Mae L. Seto

AUV Navigation and Localization: A Review

Autonomous underwater vehicle (AUV) navigation and localization in underwater environments is particularly challenging due to the rapid attenuation of Global Positioning System (GPS) and radio-frequency signals. Underwater communications are low bandwidth and unreliable, and there is no access to a global positioning system. Past approaches to solve the AUV localization problem have employed expensive inertial sensors, used installed beacons in the region of interest, or required periodic surfacing of the AUV. While these methods are useful, their performance is fundamentally limited. Advances in underwater communications and the application of simultaneous localization and mapping (SLAM) technology to the underwater realm have yielded new possibilities in the field. This paper presents a review of the state of the art of AUV navigation and localization, as well as a description of some of the more commonly used methods. In addition, we highlight areas of future research potential.

Neural-Network-Based Path Planning for a Multirobot System With Moving Obstacles

Recently, a coordinated hybrid agent (CHA) framework was proposed for the control of multiagent systems (MASs). It was demonstrated that an intelligent planner can be designed for the CHA framework to automatically generate desired actions for multiple robots in an MAS. However, in previous studies, only static obstacles in the workspace were considered. In this paper, a neural-network-based approach is proposed for a multirobot system with moving obstacles. A biologically inspired neural-network-based intelligent planner is designed for the coordination of MASs. A landscape of the neural activities for all neurons of a CHA agent contains information about the agents local goal and moving obstacles. The proposed approach is able to plan the paths for multiple robots while avoiding moving obstacles. The proposed approach is simulated using both Matlab and Vortex. The Vortex module executes control commands from the control system module, and provides the outputs describing the vehicle state and terrain information, which are, in turn, used in the control module to produce the control commands. Simulation results show that the developed intelligent planner of the CHA framework can control a large complex system so that coordination among agents can be achieved.

On drag, Strouhal number and vortex-street structure

A phenomenological model for the vortex-shedding process behind bluff cylindrical bodies is proposed. Relationships between Strouhal frequency St, drag coefficient cD, Reynolds number Re and geometric wake parameters are obtained from mass conservation, momentum conservation in the transverse direction and energy considerations. For the first time, Roshkos (Technical Report TN 3169, NACA, US Government Printing Office, Washington DC, 1954) experimental discovery of vortex-street similarity behind different cylinders is analytically derived. In addition, the empirically obtained Strouhal-frequency laws of Roshko (Technical Report TN1191, NACA, US Government Printing Office, Washington DC, 1954) and Fey (Phys. Fluids A 10 (1998) 1547) are also reproduced. Measurements of St and cD including their Re dependency for flows around cylinders with circular, square, triangular, semi-circular and other cross sections agree favorably with the proposed model.

Multiple-Robot Simultaneous Localization and Mapping: A Review

Simultaneous localization and mapping SLAM in unknown GPS-denied environments is a major challenge for researchers in the field of mobile robotics. Many solutions for single-robot SLAM exist; however, moving to a platform of multiple robots adds many challenges to the existing problems. This paper reviews state-of-the-art multiple-robot systems, with a major focus on multiple-robot SLAM. Various issues and problems in multiple-robot SLAM are introduced, current solutions for these problems are reviewed, and their advantages and disadvantages are discussed.

Sensor-Driven Online Coverage Planning for Autonomous Underwater Vehicles

At present, autonomous underwater vehicle (AUV) mine countermeasure (MCM) surveys are normally preplanned by operators using ladder or zig-zag paths. Such surveys are conducted with side-looking sonar sensors whose performance is dependent on environmental, target, sensor, and AUV platform parameters. It is difficult to obtain precise knowledge of all of these parameters to be able to design optimal mission plans offline. This research represents the first known sensor driven online approach to seabed coverage for MCM. A method is presented where paths are planned using a multiobjective optimization. Information theory is combined with a new concept coined branch entropy based on a hexagonal cell decomposition. The result is a planning algorithm that not only produces shorter paths than conventional means, but is also capable of accounting for environmental factors detected in situ. Hardware-in-the-loop simulations and in water trials conducted on the IVER2 AUV show the effectiveness of the proposed method.

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.

