Science of Autonomy: Time-Optimal Path Planning and Adaptive Sampling for Swarms of Ocean Vehicles

The science of autonomy is the systematic development of fundamental knowledge about autonomous decision making and task completing in the form of testable autonomous methods, models and systems. In ocean applications, it involves varied disciplines that are not often connected. However, marine autonomy applications are rapidly growing, both in numbers and in complexity. This new paradigm in ocean science and operations motivates the need to carry out interdisciplinary research in the science of autonomy. This chapter reviews some recent results and research directions in time-optimal path planning and optimal adaptive sampling. The aim is to set a basis for a large number of vehicles forming heterogeneous and collaborative underwater swarms that are smart, i. e., knowledgeable about the predicted environment and their uncertainties, and about the predicted effects of autonomous sensing on future operations. The methodologies are generic and applicable to any swarm that moves and senses dynamic environmental fields. However, our focus is underwater path planning and adaptive sampling with a range of vehicles such as autonomous underwater vehicles (AUV s), gliders, ships or remote sensing platforms.

[1]  Pierre FJ Lermusiaux Evolving the subspace of the three-dimensional multiscale ocean variability: Massachusetts Bay , 2001 .

[2]  Naomi Ehrich Leonard,et al.  Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in Monterey Bay , 2010, J. Field Robotics.

[3]  Pierre F. J. Lermusiaux,et al.  Uncertainty estimation and prediction for interdisciplinary ocean dynamics , 2006, J. Comput. Phys..

[4]  K. Emanuel,et al.  Optimal Sites for Supplementary Weather Observations: Simulation with a Small Model , 1998 .

[5]  Pierre F. J. Lermusiaux,et al.  Dynamically orthogonal field equations for continuous stochastic dynamical systems , 2009 .

[6]  Han-Lim Choi,et al.  Adaptive Observation Strategies for Forecast Error Minimization , 2007, International Conference on Computational Science.

[7]  Naomi Ehrich Leonard,et al.  Preparing to predict: The Second Autonomous Ocean Sampling Network (AOSN-II) experiment in the Monterey Bay , 2009 .

[8]  N.M. Patrikalakis,et al.  Path Planning of Autonomous Underwater Vehicles for Adaptive Sampling Using Mixed Integer Linear Programming , 2008, IEEE Journal of Oceanic Engineering.

[9]  N.M. Patrikalakis,et al.  Path Planning Methods for Adaptive Sampling of Environmental and Acoustical Ocean Fields , 2006, OCEANS 2006.

[10]  Pierre F. J. Lermusiaux,et al.  Quantifying Uncertainties in Ocean Predictions , 2006 .

[11]  H. Stommel The Slocum Mission , 1989 .

[12]  ScienceDirect,et al.  Deep sea research. Part II, Topical studies in oceanography , 1993 .

[13]  Master Gardener,et al.  Mathematical games: the fantastic combinations of john conway's new solitaire game "life , 1970 .

[14]  Pierre F. J. Lermusiaux,et al.  Data Assimilation with Gaussian Mixture Models Using the Dynamically Orthogonal Field Equations. Part II: Applications , 2013 .

[15]  Pierre F. J. Lermusiaux,et al.  Path planning in time dependent flow fields using level set methods , 2012, 2012 IEEE International Conference on Robotics and Automation.

[16]  Pierre F. J. Lermusiaux,et al.  Numerical schemes for dynamically orthogonal equations of stochastic fluid and ocean flows , 2013, J. Comput. Phys..

[17]  Pierre F. J. Lermusiaux,et al.  Progress and Prospects of U.S. Data Assimilation in Ocean Research , 2006 .

[18]  James G. Bellingham,et al.  Progress toward autonomous ocean sampling networks , 2009 .

[19]  Pierre FJ Lermusiaux,et al.  Time-optimal path planning in dynamic flows using level set equations: theory and schemes , 2014, Ocean Dynamics.

[20]  W. G. Leslie,et al.  Spatial and Temporal Variations in Acoustic propagation during the PLUSNet'07 Exercise in Dabob Bay , 2008 .

[21]  T. Sapsis,et al.  Dynamical criteria for the evolution of the stochastic dimensionality in flows with uncertainty , 2012 .

[22]  Pierre F. J. Lermusiaux,et al.  Nonlinear optimization of autonomous undersea vehicle sampling strategies for oceanographic data-assimilation , 2007, J. Field Robotics.

[23]  Pierre F. J. Lermusiaux,et al.  Data Assimilation with Gaussian Mixture Models Using the Dynamically Orthogonal Field Equations. Part I: Theory and Scheme , 2013 .

[24]  Konuralp Yiğit,et al.  Path Planning Methods for Autonomous Underwater Vehicles , 2011 .

[25]  J. Curcio,et al.  Autonomous surface craft provide flexibility to remote adaptive oceanographic sampling and modeling , 2008, OCEANS 2008.

[26]  Naomi Ehrich Leonard,et al.  Cooperative Control for Ocean Sampling: The Glider Coordinated Control System , 2008, IEEE Transactions on Control Systems Technology.

[27]  Pierre F. J. Lermusiaux,et al.  Multiscale Physical and Biological Dynamics in the Philippines Archipelago: Predictions and Processes , 2011 .

[28]  Ding Wang,et al.  Acoustically focused adaptive sampling and on-board routing for marine rapid environmental assessment , 2009 .

[29]  Pierre F. J. Lermusiaux,et al.  Multiscale two-way embedding schemes for free-surface primitive equations in the “Multidisciplinary Simulation, Estimation and Assimilation System” , 2010 .

[30]  Pierre F. J. Lermusiaux,et al.  Path planning in multi-scale ocean flows: Coordination and dynamic obstacles , 2015 .

[31]  Albano,et al.  Critical edge between frozen extinction and chaotic life. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[32]  Naomi Ehrich Leonard,et al.  Collective Motion, Sensor Networks, and Ocean Sampling , 2007, Proceedings of the IEEE.

[33]  Naomi Ehrich Leonard Cooperative Vehicle Environmental Monitoring , 2016 .

[34]  Pierre F. J. Lermusiaux,et al.  Adaptive modeling, adaptive data assimilation and adaptive sampling , 2007 .

[35]  Pierre F. J. Lermusiaux,et al.  Time-optimal path planning in dynamic flows using level set equations: realistic applications , 2014, Ocean Dynamics.