Towards mixed-initiative, multi-robot field experiments: Design, deployment, and lessons learned

With the advent of Autonomous Underwater Vehicles (AUVs) and other mobile platforms, marine robotics have had substantial impact on the oceanographic sciences. These systems have allowed scientists to collect data over temporal and spatial scales that would be logistically impossible or prohibitively expensive using traditional ship-based measurement techniques. Increased dependence of scientists on such robots has permeated scientific data gathering with future field campaigns involving these platforms as well as on entire infrastructure of people, processes and software, on shore and at sea. Recent field experiments carried out with a number of surface and underwater platforms give clues to how these technologies are coalescing and need to work together. We highlight one such confluence and describe a future trajectory of needs and desires for field experiments with autonomous marine robotic platforms. Our 2010 inter-disciplinary experiment in the Monterey Bay involved multiple platforms and collaborators with diverse science goals. One important goal was to enable situational awareness, planning and collaboration before, during and after this large-scale collaborative exercise. We present the overall view of the experiment and describe an important shore-side component, the Oceanographic Decision Support System (ODSS), its impact and future directions leveraging such technologies for field experiments.

[1]  David S. Wettergreen,et al.  Long-Distance Autonomous Survey and Mapping in the Robotic Investigation of Life in the Atacama Desert , 2008 .

[2]  R Sullivan,et al.  The Spirit Rover's Athena science investigation at Gusev Crater, Mars. , 2004, Science.

[3]  David M. Fratantoni,et al.  Multi-AUV Control and Adaptive Sampling in Monterey Bay , 2006, IEEE Journal of Oceanic Engineering.

[4]  J. Bresina,et al.  MAPGEN : mixed initiative planning and scheduling for the Mars '03 MER mission , 2003 .

[5]  Clodoaldo Robledo,et al.  Google Web Toolkit , 2012 .

[6]  Ari K. Jónsson,et al.  MAPGEN: Mixed-Initiative Planning and Scheduling for the Mars Exploration Rover Mission , 2004, IEEE Intell. Syst..

[7]  Frederic Py,et al.  A systematic agent framework for situated autonomous systems , 2010, AAMAS.

[8]  K. H. Low,et al.  Multi-robot adaptive exploration and mapping for environmental sensing applications , 2009 .

[9]  J. Bellingham,et al.  Autonomous Oceanographic Sampling Networks , 1993 .

[10]  Gaurav S. Sukhatme,et al.  Adaptive Sampling for Estimating a Scalar Field using a Robotic Boat and a Sensor Network , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[11]  Richard W. Hanson,et al.  Robotics in Hostile Environments , 1983 .

[12]  Terrence Fong,et al.  Robotic Follow-Up for Human Exploration , 2010 .

[13]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[14]  James G. Bellingham,et al.  Error Analysis and Sampling Strategy Design for Using Fixed or Mobile Platforms to Estimate Ocean Flux , 2010 .

[15]  James G. Bellingham,et al.  Progress toward autonomous ocean sampling networks , 2009 .

[16]  H. Zhang,et al.  OurOcean - An Integrated Solution to Ocean Monitoring and Forecasting , 2006, OCEANS 2006.

[17]  K. Rajan,et al.  Mobile autonomous process sampling within coastal ocean observing systems , 2010 .

[18]  Gaurav S. Sukhatme,et al.  Adaptive sampling for environmental robotics , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[19]  Gaurav S. Sukhatme,et al.  Simultaneous Tracking and Sampling of Dynamic Oceanographic Features with Autonomous Underwater Vehicles and Lagrangian Drifters , 2010, ISER.

[20]  Ari K. Jónsson,et al.  Activity Planning for the Mars Exploration Rovers , 2005, ICAPS.

[21]  Naomi Ehrich Leonard,et al.  Preparing to predict: The Second Autonomous Ocean Sampling Network (AOSN-II) experiment in the Monterey Bay , 2009 .

[22]  James G Bellingham,et al.  Design and Tests of an Adaptive Triggering Method for Capturing Peak Samples in a Thin Phytoplankton Layer by an Autonomous Underwater Vehicle , 2010, IEEE Journal of Oceanic Engineering.

[23]  Frederic Py,et al.  Adaptive Control for Autonomous Underwater Vehicles , 2008, AAAI.

[24]  James G. Bellingham Autonomous Ocean Sampling Networks , 2006 .