Automated Ballast Tank Control System for Autonomous Underwater Vehicles

Autonomous underwater vehicles (AUVs) are frequently used for deep-water ocean applications such as surveying and cable laying, where accurate control of vehicle depth and attitude is needed. The water level in the onboard ballast tanks is typically manually set for neutral buoyancy before each mission, while the vehicle is on the surface. The ballast tank contents are not normally adjusted to control vehicle depth and orientation while the AUV is in operation. As a result, vehicle trajectory and orientation is exclusively controlled using the vehicles control surfaces during a mission. The challenges with controlling the depth and trim of an underwater vehicle include nonlinear hydrodynamic forces, as well as inherent time delays (latencies) associated with water tank level changes and valve adjustments. Furthermore, small changes in the location of the vehicles center of gravity (i.e., due to the deployment of the AUVs payload equipment) can reduce the control authority of the AUVs control surfaces. To meet these challenges, this paper proposes two unique variable ballast system (VBS) control approaches. The first proposed VBS controller changes the weight of the AUV to help control vehicle depth and vertical (inertial) velocity. The second proposed VBS controller attempts to shift the center of gravity xG along the body-fixed x -(longitudinal)-axis to reduce depth and pitch angle error while restoring control authority to the bowplane and sternplane deflection fins. The ballasting system consists of two water tanks positioned aft and forward of amidships. The ballast tanks are then automatically filled or emptied of ocean water as desired. Numerical simulations have been carried out on a 2-D underwater vehicle simulator to test and compare the performance of the proposed ballast and fin deflection control systems. The simulation results show that, for the assumptions and conditions tested, the proposed controllers are capable of achieving a setpoint depth and pitch angle with minimal error by effectively utilizing the ballast tanks and deflection fins. As a result, the work presented in this paper helps increase the autonomy of large AUVs on long-duration missions.

Communication-constrained multi-AUV cooperative SLAM

Multi-robot deployments have the potential for completing tasks more efficiently. For example, in simultaneous localization and mapping (SLAM), robots can better localize themselves and the map if they can share measurements of each other (direct encounters) and of commonly observed parts of the map (indirect encounters). However, performance is contingent on the quality of the communications channel. In the underwater scenario, communicating over any appreciable distance is achieved using acoustics which is low-bandwidth, slow, and unreliable, making cooperative operations very challenging. In this paper, we present a framework for cooperative SLAM (C-SLAM) for multiple autonomous underwater vehicles (AUVs) communicating only through acoustics. We develop a novel graph-based C-SLAM algorithm that is able to (optimally) generate communication packets whose size scales linearly with the number of observed features since the last successful transmission, constantly with the number of vehicles in the collective, and does not grow with time even the case of dropped packets, which are common. As a result, AUVs can bound their localization error without the need for pre-installed beacons or surfacing for GPS fixes during navigation, leading to significant reduction in time required to complete missions. The proposed algorithm is validated through realistic marine vehicle and acoustic communication simulations.

Towards an Ontology for Autonomous Robots

The IEEE RAS Ontologies for Robotics and Automation Working Group is dedicated to developing a methodology for knowledge representation and reasoning in robotics and automation. As part of this working group, the Autonomous Robots sub-group is tasked with developing ontology modules for autonomous robots. This paper describes the work in progress on the development of ontologies for autonomous systems. For autonomous systems, the focus is on the cooperation, coordination, and communication of multiple unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and autonomous underwater vehicles (AUVs). The ontologies serve as a framework for working out concepts of employment with multiple vehicles for a variety of operational scenarios with emphasis on collaborative and cooperative missions.

Decentralized cooperative trajectory estimation for autonomous underwater vehicles

Autonomous agents that can communicate and make relative measurements of each other can improve their collective localization accuracies. This is referred to as cooperative localization (CL). Autonomous underwater vehicle (AUV) CL is constrained by the low throughput, high latency, and unreliability of of the acoustic channel used to communicate when submerged. Here we propose a CL algorithm specifically designed for full trajectory, or maximum a posteriori, estimation for AUVs. The method is exact and has the advantage that the broadcast packet sizes increase only linearly with the number of AUVs in the collective and do not grow at all in the case of packet loss. The approach allows for AUV missions to be achieved more efficiently since: 1) vehicles waste less time surfacing for GPS fixes, and 2) payload data is more accurately localized through the smoothing approach.