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The Space and Naval Warfare Systems Center, San Diego has been involved in the continuing development of obstacle avoidance for unmanned surface vehicles (USVs) towards the aim of a high level of autonomous navigation. An autonomous USV can fulfill a variety of missions and applications that are of increasing interest for the US Navy and other Department of Defense and Department of Homeland Security organizations. The USV obstacle avoidance package is being developed first by accurately creating a world model based on various sensors such as vision, radar, and nautical charts. Then, with this world model the USV can avoid obstacles with the use of a far-field deliberative obstacle avoidance component and a near-field reactive obstacle avoidance component. This paper addresses the advances made in USV obstacle avoidance during the last two years.

This paper presents an experimental study on the performance of a Pose Estimation (PE) method based on a 3D time-of-flight camera the SwissRanger SR4000. The PE method tracks the visual features in the camera's intensity image and computes the camera's pose change from the 3D data of the matched features. To attain a small PE error, the noises of the sensor's intensity and range data are analyzed and a Gaussian filter is applied to reduce the noises. The statistical property of the filtered data is then characterized and the result is used to determine the minimum number of 3D data points that are required for a satisfactory PE accuracy. Two feature extractors, the SIFT (Scale Invariant Feature Transform) and SURF (Speed Up Robust Features) extractors, are used for the PE method and their performances are compared in term of PE error and computational time.Experimental results with various combinations of rotation and translation movements demonstrate that the SIFT extractor outperforms the SURF extractor in both PE accuracy and repeatability.

This paper presents the application of an Evolutionary Systems Engineering Model for Unmanned systems. It is a novel approach for research, development, test, and evaluation of unmanned systems. The Evolutionary Systems Engineering Model for Unmanned System is based on the System of Systems (SoS) Systems Engineering (SE) Wave Model [2] adopted by the DoD as best practices for Systems of Systems System Engineering [3], [5]. The SoS Wave Model was adopted by SPAWAR Systems Center (SSC) Pacific's Unmanned Systems Group to enable the agile and rapid evolution of autonomous unmanned systems. It provides the backplane for the Unmanned Systems Integration, Test, and Experimentation (UxSITE) capability at SSC Pacific.It establishes a systematic process for technology insertion and overarching strategies for managing risk and ensuring key capability objectives are met as the system evolves. Implemented as a continuous improvement process, the Evolutionary System Engineering approach provides flexibility and adaptability to address the inevitable changes in both the technical landscape and the larger operational environment. This paper provides a description of the Evolutionary Systems Engineering Model, its drivers, and how implementation of the SoS Wave Model addresses them.

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