Adaptive sampling algorithms for multiple autonomous underwater vehicles

Sampling is a critical problem in the observation of underwater phenomena using single or multiple AUV platforms. The determination of optimal paths and sampling strategies that effectively utilize available resources is critical to these missions. Recent work performed jointly at RPI and AUSI on the development of adaptive sampling algorithms (ASA) utilizes information measures, estimation theory, and potential fields to direct the robots to the locations in space most likely to yield information about the sensed field variable of interest. Typical sensory information can consist of spatial distribution of one or more field variables, such as salinity, dissolved oxygen, temperature, current, etc. In order to test our algorithms we have created the MATCON simulation environment, an underwater experimental platform using solar AUVs, and a land-based experimental testbed using inexpensive wheeled robots.

[1]  John T. Wen,et al.  Nonholonomic path-planning with obstacle avoidance: a path-space approach , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[2]  F. Lewis.  Optimal Estimation: With an Introduction to Stochastic Control Theory , 1986 .

[3]  John T. Wen,et al.  A unifying passivity framework for network flow control , 2004, IEEE Transactions on Automatic Control.

[4]  Deborah Estrin,et al.  An energy-efficient MAC protocol for wireless sensor networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[5]  R. Blidberg,et al.  Autonomous undersea systems network (AUSNet) - protocols to support ad-hoc AUV communications , 2004, 2004 IEEE/OES Autonomous Underwater Vehicles (IEEE Cat. No.04CH37578).

[6]  J. Meyer-Hilberg,et al.  High accuracy navigation and landing system using GPS/IMU system integration , 1994, IEEE Aerospace and Electronic Systems Magazine.

[7]  Deborah Estrin,et al.  Geographical and Energy Aware Routing: a recursive data dissemination protocol for wireless sensor networks , 2002 .

[8]  G. Swaminathan.  Robot Motion Planning , 2006 .

[9]  Wolfram Burgard,et al.  Collaborative multi-robot exploration , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[10]  Allan R. Robinson.  Forecasting and simulating coastal ocean processes and variabilities with the Harvard Ocean Prediction System , 1999 .

[11]  A.C. Sanderson,et al.  Autonomous systems monitoring and control (ASMAC) - an AUV fleet controller , 2004, 2004 IEEE/OES Autonomous Underwater Vehicles (IEEE Cat. No.04CH37578).

[12]  M. D. Ageev,et al.  Results of the evaluation and testing of the solar powered AUV and its subsystems , 2002, Proceedings of the 2002 Workshop on Autonomous Underwater Vehicles, 2002..

[13]  Lynne E. Parker,et al.  Heterogeneous mobile sensor net deployment using robot herding and line-of-sight formations , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[14]  Mario F. M. Campos,et al.  Decentralized motion planning for multiple robots subject to sensing and communication constraints , 2003 .

[15]  D. Menemenlis.  Inverse Modeling of the Ocean and Atmosphere , 2002 .

[16]  Arthur C. Sanderson.  Multirobot Navigation Using Cooperative Teams , 1998, DARS.

[17]  Eric Brassart,et al.  A communication strategy for cooperative robots , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

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

[19]  Gaurav S. Sukhatme,et al.  An Incremental Self-Deployment Algorithm for Mobile Sensor Networks , 2002, Auton. Robots.

[20]  R. Blidberg,et al.  The AUSI/IMTP solar powered autonomous undersea vehicle , 1998, IEEE Oceanic Engineering Society. OCEANS'98. Conference Proceedings (Cat. No.98CH36259).

[21]  Fernando Paganini,et al.  Internet congestion control , 2002 .

[22]  Harvey L. Davies,et al.  NetCDF User's Guide - An Interface for Data Access Version , 1996 .

[23]  A.H. Haddad,et al.  Applied optimal estimation , 1976, Proceedings of the IEEE.

[24]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[25]  S. Glenn,et al.  Factors Influencing Model Skill for Hindcasting Shallow Water Currents during Hurricane Andrew , 1998 .

[26]  Thomas Sugar,et al.  Control of cooperating mobile manipulators , 2002, IEEE Trans. Robotics Autom..

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

[28]  François Michaud,et al.  Sharing charging stations for long-term activity of autonomous robots , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  Daniela Rus,et al.  Reactive Behavior in Self-reconfiguring Sensor Networks , 2002 .

[30]  Panganamala Ramana Kumar,et al.  RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .

[31]  Zygmunt J. Haas,et al.  A new routing protocol for the reconfigurable wireless networks , 1997, Proceedings of ICUPC 97 - 6th International Conference on Universal Personal Communications.

[32]  S. Glenn,et al.  Shallow water currents during Hurricane Andrew , 1999 .

[33]  Gaurav S. Sukhatme,et al.  Mobile Sensor Network Deployment using Potential Fields : A Distributed , Scalable Solution to the Area Coverage Problem , 2002 .

[34]  John J. Leonard,et al.  Cooperative concurrent mapping and localization , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[35]  Douglas W. Gage,et al.  Command Control for Many-Robot Systems , 1992 .

[36]  Leonidas J. Guibas,et al.  Collaborative signal and information processing: an information-directed approach , 2003 .

[37]  Arthur C. Sanderson,et al.  Robotic deployment of sensor networks using potential fields , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[38]  Stergios I. Roumeliotis,et al.  Weighted range sensor matching algorithms for mobile robot displacement estimation , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[39]  Q. Wang,et al.  UAV NAVIGATION BASED ON PARALLEL EXTENDED KALMAN FILTER , 2000 .

[40]  D. Richard Blidberg.  Solar Powered Autonomous Undersea Vehicles , 1998 .

[41]  Gaurav S. Sukhatme,et al.  Localization for Mobile Robot Teams: A Maximum Likelihood Approach , 2001 .

[42]  Christopher N. K. Mooers,et al.  Dynamical forecasting and dynamical interpolation: an experiment in the California Current , 1986 .

[43]  Brian Yamauchi,et al.  A frontier-based approach for autonomous exploration , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[44]  Richard M. Stanley.  Optimal Estimation With an Introduction to Stochastic Control , 1988 .

[45]  Peter Cornillon,et al.  Observations of Gulf Stream ring 83‐E and their interpretation using feature models , 1990 .

[46]  Wolfram Burgard,et al.  Coordination for Multi-Robot Exploration and Mapping , 2000, AAAI/IAAI.