Autonomous Underwater Manipulation: Current Trends in Dynamics, Control, Planning, Perception, and Future Directions

Details

• Journal Title:
  Current Robotics Reports
• Personal Author:
  Morgan, Edward ; Carlucho, Ignacio ; Ard, William ; Barbalata, Corina
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research)
• Description:
  Research in underwater manipulation has mostly focused on solving individual parts of the manipulation challenge; however, we believe a systemic approach needs to be taken to achieve full autonomy. With this survey, we aim to provide a review of the different dynamic modeling, control, motion planning, and perception methodologies presented in the literature, and, more importantly, we intend to highlight the necessary steps that need to be taken to achieve fully autonomous underwater manipulation. Achieving autonomous manipulation in underwater environments is a complex and multi-disciplinary challenge. Recent works have focused on moving from simulation-based environments to experimental validation of the proposed methods. Furthermore, the advancements of machine learning have been making an impact in the underwater manipulation, data-driven strategies playing a central role in the last years developments. We present an overview of the current trends in the area of autonomous underwater manipulation. First, we provide a review of state-of-the-art algorithms developed in the area of dynamic modeling, control, motion planning, and perception. Second, we discuss the limitations of the current systems and present possible avenues to obtain robust autonomous manipulation.
• Keywords:
  Automation Autonomous Robots Autonomous Underwater Vehicles Robotics Autonomous Robots Autonomous Underwater Vehicles Autonomy Control Dynamic Modeling Perception Planning Underwater Manipulation

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• Source:
  Curr Robot Rep 3, 187–198 (2022)
• DOI:
  https://doi.org/10.1007/s43154-022-00089-2
• Format:
  PDF
• Document Type:
  Journal Article
• Funding:
  Grant no. NA21OAR0110196
• Rights Information:
  Accepted Manuscript
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:da82ee2d97b407a8104a928e94ab3bc3a7901f4319628e10ce8ef959f4bbffb1c78025abed7ced4b312f272c0720884ec2a764eff556c791d269755e3f488ecd
• Download URL:
  https://repository.library.noaa.gov/view/noaa/70973/noaa_70973_DS1.pdf
• File Type:
  [PDF - 1.22 MB ]