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We present a novel stochastic optimization method to compute energy–optimal paths, among all time–optimal paths, for vehicles traveling in dynamic unsteady currents. This approach is based on solving a stochastic level set equation using a dynamically orthogonal decomposition. We first describe the dynamically orthogonal level set equation and introduce the path planning methodology. Next, we apply the method to a steady front crossing test case and validate it using a semi–analytical solution obtained by solving a dual nonlinear energy–time optimization problem. Finally, we apply the method to plan paths for a mission in the Middle Atlantic Bight and New Jersey Shelf/Hudson Canyon region.

The level set methodology for time-optimal path planning is employed to predict collision-free and fastest time trajectories for swarms of underwater vehicles deployed in the Philippine Archipelago region.
To simulate the multiscale ocean flows in this complex region, a data-assimilative primitive-equation ocean modeling system is employed with
telescoping domains that are interconnected by implicit two-way nesting.
These data-driven multiresolution simulations provide a
realistic flow environment, including variable large-scale currents,
strong jets, eddies, wind-driven currents and tides.
The properties and capabilities of the rigorous level set methodology are
illustrated and assessed quantitatively for several vehicle types and mission scenarios.
Feasibility studies of all-to-all broadcast missions, leading to minimal time transmission between source and receiver locations, are performed using a large number of vehicles.
The results with gliders and faster propelled vehicles are compared.
Reachability studies, i.e.~determining the boundaries of regions that can be reached by vehicles for exploratory missions, are then exemplified and analyzed.
Finally, the methodology is used to determine the optimal strategies
for fastest time pick-up of deployed gliders by means of
underway surface vessels or stationary platforms.
The results highlight the complex effects of multiscale flows on the optimal paths,
the need to utilize the ocean environment for more efficient autonomous
missions and the benefits of including ocean forecasts in the planning of time-optimal paths.

We develop an accurate partial differential equation based methodology that predicts the time-optimal paths of autonomous vehicles navigating in any continuous, strong and dynamic ocean currents, obviating the need for heuristics. The goal is to predict a sequence of steering directions so that vehicles can best utilize or avoid currents to minimize their travel time. Inspired by the level set method, we derive and demonstrate that a modified level set equation governs the time-optimal path in any continuous flow. We show that our algorithm is computationally efficient and apply it to a number of experiments. First, we validate our approach through a simple benchmark application in a Rankine vortex flow for which an analytical solution is available. Next, we apply our methodology to more complex, simulated flow-fields such as unsteady double-gyre flows driven by wind stress and flows behind a circular island. These examples show that time-optimal paths for multiple vehicles can be planned, even in the presence of complex flows in domains with obstacles. Finally, we present, and support through illustrations, several remarks that describe specific features of our methodology.

Lermusiaux P.F.J, T. Lolla, P.J. Haley. Jr., K. Yigit, M.P. Ueckermann, T. Sondergaard and W.G. Leslie, 2014. *Science of Autonomy: Time-Optimal Path Planning and Adaptive Sampling for Swarms of Ocean Vehicles*. Chapter 11, Springer Handbook of Ocean Engineering: Autonomous Ocean Vehicles, Subsystems and Control, Tom Curtin (Ed.). In press.

We develop and illustrate an efficient but rigorous
methodology that predicts the time-optimal paths of ocean
vehicles in dynamic continuous flows. The goal is to best
utilize or avoid currents, without limitation on these currents
nor on the number of vehicles. The methodology employs a
new modified level set equation to evolve a wavefront from
the starting point of vehicles until they reach their desired
goal locations, combining flow advection with nominal vehicle
motions. The optimal paths of vehicles are then computed
by solving particle tracking equations backwards in time.
The computational cost is linear with the number of vehicles
and geometric with spatial dimensions. The methodology is
applicable to any continuous flows and many vehicles scenarios.
Present illustrations consist of the crossing of a canonical
uniform jet and its validation with an optimization problem,
as well as more complex time varying 2D flow fields, including
jets, eddies and forbidden regions.

Wang, D., P.F.J. Lermusiaux, P.J. Haley, D. Eickstedt, W.G. Leslie and H. Schmidt, 2009. *Acoustically Focused Adaptive Sampling and On-board Routing for Marine Rapid Environmental Assessment.* Special issue of Journal of Marine Systems on "Coastal processes: challenges for monitoring and prediction", Drs. J.W. Book, Prof. M. Orlic and Michel Rixen (Guest Eds), 78, S393-S407, doi: 10.1016/j.jmarsys.2009.01.037.

