4,967 research outputs found
Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles
There is increasing interest in the deployment and operation of multiple autonomous marine vehicles (AMVs) for a number of challenging scientific and commercial operational mission scenarios. Some of the missions, such as geotechnical surveying and 3D marine habitat mapping, require that a number of heterogeneous vehicles operate simultaneously in small areas, often in close proximity of each other. In these circumstances safety, reliability, and efficient multiple vehicle operation are key ingredients for mission success. Additionally, the deployment and operation of multiple AMVs at sea are extremely costly in terms of the logistics and human resources required for mission supervision, often during extended periods of time. These costs can be greatly minimized by automating the deployment and initial steering of a vehicle fleet to a predetermined configuration, in preparation for the ensuing mission, taking into account operational constraints. This is one of the core issues addressed in the scope of the Widely Scalable Mobile Underwater Sonar Technology project (WiMUST), an EU Horizon 2020 initiative for underwater robotics research. WiMUST uses a team of cooperative autonomous ma- rine robots, some of which towing streamers equipped with hydrophones, acting as intelligent sensing and communicat- ing nodes of a reconfigurable moving acoustic network. In WiMUST, the AMVs maintain a fixed geometric formation through cooperative navigation and motion control. Formation initialization requires that all the AMVs start from scattered positions in the water and maneuver so as to arrive at required target configuration points at the same time in a completely au- tomatic manner. This paper describes the decoupled prioritized vehicle motion planner developed in the scope of WiMUST that, together with an existing system for trajectory tracking, affords a fleet of vehicles the above capabilities, while ensuring inter- vehicle collision and streamer entanglement avoidance. Tests with a fleet of seven marine vehicles show the efficacy of the system planner developed.Peer reviewe
Progressively communicating rich telemetry from autonomous underwater vehicles via relays
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2012As analysis of imagery and environmental data plays a greater role in mission construction
and execution, there is an increasing need for autonomous marine vehicles
to transmit this data to the surface. Without access to the data acquired by a
vehicle, surface operators cannot fully understand the state of the mission. Communicating
imagery and high-resolution sensor readings to surface observers remains
a significant challenge – as a result, current telemetry from free-roaming
autonomous marine vehicles remains limited to ‘heartbeat’ status messages, with
minimal scientific data available until after recovery. Increasing the challenge, longdistance
communication may require relaying data across multiple acoustic hops
between vehicles, yet fixed infrastructure is not always appropriate or possible.
In this thesis I present an analysis of the unique considerations facing telemetry
systems for free-roaming Autonomous Underwater Vehicles (AUVs) used in exploration.
These considerations include high-cost vehicle nodes with persistent storage
and significant computation capabilities, combined with human surface operators
monitoring each node. I then propose mechanisms for interactive, progressive
communication of data across multiple acoustic hops. These mechanisms include
wavelet-based embedded coding methods, and a novel image compression scheme
based on texture classification and synthesis. The specific characteristics of underwater
communication channels, including high latency, intermittent communication,
the lack of instantaneous end-to-end connectivity, and a broadcast medium,
inform these proposals. Human feedback is incorporated by allowing operators to
identify segments of data thatwarrant higher quality refinement, ensuring efficient
use of limited throughput. I then analyze the performance of these mechanisms
relative to current practices.
Finally, I present CAPTURE, a telemetry architecture that builds on this analysis.
