Nested autonomy for unmanned marine vehicles with MOOS-IvP

Dynamically orthogonal narrow-angle parabolic equations for stochastic underwater sound propagation. Part I: Theory and schemes

Robust informative acoustic predictions require precise knowledge of ocean physics, bathymetry, seabed, and acoustic parameters. However, in realistic applications, this information is uncertain due to sparse and heterogeneous measurements and complex ocean physics. Efficient techniques are thus needed to quantify these uncertainties and predict the stochastic acoustic wave fields. In this work, we derive and implement new stochastic differential equations that predict the acoustic pressure fields and their probability distributions. We start from the stochastic acoustic parabolic equation (PE) and employ the instantaneously-optimal Dynamically Orthogonal (DO) equations theory. We derive stochastic DO-PEs that dynamically reduce and march the dominant multi-dimensional uncertainties respecting the nonlinear governing equations and non-Gaussian statistics. We develop the dynamical reduced-order DO-PEs theory for the Narrow-Angle parabolic equation and implement numerical schemes for discretizing and integrating the stochastic acoustic fields.

Under-ice acoustic navigation using real-time model-aided range estimation

The long baseline (LBL) underwater navigation paradigm relies on the conversion of travel times into pseudoranges to trilaterate position. For real-time autonomous underwater vehicle (AUV) operations, this conversion assumes an isovelocity sound speed. For re-navigation, computationally and/or labor-intensive acoustic modeling may be employed to reduce uncertainty. This work demonstrates a real-time ray-based prediction of the effective sound speed along a path from source to receiver. This method was implemented for an AUV-LBL system in the Beaufort Sea in an ice-covered and a double-ducted propagation environment. Given the lack of Global Navigation Satellite Systems (GNSS) data throughout the vehicle's mission, the pseudorange performance is first evaluated on acoustic transmissions between GNSS-linked beacons. The mean real-time absolute range error between beacons is roughly 11 m at distances up to 3 km. A consistent overestimation in the real-time method provides insights for improved eigenray filtering by the number of bounces. An operationally equivalent pipeline is used to reposition the LBL beacons and re-navigate the AUV, using modeled, historical, and locally observed sound speed profiles. The best re-navigation error is 1.84 ± 2.19 m root mean square. The improved performance suggests that this approach extends the single meter accuracy of the deployed GNSS units into the water column.

Passive acoustic tracking of an unmanned underwater vehicle using bearing-Doppler-speed measurements

Tracking unmanned underwater vehicles (UUVs) in the presence of shipping traffic is a critical task for passive acoustic harbor security systems. In general, the vessels can be tracked by their unique acoustic signature caused by machinery vibration and cavitation noise. However, cavitation noise of UUVs is quiet relative to that of ships. Furthermore, tracking a target with bearing-only measurements requires the observing platform to maneuver. In this work, it is demonstrated that it is possible to passively track an UUV from its high-frequency motor noise using a stationary array in a shallow-water experiment with passing boats. The motor noise provides high signal-to-noise ratio measurements of the bearing, range rate, and speed, which we combined in an unscented Kalman filter to track the target. First, beamforming is applied to estimate the bearing. Next, the range rate is calculated from the Doppler effect on the motor noise. The propeller rotation rate can be estimated from the motor signature and converted to the speed using a pre-identified model of the robot. The bearing-Doppler-speed measurements outperformed the traditional bearing-Doppler target motion analysis: the bearing, bearing rate, range, and range rate accuracy improved by a factor of 2×, 16×, 3×, and 6×, respectively. Finally, the robustness of the tracking solution to an unknown vehicle model is evaluated.

A Backseat Control Architecture for a Slocum Glider

Adaptive sampling provides an innovative and favorable method of improving the effectiveness of underwater vehicles in collecting data. Adaptive sampling works by controlling an underwater vehicle by using measurements from sensors and states of the vehicle. A backseat driver system was developed in this work and installed on a Slocum glider to equip it with an ability to perform adaptive sampling tasks underwater. This backseat driver communicated with the main vehicle control system of the glider through a robot operating system (ROS) interface. The external control algorithms were implemented through ROS nodes, which subscribed simulated sensor measurements and states of the glider and published desired states to the glider. The glider was set up in simulation mode to test the performance of the backseat driver as integrated into the control architecture of the glider. Results from the tests revealed that the backseat driver could effectively instruct the depth, heading, and waypoints as well as activate or deactivate behaviors adaptively. The developed backseat driver will be tested in future field experiments with sensors included and safety rules implemented before being applied in adaptive sampling missions such as adaptive oil spill sampling.

