2009 ieee-rsj international conference on intelligent robots and systems

A wide range of human–robot collaborative applications in diverse domains, such as manufacturing, health care, the entertainment industry, and social interactions, require an autonomous robot to fo...

Person-following by autonomous robots: A categorical overview:

In this paper, we present a number of robust methodologies for an underwater robot to visually detect, follow, and interact with a diver for collaborative task execution. We design and develop two autonomous diver‐following algorithms, the first of which utilizes both spatial‐ and frequency‐domain features pertaining to human swimming patterns to visually track a diver. The second algorithm uses a convolutional neural network‐based model for robust tracking‐by‐detection. In addition, we propose a hand gesture‐based human–robot communication framework that is syntactically simpler and computationally more efficient than the existing grammar‐based frameworks. In the proposed interaction framework, deep visual detectors are used to provide accurate hand gesture recognition; subsequently, a finite‐state machine performs robust and efficient gesture‐to‐instruction mapping. The distinguishing feature of this framework is that it can be easily adopted by divers for communicating with underwater robots without using artificial markers or requiring memorization of complex language rules. Furthermore, we validate the performance and effectiveness of the proposed methodologies through a number of field experiments in closed‐ and open‐water environments. Finally, we perform a user interaction study to demonstrate the usability benefits of our proposed interaction framework compared to the existing methods.

pdf/understanding-human-motion-and-gestures-for-underwater-human-2a02crssv1.pdf

Understanding human motion and gestures for underwater human–robot collaboration

Simulation plays an important role in the development, testing and evaluation of new robotic applications, reducing implementation time, cost and risk. For much of the robotics community, the open-source Gazebo robot simulator has emerged as the de facto standard environment for prototyping and testing robotic systems. While Gazebo offers strong support for terrestrial, aerial and space robotics applications, less support is available for marine applications involving vehicles at and below the water surface. To address this deficiency, we present the Virtual RobotX (VRX) simulation, a general purpose open-source development and testing tool, based on Gazebo, capable of approximating the behavior of unmanned surface vessels operating in complex ocean environments. We highlight the application of these capabilities using the VRX challenge reference implementation, a new simulation-based robot competition designed to complement the physical Maritime RobotX Challenge.

Toward Maritime Robotic Simulation in Gazebo

The estimation of geometric structure of objects located underwater underpins a plethora of applications such as mapping shipwrecks for archaeology, monitoring the health of coral reefs, detecting faults in off-shore oil rigs and pipelines, detection and identification of potential threats on the seabed, etc. Acoustic imaging is the most popular choice for underwater sensing. Underwater exploratory vehicles typically employ wide-aperture Sound Navigation and Ranging (SONAR) imaging sensors. Although their wide aperture enables scouring large volumes of water ahead of them for obstacles, the resulting images produced are blurry due to integration over the aperture. Performing 3D reconstruction from this blurry data is notoriously difficult. This challenging inverse problem is further exacerbated by the presence of speckle noise and reverberations. The state of the art methods in 3D reconstruction from sonar either require bulky and expensive matrix-arrays of sonar sensors or additional narrowaperture sensors. Due to its low footprint, the latter induces gaps between reconstructed scans. Avoiding such gaps requires slow and cumbersome scanning by the vehicles that carry the scanners. In this paper, we present two reconstruction methods enabling on-site 3D reconstruction from imaging sonars of any aperture. The first of these presents an elegant linear formulation of the problem, as a blind deconvolution with a spatially-varying kernel. The second method is a simple algorithmic approach for approximate reconstruction, using a non-linear formulation. We demonstrate that our simple approximation algorithms perform 3D reconstruction directly from the data recorded by wide-aperture systems thus eliminating the need for multiple sensors to be mounted on underwater vehicles for this purpose. Additionally, we observe that the wide aperture may be exploited to improve the coverage of the reconstructed samples (on the scanned object’s surface). We demonstrate the efficacy of our algorithms on simulated as well as real data acquired using two sensors and compare our work to the state of the art in sonar reconstruction. Finally, we show the employability of our reconstruction methods on field data gathered by an Autonomous Underwater Vehicle.

Three-dimensional reconstruction of underwater objects using wide-aperture imaging SONAR

This paper describes research performed in pursuit of autonomous swarm tactics for unmanned aerial vehicles (UAVs) as part of an internally funded effort at the Georgia Tech Research Institute (GTRI). Human engineered swarming tactics, reinforcement learned tactics, and aggressive flight maneuvers are explored. Aggressive flight maneuvers included autonomous descents, rolls, and an Immelmann maneuver. These efforts expand on preliminary control capabilities and are intended to enable groups of UAVs to act as a defensive swarm to counter aggressor swarms. All tactics have been validated in simulation using the GTRI-developed SCRIMMAGE [1], [2] simulation engine and were further proven in live-fly experiments throughout the duration of the ACTION effort, which ran from 2017-2019.

Aerial Combat Tactics In Overwhelming Numbers

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Kevin J. DeMarco

Author Tools

Chat about Author

Papers

Aerial Combat Tactics In Overwhelming Numbers