Coordinated Sensor-Based Area Coverage and Cooperative Localization of a Heterogeneous Fleet of Autonomous Surface Vessels (ASVs)

Sensor coverage with fleets of robots is a complex task requiring solutions to localization, communication, navigation and basic sensor coverage. Sensor coverage of large areas is a problem that occurs in a variety of different environments from terrestrial to aerial to aquatic. In this thesis we consider the aquatic version of the problem. Given a known aquatic environment and collection of aquatic surface vehicles with known kinematic and dynamic constraints, how can a fleet of vehicles be deployed to provide sensor coverage of the surface of the body of water? Rather than considering this problem in general, in this work we consider the problem given a specific fleet consisting of one very well equipped robot aided by a number of smaller, less well equipped devices that must operate in close proximity to the main robot. A boustrophedon decomposition algorithm is developed that incorporates the motion, sensing and communication constraints imposed by the autonomous fleet. Solving the coverage problem leads to a localization/communication problem. A critical problem for a group of autonomous vehicles is ensuring that the collection operates within a common reference frame. Here we consider the problem of localizing a heterogenous collection of aquatic surface vessels within a global reference frame. We assume that one vessel -- the mother robot -- has access to global position data of high accuracy, while the other vessels -- the child robots -- utilize limited onboard sensors and sophisticated sensors on board the mother robot to localize themselves. This thesis provides details of the design of the elements of the heterogeneous fleet including the sensors and sensing algorithms along with the communication strategy used to localize all elements of the fleet within a global reference frame. Details of the robot platforms to be used in implementing a solution are also described. Simulation of the approach is used to demonstrate the effectiveness of the algorithm, and the algorithm and its components are evaluated using a fleet of ASVs

An Autonomous Surface Vehicle for Long Term Operations

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.Comment: In proceedings of MTS/IEEE OCEANS, 2018, Charlesto

Oceanids C2: An Integrated Command, Control, and Data Infrastructure for the Over-the-Horizon Operation of Marine Autonomous Systems

Long-range Marine Autonomous Systems (MAS), operating beyond the visual line-of-sight of a human pilot or research ship, are creating unprecedented opportunities for oceanographic data collection. Able to operate for up to months at a time, periodically communicating with a remote pilot via satellite, long-range MAS vehicles significantly reduce the need for an expensive research ship presence within the operating area. Heterogeneous fleets of MAS vehicles, operating simultaneously in an area for an extended period of time, are becoming increasingly popular due to their ability to provide an improved composite picture of the marine environment. However, at present, the expansion of the size and complexity of these multi-vehicle operations is limited by a number of factors: (1) custom control-interfaces require pilots to be trained in the use of each individual vehicle, with limited cross-platform standardization; (2) the data produced by each vehicle are typically in a custom vehicle-specific format, making the automated ingestion of observational data for near-real-time analysis and assimilation into operational ocean models very difficult; (3) the majority of MAS vehicles do not provide machine-to-machine interfaces, limiting the development and usage of common piloting tools, multi-vehicle operating strategies, autonomous control algorithms and automated data delivery. In this paper, we describe a novel piloting and data management system (C2) which provides a unified web-based infrastructure for the operation of long-range MAS vehicles within the UK's National Marine Equipment Pool. The system automates the archiving, standardization and delivery of near-real-time science data and associated metadata from the vehicles to end-users and Global Data Assembly Centers mid-mission. Through the use and promotion of standard data formats and machine interfaces throughout the C2 system, we seek to enable future opportunities to collaborate with both the marine science and robotics communities to maximize the delivery of high-quality oceanographic data for world-leading science

Communications, Decision-Making, and Interactions of a Multi-Agent Autonomous Vehicle System

