A future for intelligent autonomous ocean observing systems

Ocean scientists have dreamed of and recently started to realize an ocean observing revolution with autonomous observing platforms and sensors. Critical questions to be answered by such autonomous systems are where, when, and what to sample for optimal information, and how to optimally reach the sampling locations. Definitions, concepts, and progress towards answering these questions using quantitative predictions and fundamental principles are presented. Results in reachability and path planning, adaptive sampling, machine learning, and teaming machines with scientists are overviewed. The integrated use of differential equations and theory from varied disciplines is emphasized. The results provide an inference engine and knowledge base for expert autonomous observing systems. They are showcased using a set of recent at-sea campaigns and realistic simulations. Real-time experiments with identical autonomous underwater vehicles (AUVs) in the Buzzards Bay and Vineyard Sound region first show that our predicted time-optimal paths were faster than shortest distance paths. Deterministic and probabilistic reachability and path forecasts issued and validated for gliders and floats in the northern Arabian Sea are then presented. Novel Bayesian adaptive sampling for hypothesis testing and optimal learning are finally shown to forecast the observations most informative to estimate the accuracy of model formulations, the values of ecosystem parameters and dynamic fields, and the presence of Lagrangian Coherent Structures

Autonomous and Adaptive Underwater Plume Detection and Tracking with AUVs: Concepts, Methods, and Available Technology

An autonomous underwater vehicle (AUV) equipped with environmental sensors and an on-board autonomy system can greatly increase the efficiency of environmental data collection and the synopticity of the data set collected simply by autonomously adapting its motion to changes it senses in its local environment. One application of this is tracking ocean features in an unknown ocean environment. This can be accomplished with one or multiple AUVs collaborating in near-real-time using acoustic communications. To further explore one example of this application, this paper focuses on using multiple AUVs to track underwater plumes. We evaluate various types of plumes (e.g., hydrothermal vent plumes, algal blooms, oil leaks), how each plume type may be detected and its spatial extent determined, what types of sensors can be used, and how AUVs can be employed to autonomously and adaptively track these dynamic plumes. Since AUVs vary significantly in design, mobility, deployment duration, on-board processing power, etc., it is also necessary to consider the best choice of AUV (or combination of AUVs) to track a plume. Thus, an operator/scientist's choice of AUV type(s) will likely depend the type of plume to be tracked, or vice versa. Since most underwater plumes are highly spatiotemporally dynamic, employing environmentally adaptive autonomy to track them with a fleet of AUVs is one of the most efficient ways to do so, given today's technology. Keywords: Autonomous vehicles; Adaptive systems; Marine systems; Sampling systems; Tracking applications; Marine environmental sampling; Underwater plume

Path planning with spatiotemporal optimal stopping for stochastic mission monitoring

© 2017 IEEE. We consider an optimal stopping formulation of the mission monitoring problem, in which a monitor vehicle must remain in close proximity to an autonomous robot that stochastically follows a predicted trajectory. This problem arises in a diverse range of scenarios, such as autonomous underwater vehicles supervised by surface vessels, pedestrians monitored by aerial vehicles, and animals monitored by agricultural robots. The key problem characteristics we consider are that the monitor must remain stationary while observing the robot, robot motion is modeled in general as a stochastic process, and observations are modeled as a spatial probability distribution. We propose a resolution-complete algorithm that runs in a polynomial time. The algorithm is based on a sweep-plane approach and generates a motion plan that maximizes the expected observation time and value. A variety of stochastic models may be used to represent the robot trajectory. We present results with data drawn from real AUV missions, a real pedestrian trajectory dataset and Monte Carlo simulations. Our results demonstrate the performance and behavior of our algorithm, and relevance to a variety of applications

Constructing a distributed AUV network for underwater plume-tracking operations

© The Author(s), 2012. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in International Journal of Distributed Sensor Networks 2012 (2012): 191235, doi:10.1155/2012/191235.In recent years, there has been significant concern about the impacts of offshore oil spill plumes and harmful algal blooms on the coastal ocean environment and biology, as well as on the human populations adjacent to these coastal regions. Thus, it has become increasingly important to determine the 3D extent of these ocean features (“plumes”) and how they evolve over time. The ocean environment is largely inaccessible to sensing directly by humans, motivating the need for robots to intelligently sense the ocean for us. In this paper, we propose the use of an autonomous underwater vehicle (AUV) network to track and predict plume shape and motion, discussing solutions to the challenges of spatiotemporal data aliasing (coverage versus resolution), underwater communication, AUV autonomy, data fusion, and coordination of multiple AUVs. A plume simulation is also developed here as the first step toward implementing behaviors for autonomous, adaptive plume tracking with AUVs, modeling a plume as a sum of Fourier orders and examining the resulting errors. This is then extended to include plume forecasting based on time variations, and future improvements and implementation are discussed.This research was made with Government support under and awarded by DoD, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice

Arctic Domain Awareness Center DHS Center of Excellence (COE): Project Work Plan

As stated by the DHS Science &Technology Directorate, “The increased and diversified use of maritime spaces in the Arctic - including oil and gas exploration, commercial activities, mineral speculation, and recreational activities (tourism) - is generating new challenges and risks for the U.S. Coast Guard and other DHS maritime missions.” Therefore, DHS will look towards the new ADAC for research to identify better ways to create transparency in the maritime domain along coastal regions and inland waterways, while integrating information and intelligence among stakeholders. DHS expects the ADAC to develop new ideas to address these challenges, provide a scientific basis, and develop new approaches for U.S. Coast Guard and other DHS maritime missions. ADAC will also contribute towards the education of both university students and mid-career professionals engaged in maritime security. The US is an Arctic nation, and the Arctic environment is dynamic. We have less multi-year ice and more open water during the summer causing coastal villages to experience unprecedented storm surges and coastal erosion. Decreasing sea ice is also driving expanded oil exploration, bringing risks of oil spills. Tourism is growing rapidly, and our fishing fleet and commercial shipping activities are increasing as well. There continues to be anticipation of an economic pressure to open up a robust northwest passage for commercial shipping. To add to the stresses of these changes is the fact that these many varied activities are spread over an immense area with little connecting infrastructure. The related maritime security issues are many, and solutions demand increasing maritime situational awareness and improved crisis response capabilities, which are the focuses of our Work Plan. UAA understands the needs and concerns of the Arctic community. It is situated on Alaska’s Southcentral coast with the port facility through which 90% of goods for Alaska arrive. It is one of nineteen US National Strategic Seaports for the US DOD, and its airport is among the top five in the world for cargo throughput. However, maritime security is a national concern and although our focus is on the Arctic environment, we will expand our scope to include other areas in the Lower 48 states. In particular, we will develop sensor systems, decision support tools, ice and oil spill models that include oil in ice, and educational programs that are applicable to the Arctic as well as to the Great Lakes and Northeast. The planned work as detailed in this document addresses the DHS mission as detailed in the National Strategy for Maritime Security, in particular, the mission to Maximize Domain Awareness (pages 16 and 17.) This COE will produce systems to aid in accomplishing two of the objectives of this mission. They are: 1) Sensor Technology developing sensor packages for airborne, underwater, shore-based, and offshore platforms, and 2) Automated fusion and real-time simulation and modeling systems for decision support and planning. An integral part of our efforts will be to develop new methods for sharing of data between platforms, sensors, people, and communities.United States Department of Homeland SecurityCOE ADAC Objective/Purpose / Methodology / Center Management Team and Partners / Evaluation and Transition Plans / USCG Stakeholder Engagement / Workforce Development Strategy / Individual Work Plan by Projects Within a Theme / Appendix A / Appendix B / Appendix

Efficiently Learning Human Preferences for Robot Autonomy

Human-robot teams are invaluable for mapping unknown environments, exploring difficult-to-reach areas, and manipulating inaccessible equipment. However, guiding autonomous robots requires dealing with these dynamic domains while synthesizing a significant amount of data and balancing competing objectives. Current mission planning methods often involve manually specifying low-level parameters of the mission, such as exact waypoints or control inputs. These methods cannot perfectly cope with the changing surroundings and limited communications that come with operating in these complex conditions. To address this and reduce the burden on human operators, the field has trended towards ever-increasing levels of autonomy. Providing this long-term autonomy requires more usable, robust collaborative mission planning solutions that leverage the strengths of both the robot and the human operator. In this thesis, we propose two novel methods for improving the collaboration of human-robot teams by enabling the robot to learn an operator's preferences for mission planning. These techniques provide the robot with a rich representation of the human's goals while utilizing familiar techniques to speed learning. The first method is trained by making small-scale, iterative improvements to candidate mission plans generated by the robot, similar to the small improvements an operator would make while planning an actual mission. Using a novel coactive learning algorithm, the method learns the operator's preferences from the feature differences between the original and improved mission plans while remaining robust to errors and noise in the operator's corrections. The second proposed method simplifies the queries by asking survey-style rating and ranking questions about candidate plans. These queries are generated by a Gaussian process (GP) active learner that uses the responses to learn the most preferred region of the mission preference space. The ranking query responses provide the GP with general relational information about several points in the preference space, while the rating query responses provide a specific preference about a single point. A custom probit allows the GP to incorporate the different strengths of each query type into a single preference model. Tests in simulated lake monitoring missions show that these methods can efficiently and accurately learn an operator’s preferences. Additionally, a field trial in which an EcoMapper autonomous underwater vehicle monitors the ecology of a lake validates the use of the coactive learning method. These results demonstrate that these techniques can enable a robot to accurately learn a human operator's preferences, then autonomously plan and perform missions that apply those preferences without relying on regular intervention by the operator