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Development and Testing of Navigation Algorithms for Autonomous Underwater Vehicles

Cole Powell

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Published in Development And Testing Of Navigation Algorithms For Autonomous Underwater Vehicles (Springer Theses)

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Autonomous underwater vehicles (AUVs) are unmanned, self-guided vehicles that are capable of operating in the underwater environment without human intervention. They are used for a variety of applications, including oceanographic research, environmental monitoring, and military operations.

One of the key challenges in developing AUVs is the development of reliable and accurate navigation algorithms. These algorithms must be able to accurately estimate the vehicle's position, orientation, and velocity in order to enable the vehicle to navigate autonomously.

Development and Testing of Navigation Algorithms for Autonomous Underwater Vehicles (Springer Theses)

5 out of 5

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In this article, we will discuss the development and testing of navigation algorithms for AUVs. We will begin by providing an overview of the different types of navigation algorithms that are available. We will then discuss the factors that must be considered when selecting a navigation algorithm for an AUV. Finally, we will provide an overview of the testing procedures that are used to evaluate the performance of navigation algorithms.

Types of Navigation Algorithms

There are a variety of different navigation algorithms that can be used for AUVs. The most common types of algorithms include:

* Dead reckoning: Dead reckoning is a navigation technique that uses the vehicle's odometry (i.e., its measurements of its own motion) to estimate its position and orientation. Dead reckoning is a simple and inexpensive navigation technique, but it is not very accurate over long distances. * Inertial navigation: Inertial navigation is a navigation technique that uses inertial sensors (i.e., accelerometers and gyroscopes) to measure the vehicle's motion. Inertial navigation is more accurate than dead reckoning, but it is also more expensive. * GPS navigation: GPS navigation is a navigation technique that uses the Global Positioning System (GPS) to estimate the vehicle's position. GPS navigation is very accurate, but it is not available in all environments (e.g., underwater). * Acoustic navigation: Acoustic navigation is a navigation technique that uses acoustic signals to estimate the vehicle's position. Acoustic navigation is not as accurate as GPS navigation, but it is available in all environments.

The type of navigation algorithm that is best for an AUV will depend on the specific application. For example, if the AUV is operating in an environment where GPS is not available, then an acoustic navigation algorithm would be a better choice.

Factors to Consider When Selecting a Navigation Algorithm

When selecting a navigation algorithm for an AUV, there are a number of factors that must be considered. These factors include:

* Accuracy: The accuracy of a navigation algorithm is determined by how well it can estimate the vehicle's position and orientation. The accuracy of a navigation algorithm is typically measured in terms of its root mean square error (RMSE). * Precision: The precision of a navigation algorithm is determined by how repeatable its estimates are. The precision of a navigation algorithm is typically measured in terms of its standard deviation. * Robustness: The robustness of a navigation algorithm is determined by how well it can perform in different environments. A robust navigation algorithm will be able to perform well in a variety of conditions, such as in the presence of noise, interference, and sensor failures. * Computational cost: The computational cost of a navigation algorithm is determined by how much processing power it requires. A low-cost navigation algorithm will be able to run on a small, inexpensive computer. * Size and weight: The size and weight of a navigation algorithm are important considerations for AUVs that are small or have limited payload capacity.

Testing Navigation Algorithms

Once a navigation algorithm has been selected, it is important to test it to ensure that it meets the desired performance requirements. The testing of navigation algorithms typically involves the following steps:

1. Development of a test plan: The first step in testing a navigation algorithm is to develop a test plan. The test plan should specify the test goals, the test procedures, and the acceptance criteria. 2. Setup of the test environment: The next step is to setup the test environment. The test environment should be representative of the environment in which the AUV will operate. 3. Execution of the test: The next step is to execute the test. The test should be executed according to the test plan. 4. Analysis of the test results: The final step is to analyze the test results. The test results should be analyzed to determine whether the navigation algorithm meets the desired performance requirements.

The development and testing of navigation algorithms for AUVs is a complex and challenging task. However, by following the steps outlined in this article, it is possible to develop and test navigation algorithms that are accurate, reliable, and robust.

Development and Testing of Navigation Algorithms for Autonomous Underwater Vehicles (Springer Theses)

5 out of 5