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.

Improving Resource Management for Unattended Observation of the Marginal Ice Zone Using Autonomous Underwater Gliders

We present control policies for use with a modified autonomous underwater glider that are intended to enable remote launch/recovery and long-range unattended survey of the Arctic's marginal ice zone (MIZ). This region of the Arctic is poorly characterized but critical to the dynamics of ice advance and retreat. Due to the high cost of operating support vessels in the Arctic, the proposed glider architecture minimizes external infrastructure requirements for navigation and mission updates to brief and infrequent satellite updates on the order of once per day. This is possible through intelligent power management in combination with hybrid propulsion, adaptive velocity control, and dynamic depth band selection based on real-time environmental state estimation. We examine the energy savings, range improvements, decreased communication requirements, and temporal consistency that can be attained with the proposed glider architecture and control policies based on preliminary field data, and we discuss a future MIZ survey mission concept in the Arctic. Although the sensing and control policies presented here focus on under ice missions with an unattended underwater glider, they are hardware independent and are transferable to other robotic vehicle classes, including in aerial and space domains.

An acoustic remote sensing method for high-precision propeller rotation and speed estimation of unmanned underwater vehicles

Understanding the dominant sources of acoustic noise in unmanned underwater vehicles (UUVs) is important for passively tracking these platforms and for designing quieter propulsion systems. This work describes how the vehicle's propeller rotation can be passively measured by the unique high frequency acoustic signature of a brushless DC motor propulsion system and compares this method to Detection of Envelope Modulation on Noise (DEMON) measurements. First, causes of high frequency tones were determined through direct measurements of two micro-UUVs and an isolated thruster at a range of speeds. From this analysis, common and dominant features of noise were established: strong tones at the motor's pulse-width modulated frequency and its second harmonic, with sideband spacings at the propeller rotation frequency multiplied by the poles of the motor. In shallow water field experiments, measuring motor noise was a superior method to the DEMON algorithm for estimating UUV speed. In negligible currents, and when the UUV turn-per-knot ratio was known, measuring motor noise produced speed predictions within the error range of the vehicle's inertial navigation system's reported speed. These findings are applicable to other vehicles that rely on brushless DC motors and can be easily integrated into passive acoustic systems for target motion analysis.

An Inland Shore Control Centre for Monitoring or Controlling Unmanned Inland Cargo Vessels

Augmenting the automation level of the inland waterway cargo transport sector, coupled with mechatronic innovation in this sector, could increase its competitiveness. This increase might potentially induce a sustainable paradigm shift in the road-dominated inland cargo transport sector. A key enabler of this envisaged shift may be an inland shore control centre (I-SCC) capable of remotely monitoring and controlling inland vessels. Accordingly, this study investigated the concept and design requirements to achieve an inland I-SCC that provides interaction services when supervising an unmanned surface vessel (USV). This I-SCC can help its operator to develop situational awareness and sensemaking. The conducted experiments offered insights into the performance of both the I-SCC system and its operator, and unlock research on the impact on ship sense and harmony when remotely controlling a USV. The Hull-To-Hull project extends the current I-SCC by providing enhanced motion control. This enhancement enables further performance insights and might improve the future monitoring of USVs. The successful I-SCC construction, the preliminary experiments, and the design-extension demonstrate that the I-SCC can serve as an experimental platform for both mechatronic innovation and human-automation integration research in the inland waterway sector, whilst additionally providing fruitful knowledge for adjacent research domains.

A Fault-Tolerant Steering Prototype for X-Rudder Underwater Vehicles

The X-rudder concept has been applied to more and more autonomous underwater vehicles (AUVs) in recent years, since it shows better maneuverability and robustness against rudder failure compared to the traditional cruciform rudder. Aiming at the fault-tolerant control of the X-rudder AUV (hereinafter abbreviated as xAUV), a fault-tolerant steering prototype system which can realize dynamics control, autonomous rudder fault detection and fault-tolerant control is presented in this paper. The steering prototype system is deployed on a verification platform, an xAUV, in which the monitor software is developed based on the factory method and the onboard software is developed based on the finite state machine (FSM). Dual-loop increment feedback control (DIFC) is first introduced to obtain smooth virtual rudder commands considering actuator's limitations. Then the virtual rudder commands are transformed into X-rudder commands based on the mapping theory. In rudder fault diagnosis, an optimized particle filter is proposed for estimating rudder effect deduction, with proposal distribution derived from unscented Kalman filter (UKF). Then the fault type can be determined by analyzing indicators related to the deduction. Fault-tolerant control is addressed by dealing with nonlinear programming (NLP) problem, where minimization of allocation errors and control efforts are set as the optimization objectives, and rudder failure, saturation and actuators limitations are considered as constraints. The fixed-point iteration method is utilized to solve this optimization problem. Many field tests have been conducted in towing tank. The experimental results demonstrate that the proposed steering prototype system is able to detect rudder faults and is robust against rudder failure.