Autonomous vehicles are becoming ever more common and offer many attractive benefits to society. They can operate for long periods of time unattended, operate in environments that may be dangerous to humans, perform time consuming or repetitive tasks and all with greater efficiency and lower costs than humans. For these vehicles to be able to do these things, algorithms need to be designed and optimized that allow them to interact with the real-world environment in safe, effective, and efficient ways. We designed and built a set of three homogeneous water-based autonomous surface vehicles equipped with appropriate sensors and communications ability along with algorithms designed to allow these vehicles to perform various cooperative tasks using data obtained from the vehicles’ sensors and data shared between the vehicles. These vehicles were designed to be modular, economical, and, where possible, were constructed using off-the-shelf technology with programming designed to take advantage of these systems. When the COVID-19 pandemic put an end to lab and field work the physical vehicles were stored but the research continued utilizing a hybrid hardware-software simulation of the system. Three microcontrollers identical to the devices controlling the physical boats were attached via a Universal Serial Bus (USB) hub to a desktop computer running a simulated environment written in Python™. The three vehicles (microcontrollers) were given tasks including patrolling adjoining areas of the water body delineated by latitude and longitude boundaries while staying within their own boundary and avoiding collisions with the other vehicles. Initial testing was successful with the algorithm able to maintain the vehicles within their boundary \u3e=95% of the time with no collisions. Additional problem types including parallel travel; wind and current challenges; and gradient tracking and relevant algorithms are discussed

Tele-Supervised Adaptive Ocean Sensor Fleet

The Tele-supervised Adaptive Ocean Sensor Fleet (TAOSF) is a multi-robot science exploration architecture and system that uses a group of robotic boats (the Ocean-Atmosphere Sensor Integration System, or OASIS) to enable in-situ study of ocean surface and subsurface characteristics and the dynamics of such ocean phenomena as coastal pollutants, oil spills, hurricanes, or harmful algal blooms (HABs). The OASIS boats are extended- deployment, autonomous ocean surface vehicles. The TAOSF architecture provides an integrated approach to multi-vehicle coordination and sliding human-vehicle autonomy. One feature of TAOSF is the adaptive re-planning of the activities of the OASIS vessels based on sensor input ( smart sensing) and sensorial coordination among multiple assets. The architecture also incorporates Web-based communications that permit control of the assets over long distances and the sharing of data with remote experts. Autonomous hazard and assistance detection allows the automatic identification of hazards that require human intervention to ensure the safety and integrity of the robotic vehicles, or of science data that require human interpretation and response. Also, the architecture is designed for science analysis of acquired data in order to perform an initial onboard assessment of the presence of specific science signatures of immediate interest. TAOSF integrates and extends five subsystems developed by the participating institutions: Emergent Space Tech - nol ogies, Wallops Flight Facility, NASA s Goddard Space Flight Center (GSFC), Carnegie Mellon University, and Jet Propulsion Laboratory (JPL). The OASIS Autonomous Surface Vehicle (ASV) system, which includes the vessels as well as the land-based control and communications infrastructure developed for them, controls the hardware of each platform (sensors, actuators, etc.), and also provides a low-level waypoint navigation capability. The Multi-Platform Simulation Environment from GSFC is a surrogate for the OASIS ASV system and allows for independent development and testing of higher-level software components. The Platform Communicator acts as a proxy for both actual and simulated platforms. It translates platform-independent messages from the higher control systems to the device-dependent communication protocols. This enables the higher-level control systems to interact identically with heterogeneous actual or simulated platforms

Design of Large Diameter Mine Countermeasure Hybrid Power Unmanned Underwater Vehicle

Mines are one of the most cost-effective and moderated weapon systems that are easy to deploy, but difficult to clear. Not only has the development of the mine countermeasure (MCM) underwater unmanned vehicle (UUV) improved cost- and time-effectiveness in operation, but also it has avoided unnecessary human casualties

Toward a complete system for surveillance of the whole EEZ: ScanMaris and associated projects

International audienceThere is currently an undeniable increase of maritime goods exchanges. As a consequence, maritime threats and risks are also rising. Innovative solution has to be developed to improve the security of this huge economic activity. Future generation of maritime surveillance system should allow: permanent and all weather coverage of maritime border areas, continuous collection of heterogeneous data provided by various sources, automatic detection of abnormal vessel behaviors, understanding of suspicious events, and early identification of threats. No equipment and information system deployments are at present able to answer all these requirements. We propose here an integrated system with relevant innovative technologies and capacities. The integrated system includes existing conventional and innovative sensors networks as well as new functionalities to track vessel movements and activities or detect abnormal vessel behaviors. The proposed high level engineering architecture is able to generate documented alarms using abnormal events. Those events are extracted from our intelligent maritime traffic picture. Thus, we aim to validate an end to end surveillance chain for future operational sea border surveillanc