Behavior-based planning and prosecution architecture for Autonomous Underwater Vehicles in Ocean Observatories

This paper discusses the autonomy framework proposed for the mobile instruments such as Autonomous Underwater Vehicles (AUVs) and gliders. Paper focuses on the challenges faced by these clusters of mobile platform in executive tasks such as adaptive sampling in the hostile underwater environment. Collaborations between these mobile instruments are essential to capture the environmental changes and track them for time-series analysis. This paper looks into the challenges imposed by the underwater communication infrastructure and presents the nested autonomy architecture as a solution to overcome these challenges. The autonomy architecture is separated from the low-level control architecture of these instruments, which is called the `backseat driver'. The back-seat driver paradigm is implemented on the Mission Oriented Object Suite (MOOS) developed at MIT. The autonomy is achieved by generating multiple behaviors (multiple objective functions) linked to the internal state of the platform as well as the environment. Optimization engine called the MOOS-IvP is used to pick the best action for the given instance based on the mission at hand. At sea operational scenarios and results are presented to demonstrate the proposed autonomy architecture for Ocean Observatory Initiative (OOI). Keywords: Autonomous Underwater Vehicles (AUVs), Underwater Gliders, MOOS, MOOS DB, MOOS-IvP, OOI-CI, behavior-based autonom

Exploiting Adaptive and Collaborative AUV Autonomy for Detection and Characterization of Internal Waves

Advances in the fields of autonomy software and environmental sampling techniques for autonomous underwater vehicles (AUVs) have recently allowed for the merging of oceanographic data collection with the testing of emerging marine technology. The Massachusetts Institute of Technology (MIT) Laboratory for Autonomous Marine Sensing Systems (LAMSS) group conducted an Internal Wave Detection Experiment in August 2010 with these advances in mind. The goal was to have multiple AUVs collaborate autonomously through onboard autonomy software and real-time underwater acoustic communication to monitor for the presence of internal waves by adapting to changes in the environment (specifically the temperature variations near the thermocline/pycnocline depth). The experimental setup, implementation, data, deployment results, and internal wave detection and quantification results are presented in this paper.United States. Office of Naval Research (Grant N00014-08-1-0013)United States. Dept. of DefenseUnited States. Air Force Office of Scientific ResearchAmerican Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship (32 CFR 168a

Interoperability Among Unmanned Maritime Vehicles: Review and First In-field Experimentation

Complex maritime missions, both above and below the surface, have traditionally been carried out by manned surface ships and submarines equipped with advanced sensor systems. Unmanned Maritime Vehicles (UMVs) are increasingly demonstrating their potential for improving existing naval capabilities due to their rapid deployability, easy scalability, and high reconfigurability, offering a reduction in both operational time and cost. In addition, they mitigate the risk to personnel by leaving the man far-from-the-risk but in-the-loop of decision making. In the long-term, a clear interoperability framework between unmanned systems, human operators, and legacy platforms will be crucial for effective joint operations planning and execution. However, the present multi-vendor multi-protocol solutions in multi-domain UMVs activities are hard to interoperate without common mission control interfaces and communication protocol schemes. Furthermore, the underwater domain presents significant challenges that cannot be satisfied with the solutions developed for terrestrial networks. In this paper, the interoperability topic is discussed blending a review of the technological growth from 2000 onwards with recent authors' in-field experience; finally, important research directions for the future are given. Within the broad framework of interoperability in general, the paper focuses on the aspect of interoperability among UMVs not neglecting the role of the human operator in the loop. The picture emerging from the review demonstrates that interoperability is currently receiving a high level of attention with a great and diverse deal of effort. Besides, the manuscript describes the experience from a sea trial exercise, where interoperability has been demonstrated by integrating heterogeneous autonomous UMVs into the NATO Centre for Maritime Research and Experimentation (CMRE) network, using different robotic middlewares and acoustic modem technologies to implement a multistatic active sonar system. A perspective for the interoperability in marine robotics missions emerges in the paper, through a discussion of current capabilities, in-field experience and future advanced technologies unique to UMVs. Nonetheless, their application spread is slowed down by the lack of human confidence. In fact, an interoperable system-of-systems of autonomous UMVs will require operators involved only at a supervisory level. As trust develops, endorsed by stable and mature interoperability, human monitoring will be diminished to exploit the tremendous potential of fully autonomous UMVs

