The challenge of autonomous underwater navigation

Navigating an AUV presents unique challenges to the researcher and the practitioner. One way to understand the particularities of this challenge is to consider two important facets of AUV operations: the marine environment and desired results.

The ocean environment presents both challenges and opportunities for autonomous navigation. The challenges are well documented: seawater is opaque to electromagnetic signals making Global Positioning System (GPS) solutions infeasible; acoustic communication is limited in bandwidth, scale and reliability (Catipovic, 1990) and the ocean environment is observationally limited and ever-changing.

On the other hand the deep-sea environment can be an ideal place for autonomous vehicle operations. The unstructured environment can be structured by the addition of acoustic transponders moored to the seafloor or through close communication with a surface ship. Either method provides an absolute position reference which decreases the demands on real-time perception and decision making. Also, deep-water can be one of the most forgiving acoustic environments because of the homogeneous and stable sounds speed structure and low ambient noise.

The opportunity for novel observation counterbalances these operational difficulties. We have better maps of Mars, Venus and the Moon that we have of the Earth's ocean, creating a great potential to advance our observational capability through technology.

2.2 Creating new data products

Typically a gap between the needs of the AUV user and the capabilities of the navigation solution. The user is often not directly interested in the navigation, but instead is focused on producing a data product, an gestalt representation of the underwater environment. The vehicle designer should incorporate the right navigation instruments and the right data processing to provide a navigation solution appropriate for the desired data product. This perspective, having the requirements of the data product drive the design decisions, leads to closing the gap illustrated in Fig. 2.

Fig. 1. Illustration of how vehicle design decisions are driven by the needs of the application (the desired data product) and the capabilities of the navigation sensors and algorithms.

It is only a slight over simplification to consider the resolution of any observation to be directly proportional to the navigation precision. Fig. 2 shows a common situation to illustrate this notion. In this case the data products are a photomosaic and a small-scale bathymetry map, both shown in the figure. The remotely operated vehicle (ROV) JASON is shown as it surveys the seafloor. Navigation allows all the measurements (e.g., sonar bathymetry) and observations (e.g., optical images) to be placed in a common coordinate system. How well we can resolve two disparate data sources, i.e., the resolution of our data product, depends on the uncertainty in our navigation. Summarized another way, the spatial size of each "pixel" in our final image is fundametnally limited to the uncertainty in our navigation solution.

Data Products

Sensors/ Algorithms

Functional Requirements

Technical Capabilities

AUV Design

Fig. 2. Illustration of the concept of co-registered data. The ROV JASON is shown performing a survey collecting optical images and bathymetry data. Range-based navigation provides a common coordinate system. Component images are courtesy of the Deep Submergence Lab (DSL) at the Woods Hole Oceanographic Institution.

Fig. 2. Illustration of the concept of co-registered data. The ROV JASON is shown performing a survey collecting optical images and bathymetry data. Range-based navigation provides a common coordinate system. Component images are courtesy of the Deep Submergence Lab (DSL) at the Woods Hole Oceanographic Institution.

3. State of practice

AUV operations require a reliable navigation solution. Methods currently in operation on autonomous platforms are simple and robust. These real-world solutions typically make use of just a few key sensors:

• GPS receivers to measure position at the surface

• Long baseline transponders to measure the distance from the AUV to transponders in known locations.

• Doppler velocity logs to measure velocity relative to the bottom, supported by attitude and heading measurements

These sensors are dedicated navigation sensors, distinct from the remote sensing payload sensors which collect measurements which are not processed for real-time perception. These relatively simple sensing modalities, configured and combined in a variety of interesting ways, have proven to provide a variety of solutions that are robust to the complexities of the ocean environment.

3.1 An example

It is informative to consider a particular example. This example, like the data shown in Fig. 2, is taken from work with the JASON ROV system from the Deep Submergence Lab at

Woods Hole Oceanographic. The ROV is instrumented with a combination that has become standard in AUV and ROV applications: absolute positioning using LBL transponders and seafloor odometry from a DVL and heading reference.

To understand the tradeoffs in designing an appropriate navigation system it is useful to contrast modalities that exhibit unbounded error growth with those that have bounded error. Fig. 3 illustrates this contrast. The dead-reckoning solution provided by the DVL alone is shown to drift over time; the error growth is unbounded. In Fig. 3 the DVL track begins at the origin (shown in the figure as a large "X") and then diverges from the absolute reference provided by the LBL reference. In what follows we show how quantitative models of this error accumulation can be used to improve design and operation.

Navigation Tracks: Jason 230

Navigation Tracks: Jason 230

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