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Human Assisted Robotic Team Campaigns for Aquatic Monitoring

  • Author(s): Singh, Amarjeet
  • Batalin, Maxim
  • Stealey, Michael
  • Zhang, Bin
  • Dhariwal, Amit
  • Stauffer, Beth
  • Moorthi, Stefanie
  • Oberg, Carl
  • de Menezes Pereira, Arvind Antonio
  • Chen, Victor
  • Lam, Yeung
  • Caron, David
  • Hansen, Mark
  • Kaiser, W J
  • Sukhatme, Gaurav
  • et al.
Abstract

Large-scale environmental sensing, e.g., understanding microbial processes in an aquatic ecosystem, requires coordination across a multidisciplinary team of experts working closely with a robotic sensing and sampling system. We describe a human-robot team that conducted an aquatic sampling campaign in Lake Fulmor, San Jacinto Mountains Reserve, California during three consecutive site visits (May 9–11, June 19–22, and August 28–31, 2006). The goal of the campaign was to study the behavior of phytoplankton in the lake and their relationship to the underlying physical, chemical, and biological parameters. Phytoplankton form the largest source of oxygen and the foundation of the food web in most aquatic ecosystems. The reported campaign consisted of three system deployments spanning four months. The robotic system consisted of two subsystems—NAMOS (networked aquatic microbial observing systems) comprised of a robotic boat and static buoys, and NIMS-RD (rapidly deployable networked infomechanical systems) comprised of an infrastructure-supported tethered robotic system capable of high-resolution sampling in a two-dimensional cross section (vertical plane) of the lake. The multidisciplinary human team consisted of 25 investigators from robotics, computer science, engineering, biology, and statistics.We describe the lake profiling campaign requirements, the robotic systems assisted by a human team to perform high fidelity sampling, and the sensing devices used during the campaign to observe several environmental parameters. We discuss measures taken to ensure system robustness and quality of the collected data. Finally, we present an analysis of the data collected by iteratively adapting our experiment design to the observations in the sampled environment. We conclude with the plans for future deployments.

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