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

Adaptive sampling in autonomous marine sensor networks

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2006In this thesis, an innovative architecture for real-time adaptive and cooperative control of autonomous sensor platforms in a marine sensor network is described in the context of the autonomous oceanographic network scenario. This architecture has three major components, an intelligent, logical sensor that provides high-level environmental state information to a behavior-based autonomous vehicle control system, a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control in complex environments with multiple constraints, and an approach to cooperative robotics that is a hybrid between the swarm cooperation and intentional cooperation approaches. The mobility of the sensor platforms is a key advantage of this strategy, allowing dynamic optimization of the sensor locations with respect to the classification or localization of a process of interest including processes which can be time varying, not spatially isotropic and for which action is required in real-time. Experimental results are presented for a 2-D target tracking application in which fully autonomous surface craft using simulated bearing sensors acquire and track a moving target in open water. In the first example, a single sensor vehicle adaptively tracks a target while simultaneously relaying the estimated track to a second vehicle acting as a classification platform. In the second example, two spatially distributed sensor vehicles adaptively track a moving target by fusing their sensor information to form a single target track estimate. In both cases the goal is to adapt the platform motion to minimize the uncertainty of the target track parameter estimates. The link between the sensor platform motion and the target track estimate uncertainty is fully derived and this information is used to develop the behaviors for the sensor platform control system. The experimental results clearly illustrate the significant processing gain that spatially distributed sensors can achieve over a single sensor when observing a dynamic phenomenon as well as the viability of behavior-based control for dealing with uncertainty in complex situations in marine sensor networks.Supported by the Office of Naval Research, with a 3-year National Defense Science and Engineering Grant Fellowship and research assistantships through the Generic Ocean Array Technology Sonar (GOATS) project, contract N00014-97-1-0202 and contract N00014-05-G-0106 Delivery Order 008, PLUSNET: Persistent Littoral Undersea Surveillance Network

Experiments in dynamic control of autonomous marine vehicles using acoustic modems

Marine robots are an increasingly attractive means for observing and monitoring in the ocean, but underwater acoustic communication (“acomms”) remains a major challenge, especially for real-time control. Packet loss occurs widely, bit rates are low, and there are significant delays. We consider here strategies for feedback control with acomms links in either the sensor-controller channel, or the controller-actuator channel. On the controller-actuator side we implement sparse packetized predictive control (S-PPC), which simultaneously addresses packet-loss and the data rate limit. For the sensor-controller channel we study a modified information filter (MIF) in a Linear Quadratic Gaussian (LQG) control scheme. Field experiments were carried out with both approaches, regulating crosstrack error in a robotic kayak using acomms. Outcomes with both the S-PPC and MIF LQG confirm that good performance is achievable.United States. Office of Naval Research (Grant N00014-09-1-0700)National Science Foundation (U.S.) (Contract CNS-1212597)Finmeccanic

Intelligent adaptive underwater sensor networks

Autonomous Underwater Vehicle (AUV) technology has reached a sufficient maturity level to be considered a suitable alternative to conventional Mine Countermeasures (MCM). Advantages of using a network of AUVs include time and cost efficiency, no personnel in the minefield, and better data collection. A major limitation for underwater robotic networks is the poor communication channel. Currently, acoustics provides the only means to send messages beyond a few metres in shallow water, however the bandwidth and data rate are low, and there are disturbances, such as multipath and variable channel delays, making the communication non-reliable. The solution this thesis proposes using a network of AUVs for MCM is the Synchronous Rendezvous (SR) method --- dynamically scheduling meeting points during the mission so the vehicles can share data and adapt their future actions according to the newly acquired information. Bringing the vehicles together provides a robust way of exchanging messages, as well as means for regular system monitoring by an operator. The gains and losses of the SR approach are evaluated against a benchmark scenario of vehicles having their tasks fixed. The numerical simulation results show the advantage of the SR method in handling emerging workload by adaptively retasking vehicles. The SR method is then further extended into a non-myopic setting, where the vehicles can make a decision taking into account how the future goals will change, given the available resource and estimation of expected workload. Simulation results show that the SR setting provides a way to tackle the high computational complexity load, common for non-myopic solutions. Validation of the SR method is based on trial data and experiments performed using a robotics framework, MOOS-IvP. This thesis develops and evaluates the SR method, a mission planning approach for underwater robotic cooperation in communication and resource constraint environment

Adaptive and Collaborative Bathymetric Channel-Finding Approach for Multiple Autonomous Marine Vehicle

This paper reports an investigation into the problem of rapid identification of a channel that crosses a body of water using one or more Unmanned Surface Vehicles (USV). A new algorithm called Proposal Based Adaptive Channel Search (PBACS) is presented as a potential solution that improves upon current methods. The empirical performance of PBACS is compared to lawnmower surveying and to Markov decision process (MDP) planning with two state-of-the-art reward functions: Upper Confidence Bound (UCB) and Maximum Value Information (MVI). The performance of each method is evaluated through comparison of the time it takes to identify a continuous channel through an area, using one, two, three, or four USVs. The performance of each method is compared across ten simulated bathymetry scenarios and one field area, each with different channel layouts. The results from simulations and field trials indicate that on average multi-vehicle PBACS outperforms lawnmower, UCB, and MVI based methods, especially when at least three vehicles are used.Comment: (v1) Submitted to IEEE International Conference on Robotics and Automation (ICRA) 202