CAPTURE draws on advances in compression and delay tolerant networking to
enable progressive transmission of scientific data, including imagery, across multiple acoustic hops. In concert with a physical layer, CAPTURE provides an endto-
end networking solution for communicating science data from autonomous marine
vehicles. Automatically selected imagery, sonar, and time-series sensor data
are progressively transmitted across multiple hops to surface operators. Human
operators can request arbitrarily high-quality refinement of any resource, up to an
error-free reconstruction. The components of this system are then demonstrated
through three field trials in diverse environments on SeaBED, OceanServer and
Bluefin AUVs, each in different software architectures.Thanks to the National Science Foundation, and the
National Oceanic and Atmospheric Administration for
their funding of my education and this work
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
Communication-constrained multi-AUV cooperative SLAM
Multi-robot deployments have the potential for completing tasks more efficiently. For example, in simultaneous localization and mapping (SLAM), robots can better localize themselves and the map if they can share measurements of each other (direct encounters) and of commonly observed parts of the map (indirect encounters). However, performance is contingent on the quality of the communications channel. In the underwater scenario, communicating over any appreciable distance is achieved using acoustics which is low-bandwidth, slow, and unreliable, making cooperative operations very challenging. In this paper, we present a framework for cooperative SLAM (C-SLAM) for multiple autonomous underwater vehicles (AUVs) communicating only through acoustics. We develop a novel graph-based C-SLAM algorithm that is able to (optimally) generate communication packets whose size scales linearly with the number of observed features since the last successful transmission, constantly with the number of vehicles in the collective, and does not grow with time even the case of dropped packets, which are common. As a result, AUVs can bound their localization error without the need for pre-installed beacons or surfacing for GPS fixes during navigation, leading to significant reduction in time required to complete missions. The proposed algorithm is validated through realistic marine vehicle and acoustic communication simulations.United States. Office of Naval Research (Grant N00014-13-1-0588)National Science Foundation (U.S.) (Award IIS-1318392)United States. Office of Naval Research Globa
Challenges in Collaborative HRI for Remote Robot Teams
Collaboration between human supervisors and remote teams of robots is highly
challenging, particularly in high-stakes, distant, hazardous locations, such as
off-shore energy platforms. In order for these teams of robots to truly be
beneficial, they need to be trusted to operate autonomously, performing tasks
such as inspection and emergency response, thus reducing the number of
personnel placed in harm's way. As remote robots are generally trusted less
than robots in close-proximity, we present a solution to instil trust in the
operator through a `mediator robot' that can exhibit social skills, alongside
sophisticated visualisation techniques. In this position paper, we present
general challenges and then take a closer look at one challenge in particular,
discussing an initial study, which investigates the relationship between the
level of control the supervisor hands over to the mediator robot and how this
affects their trust. We show that the supervisor is more likely to have higher
trust overall if their initial experience involves handing over control of the
emergency situation to the robotic assistant. We discuss this result, here, as
well as other challenges and interaction techniques for human-robot
collaboration.Comment: 9 pages. Peer reviewed position paper accepted in the CHI 2019
Workshop: The Challenges of Working on Social Robots that Collaborate with
People (SIRCHI2019), ACM CHI Conference on Human Factors in Computing
Systems, May 2019, Glasgow, U
The design and implementation of a multi-agent architecture to increase coordination efficiency in multi-AUV operations
This research addresses the problem of coordinating multiple autonomous underwater
vehicle (AUV) operations. An intelligent mission executive has been created that uses
multi-agent technology to control and coordinate multiple AUVs in communication
deficient environments. By incorporating real time vehicle prediction, blackboardbased
hierarchical mission plans and mission optimisation in conjunction with a simple
broadcast communication system this system aims to handle the limitations inherent in
underwater operations and intelligently control multiple vehicles. In this research
efficiency is evaluated and then compared to the current state of the art in multiple AUV
control. The research is then validated in real AUV coordination trials.
Results will show that compared to the state of the art the control system developed and
implemented in this research coordinates multiple vehicles more efficiently and is able
to function in a range of poor communication environments. These findings are
supported by in water validation trials with heterogeneous AUVs.
This thesis will first present an in depth state of the art of the related research topics
including multi-agent systems, collaborative robotics and autonomous underwater
vehicles. The development and functionality of this research will then be explained
followed by a detailed description of the experiments. Results are then presented both
for the simulated and real world trials followed by a discussion of the findings
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