Unmanned Surface Vehicle Simulator with Realistic Environmental Disturbances

The use of robotics in disaster scenarios has become a reality. However, an Unmanned Surface Vehicle (USV) needs a robust navigation strategy to face unpredictable environmental forces such as waves, wind, and water current. A starting step toward this goal is to have a programming environment with realistic USV models where designers can assess their control strategies under different degrees of environmental disturbances. This paper presents a simulation environment integrated with robotic middleware which models the forces that act on a USV in a disaster scenario. Results show that these environmental forces affect the USV's trajectories negatively, indicating the need for more research on USV control strategies considering harsh environmental conditions. Evaluation scenarios were presented to highlight specific features of the simulator, including a bridge inspection scenario with fast water current and winds.

Classification of underwater targets from autonomous underwater vehicle sampled bistatic acoustic scattered fields

One of the long term goals of autonomous underwater vehicle (AUV) minehunting is to have multiple inexpensive AUVs in a harbor autonomously classify hazards. Existing acoustic methods for target classification using AUV-based sensing, such as sidescan and synthetic aperture sonar, require an expensive payload on each outfitted vehicle and post-processing and/or image interpretation. A vehicle payload and machine learning classification methodology using bistatic angle dependence of target scattering amplitudes between a fixed acoustic source and target has been developed for onboard, fully autonomous classification with lower cost-per-vehicle. To achieve the high-quality, densely sampled three-dimensional (3D) bistatic scattering data required by this research, vehicle sampling behaviors and an acoustic payload for precision timed data acquisition with a 16 element nose array were demonstrated. 3D bistatic scattered field data were collected by an AUV around spherical and cylindrical targets insonified by a 7-9 kHz fixed source. The collected data were compared to simulated scattering models. Classification and confidence estimation were shown for the sphere versus cylinder case on the resulting real and simulated bistatic amplitude data. The final models were used for classification of simulated targets in real time in the LAMSS MOOS-IvP simulation package [M. Benjamin, H. Schmidt, P. Newman, and J. Leonard, J. Field Rob. 27, 834-875 (2010)].

Sensor-driven area coverage for an autonomous fixed-wing unmanned aerial vehicle

Area coverage with an onboard sensor is an important task for an unmanned aerial vehicle (UAV) with many applications. Autonomous fixed-wing UAVs are more appropriate for larger scale area surveying since they can cover ground more quickly. However, their non-holonomic dynamics and susceptibility to disturbances make sensor coverage a challenging task. Most previous approaches to area coverage planning are offline and assume that the UAV can follow the planned trajectory exactly. In this paper, this restriction is removed as the aircraft maintains a coverage map based on its actual pose trajectory and makes control decisions based on that map. The aircraft is able to plan paths in situ based on sensor data and an accurate model of the on-board camera used for coverage. An information theoretic approach is used that selects desired headings that maximize the expected information gain over the coverage map. In addition, the branch entropy concept previously developed for autonomous underwater vehicles is extended to UAVs and ensures that the vehicle is able to achieve its global coverage mission. The coverage map over the workspace uses the projective camera model and compares the expected area of the target on the ground and the actual area covered on the ground by each pixel in the image. The camera is mounted on a two-axis gimbal and can either be stabilized or optimized for maximal coverage. Hardware-in-the-loop simulation results and real hardware implementation on a fixed-wing UAV show the effectiveness of the approach. By including the already developed automatic takeoff and landing capabilities, we now have a fully automated and robust platform for performing aerial imagery surveys.

AUV behavior recognition using behavior histograms, HMMs, and CRFs

This paper focuses on behavior recognition in an underwater application as a substitute for communicating through acoustic transmissions, which can be unreliable. The importance of this work is that sensor information regarding other agents can be leveraged to perform behavior recognition, which is activity recognition of robots performing specific programmed behaviors, and task-assignment. This work illustrates the use of Behavior Histograms, Hidden Markov Models (HMMs), and Conditional Random Fields (CRFs) to perform behavior recognition. We present challenges associated with using each behavior recognition technique along with results on individually selected test trajectories, from simulated and real sonar data, and real-time recognition through a simulated mission.