Supervision and control of Autonomous underwater vehicles (AUVs) has traditionally been focused on an operator determining a priori the sequence of waypoints of a single vehicle for a mission. As AUVs become more ubiquitous as a scientific tool, we envision the need for controlling multiple vehicles which would impose less cognitive burden on the operator with a more abstract form of human-in-the-loop control. Such mixed-initiative methods in goal-oriented commanding are new for the oceanographic domain and we describe the motivations and preliminary experiments with multiple vehicles operating simultaneously in the water, using a shore-based automated planner.

Obtaining synoptic observations of dynamic ocean phenomena such as fronts, eddies, oxygen minimum zones and blooms has been challenging primarily due to the large spatial scales involved. Traditional methods of observation with manned ships are expensive and, unless the vessel can survey at high-speed, unrealistic. Autonomous underwater vehicles (AUVs) are robotic platforms that have been making steady gains in sampling capabilities and impacting oceanographic observations especially in coastal areas. However, their reach is still limited by operating constraints related to their energy sources. Unmanned aerial vehicles (UAVs) recently introduced in coastal and polar oceanographic experiments have added to the mix in observation strategy and methods. They offer a tantalizing opportunity to bridge such scales in operational oceanography by coordinating with AUVs in the water-column to get in-situ measurements. In this paper, we articulate the principal challenges in operating UAVs with AUVs making synoptic observations for such targeted water-column sampling. We do so in the context of autonomous control and operation for networked robotics and describe novel experiments while articulating the key challenges and lessons learned.

Marine phenomena such as algal blooms can be detected using in situ measurements onboard autonomous underwater vehicles (AUVs), but understanding plankton ecology and community structure requires retrieval and analysis of water specimens. This process requires shipboard or manual sample collection, followed by onshore lab analysis which is time-consuming. Better understanding of the relationship between the observable environmental features and organism abundance would allow more precisely targeted sampling and thereby save time. In this work, we present an approach to learn and improve models that predict this relationship. Coupled with recent advances in AUV technology allowing selective retrieval of water samples, this constitutes a new paradigm in biological sampling. We use organism abundance models along with spatial models of environmental features learned immediately after AUV deployments to compute spatial distributions of organisms in the coastal ocean purely from in situ AUV data. We use Gaussian process regression along with the unscented transform to fuse the two models, obtaining both the mean and variance of the organism abundance estimates. The uncertainty in organism abundance predictions is used in a sampling strategy to selectively acquire new water specimens that improves the organism abundance models. Simulation results are presented demonstrating the advantage of performing hierarchical probabilistic regression. After the validation through simulation, we show predictions of organism abundance from models learned on lab-analyzed water sample data, and AUV survey data.

Fronts have been recognized as hotspots of intense biological activity and are important targets for observation to understand coastal ecology and transport in a changing ocean. With high spatial and temporal variability, detection and event response for frontal zones is challenging for robotic platforms like autonomous underwater vehicles (AUVs). These vehicles have shown their versatility and cost-effectiveness in using automated approaches to detect a range of features. Targeting them for in-situ observation and sampling capabilities for frontal zones then provides an important tool for characterizing rapid and episodic changes. We introduce a novel momentum-based front detection (MBFD) algorithm which utilizes a Kalman filter and a momentum accumulator function to identify significant temperature gradients associated with upwelling fronts. MBFD is designed to work at a number of levels including onboard an AUV, on-shore with a sparse real-time data stream and post-experiment on a full resolution data set gathered by a vehicle. Such a multi-layered approach plays an important role in mixed human-robot decision making for oceanographers making coordinated sampling and asset allocation strategies in large multi-robot field experiments in the coastal ocean.

With the advent of Autonomous Underwater Vehicles (AUVs) and other mobile platforms, marine robotics have had substantial impact on the oceanographic sciences. These systems have allowed scientists to collect data over temporal and spatial scales that would be logistically impossible or prohibitively expensive using traditional ship-based measurement techniques. Increased dependence of scientists on such robots has permeated scientific data gathering with future field campaigns involving these platforms as well as on entire infrastructure of people, processes and software, on shore and at sea. Recent field experiments carried out with a number of surface and underwater platforms give clues to how these technologies are coalescing and need to work together. We highlight one such confluence and describe a future trajectory of needs and desires for field experiments with autonomous marine robotic platforms. Our 2010 inter-disciplinary experiment in the Monterey Bay involved multiple platforms and collaborators with diverse science goals. One important goal was to enable situational awareness, planning and collaboration before, during and after this large-scale collaborative exercise. We present the overall view of the experiment and describe an important shore-side component, the Oceanographic Decision Support System (ODSS), its impact and future directions leveraging such technologies for field experiments.

This paper presents an oceanographic toolchain that can be used to generate multi-vehicle robotic surveys for large-scale dynamic features in the coastal ocean. Our science application targets Harmful Algal Blooms (HABs) which have significant societal impact to coastal communities yet are poorly understood ecologically. Bloom patches can be large spatially (in kms) and unpredictable in their extent. To understand their ecology, we need to be able to bring back water samples from the `right' places and times for lab analysis. In doing so, we target hotspots representative of intense biogeochemical activity for such sampling. Our approach uses remote sensing data to detect such hotspots using ocean color as a proxy, and advectively projects these patches spatio-temporally using surface current data from HF Radar stations. Experiments with satellite and Radar data sets are promising for large, coherent blooms. We show how these predictions can be used to select an appropriate sampling trajectory for an AUV.

Autonomous Underwater Vehicles (AUVs) are an increasingly important tool for oceanographic research demonstrating their capabilities to sample the water column in depths far beyond what humans are capable of visiting, and doing so routinely and cost-effectively. However, control of these platforms to date has relied on fixed sequences for execution of pre-planned actions limiting their effectiveness for measuring dynamic and episodic ocean phenomenon. In this paper we present an agent architecture developed to overcome this limitation through on-board planning using Constraint- based Reasoning. Preliminary versions of the architecture have been integrated and tested in simulation and at sea.

This paper presents a new approach to integrate real-time execution control in autonomous systems and how such an approach integrates in their software architecture. The use of decisional autonomy is becoming more widely accepted as a solution to the increasing need to deploy complex systems (robots, satellites, etc.) able to perform nontrivial tasks in various environments. We present an overview of the organization of such systems. Then we explain why the increasing complexity of functional components as well as the presence of autonomy components becomes an obstacle to system safety and dependability. To address this issue, we propose the integration of an execution control component in the software architecture. This component is synthesized from a model of the acceptable and dangerous state using model-checking techniques. The execution controller has a generic representation of system behavior and, according to some specified system constraints, acts as a "safety bag" allowing acceptable states and avoiding forbidden ones. The controller uses an OBDD like data structure which offers a bounded execution time, and which can be formally validated offline to check temporal properties. Real experimentations have been made on our autonomous mobile robots, and have confirmed it can catch in real-time design errors from the decisional components which would have lead to disastrous consequences.

This paper presents some recent developments of the LAAS architecture for autonomous mobile robots. In particular, we specify the role of the execution control level of this architecture. This level has a fault protection role with respect to the commands issued by the decisional level, which are transmitted to the real system (through the functional level). We introduce a new approach and a new tool inspired from the model checking domain. We present a new language to specify the model of acceptable and required states of the system (valid contexts for requests to functional module and resources usage). This language is compiled in an ordered binary decision diagram like structure which is then used online to check the specified constraints in real-time. Such model checking approach could be extended to check off line more complex temporal properties of the system.