work presents a unified formalism for multi-vehicle control and estimation with measurement, control, and acknowledgment packets all subject to scheduling, delays and packet loss. The modular framework we develop is built around a jump linear system description incorporating receding horizon optimization and buffering at actuators. Integration of these elements enables synthesis of a novel technique for estimation using delayed and lossy control acknowledgments-a desirable and practical capability of fielded systems that has not been considered to date.Simulations and field experiments demonstrate the effectiveness of our approach.

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“…There have also been some proposals for the use of cooperating platforms to collectively sample the environment [14], but with few implementations in the field, probably due to practical difficulties. Recently, the possibility of using adaptive sampling with multiple AUVs has regained attention, as a means to improve even further the efficiency in the sampling process, such as the algorithms proposed in [15] and [16] to track plumes and other dynamic ocean features.…”

Section: Introductionmentioning

confidence: 99%

Autonomous tracking of a horizontal boundary

Cruz¹,

Matos²

2014

2014 Oceans - St. John's

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The ability to employ autonomous vehicles to find and track the boundary between two different water masses can increase the efficiency in waterborne data collection, by concentrating measurements in the most relevant regions and capturing detailed spacial and temporal variations. In this paper we provide a guidance mechanism to enable an autonomous vehicle to find and track the steepest gradient of a scalar field in the horizontal plane. The main innovation in our approach is the mechanism to adapt the orientation of the crossings to the local curvature of the boundary, so that the vehicle can keep tracking the gradient regardless of its horizontal orientation. As an example, we show how the algorithms can be used to find and track the boundary of a dredged navigation channel, using only altimeter measurements.

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Oceanographic pursuit: Networked control of multiple vehicles tracking dynamic ocean features

Cited by 16 publications

References 51 publications

Autonomous Four‐Dimensional Mapping and Tracking of a Coastal Upwelling Front by an Autonomous Underwater Vehicle

Autonomous Four‐Dimensional Mapping and Tracking of a Coastal Upwelling Front by an Autonomous Underwater Vehicle

Controller design for underwater vehicle systems with communication constraints

Autonomous tracking of a horizontal boundary

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