The goal of adaptive sampling in the ocean is to predict
the types and locations of additional ocean measurements that
would be most useful to collect. Quantitatively, what is most useful
is defined by an objective function and the goal is then to optimize
this objective under the constraints of the available observing network.
Examples of objectives are better oceanic understanding, to
improve forecast quality, or to sample regions of high interest. This
work provides a new path-planning scheme for the adaptive sampling
problem. We define the path-planning problem in terms of
an optimization framework and propose a method based on mixed
integer linear programming (MILP). The mathematical goal is to
find the vehicle path that maximizes the line integral of the uncertainty
of field estimates along this path. Sampling this path can improve
the accuracy of the field estimates the most. While achieving
this objective, several constraints must be satisfied and are implemented.
They relate to vehicle motion, intervehicle coordination,
communication, collision avoidance, etc. The MILP formulation is
quite powerful to handle different problem constraints and flexible
enough to allow easy extensions of the problem. The formulation
covers single- and multiple-vehicle cases as well as singleand
multiple-day formulations. The need for a multiple-day formulation
arises when the ocean sampling mission is optimized for
several days ahead. We first introduce the details of the formulation,
then elaborate on the objective function and constraints, and
finally, present a varied set of examples to illustrate the applicability
of the proposed method.

Lermusiaux, P.F.J, 2007. *Adaptive Modeling, Adaptive Data Assimilation and Adaptive Sampling.* Refereed invited manuscript. Special issue on "Mathematical Issues and Challenges in Data Assimilation for Geophysical Systems: Interdisciplinary Perspectives". C.K.R.T. Jones and K. Ide, Eds. Physica D, Vol 230, 172-196, doi:
10.1016/j.physd.2007.02.014.

The problem of how to optimally deploy a suite of sensors to estimate the oceanographic
environment is addressed. An optimal way to estimate (nowcast) and predict (forecast)
the ocean environment is to assimilate measurements from dynamic and uncertain regions
into a dynamical ocean model. In order to determine the sensor deployment strategy
that optimally samples the regions of uncertainty, a Genetic Algorithm (GA) approach
is presented. The scalar cost function is defined as a weighted combination of a sensor
suite’s sampling of the ocean variability, ocean dynamics, transmission loss sensitivity,
modeled temperature uncertainty (and others). The benefit of the GA approach is that the
user can determine “optimal” via a weighting of constituent cost functions, which can
include ocean dynamics, acoustics, cost, time, etc. A numerical example with three gliders,
two powered AUVs, and three moorings is presented to illustrate the optimization
approach in the complex shelfbreak region south of New England.

Variabilities in the coastal ocean environment span
a wide range of spatial and temporal scales. From an acoustic
viewpoint, the limited oceanographic measurements and today’s
ocean modeling capabilities can’t always provide oceanic-acoustic
predictions in sufficient detail and with enough accuracy. Adaptive
Rapid Environmental Assessment (AREA) is a new adaptive sampling
concept being developed in connection with the emergence
of the Autonomous Ocean Sampling Network (AOSN) technology.
By adaptively and optimally deploying in-situ measurement
resources and assimilating these data in coupled nested ocean
and acoustic models, AREA can dramatically improve the ocean
estimation that matters for acoustic predictions and so be
essential for such predictions. These concepts are outlined and
preliminary methods are developed and illustrated based on
the Focused Acoustic Forecasting-05 (FAF05) exercise. During
FAF05, AREA simulations were run in real-time and engineering
tests carried out, within the context of an at-sea experiment
with Autonomous Underwater Vehicles (AUV) in the northern
Tyrrhenian sea, on the eastern side of the Corsican channel.

Yilmaz, N.K., C. Evangelinos, N.M. Patrikalakis, P.F.J. Lermusiaux, P.J. Haley, W.G. Leslie, A.R. Robinson, D. Wang and H. Schmidt, 2006a. *Path Planning Methods for Adaptive Sampling of Environmental and Acoustical Ocean Fields*, Oceans 2006, 6pp, Boston, MA, 18-21 Sept. 2006, doi: 10.1109/OCEANS.2006.306841.