Environmental monitoring of marine environments presents several challenges: the harshness of the environment, the often remote location, and most importantly, the vast area it covers. Manual operations are time consuming, often dangerous, and labor intensive. Operations from oceanographic vessels are costly and limited to open seas and generally deeper bodies of water. In addition, with lake, river, and ocean shoreline being a finite resource, waterfront property presents an ever increasing valued commodity, requiring exploration and continued monitoring of remote waterways. In order to efficiently explore and monitor currently known marine environments as well as reach and explore remote areas of interest, we present a design of an autonomous surface vehicle (ASV) with the power to cover large areas, the payload capacity to carry sufficient power and sensor equipment, and enough fuel to remain on task for extended periods. An analysis of the design and a discussion on lessons learned during deployments is presented in this paper.
In 2005, MIT's Curcio al introduced their surface craft for oceanographic and undersea testing (SCOUT) @cite_13 . SCOUT is geared for oceanographic deployment based on an obstacle avoidance system working in conjunction with a remote palm device for high level mission control. Their pioneering design and build of a truly unmanned boat set the stage for a variety of expansions of their original design. For our purposes, the electric drivetrain results in an increased draft to allow clearance for the electric motor head and propeller to displace water below the stern of the kayak. As well, the mission specific sensor design and implementation offer us insight for our design to remain flexible and modular to accommodate larger and heavier instrumentation. Examples include sidescan sonar sensors and acoustic Doppler current profilers (ADCP) that are becoming prolific as the devices become more affordable. The small size limiting manual operation capabilities, low operating maximum speed of 5.6 kilometers per hour, and medium operation time of eight hours between charging are shortcomings of SCOUT that we seek to improve.
@cite_13
1940215497 2013402723 85781994 2267281579
In order to advance research efforts in the area of cooperative autonomy, a low cost autonomous surface craft, SCOUT (Surface Craft for Oceanographic and Undersea Testing) was developed by engineers at the MIT Department of Ocean Engineering. Design objectives include simplicity, robustness, versatility and improved operational utility. Four vehicles were fabricated during the summer of 2004 and a number of field experiments have been conducted using these craft. This paper discusses the design of the SCOUT vehicle and introduces some examples of the utility of this platform as a tool for improved software development, particularly in the area of cooperative autonomy. This paper describes a system for reacquiring features of interest in a shallow-water ocean environment, using autonomous underwater vehicles (AUVs) equipped with low-cost sonar and navigation sensors. In performing mine countermeasures, it is critical to enable AUVs to navigate accurately to previously mapped objects of interest in the water column or on the seabed, for further assessment or remediation. An important aspect of the overall system design is to keep the size and cost of the reacquisition vehicle as low as possible, as it may potentially be destroyed in the reacquisition mission. This low-cost requirement prevents the use of sophisticated AUV navigation sensors, such as a Doppler velocity log (DVL) or an inertial navigation system (INS). Our system instead uses the Proviewer 900-kHz imaging sonar from Blueview Technologies, which produces forward-looking sonar (FLS) images at ranges up to 40 mat approximately 4 Hz. In large volumes, it is hoped that this sensor can be manufactured at low cost. Our approach uses a novel simultaneous localization and mapping (SLAM) algorithm that detects and tracks features in the FLS images to renavigate to a previously mapped target. This feature-based navigation (FBN) system incorporates a number of recent advances in pose graph optimization algorithms for SLAM. The system has undergone extensive field testing over a period of more than four years, demonstrating the potential for the use of this new approach for feature reacquisition. In this report, we review the methodologies and components of the FBN system, describe the system's technological features, review the performance of the system in a series of extensive in-water field tests, and highlight issues for future research. In this paper we present strategies for adaptive sampling using Autonomous Underwater Vehicle (AUV) ∞eets. The central theme of our strategies is the use of feedback that integrates distributed in-situ measurements into a coordinated mission planner. The measurements consist of GPS updates and estimated gradients of the environmental flelds (e.g., temperature) that are used to navigate the AUV ∞eets enabling effective front tracking and or feature detection. To this efiect these ∞eets are required to translate to collect and seek good data, expand contract to efiect changes in sensor resolution, and rotate and reconflgure to maximize sensing coverage, all while retaining a prescribed formation. These strategies play a key role in directing a cooperative ∞eet of autonomous underwater gliders in the flrst experiment of the O‐ce of Naval Research sponsored Autonomous Ocean Sampling Network II (AOSN-II) project in Monterey Bay, during August-September 2003. We present the coordination framework and investigate the efiectiveness of our sampling strategies in the context of AOSN-II via detailed simulations. Survey-class autonomous underwater vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the mid-water column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Current Profiler mode of the DVL during descent, along with data from other sensors, the method discussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Mapping (SLAM) and on descents up to 3km deep with the Sentry AUV.