Collaborative Sampling Using Heterogeneous Marine Robots Driven by Visual Cues

Sandeep Manjanna, Johanna Hansen, Alberto Quattrini Li, Ioannis Rekleitis, Gregory Dudek
In Canadian Conference on Computer and Robot Vision (CRV) 2017.


Abstract: This paper addresses distributed data sampling in marine environments using robotic devices. We present a method to strategically sample locally observable features using two classes of sensor platforms. Our system consists of a sophisticated autonomous surface vehicle (ASV) which strategically samples based on information provided by a team of inexpensive sensor nodes. The sensor nodes effectively extend the observational capabilities of the vehicle by capturing georeferenced samples from disparate and moving points across the region. The ASV uses this information, along with its own observations, to plan a path so as to sample points which it expects to be particularly informative. We compare our approach to a traditional exhaustive survey approach and show that we are able to effectively represent a region with less energy expenditure. Experiments in simulation and using the real vehicles validate our approach.



  author       = {Sandeep Manjanna and Johanna Hansen and Alberto Quattrini
		 Li and Ioannis Rekleitis and Gregory Dudek},
  title        = {Collaborative Sampling Using Heterogeneous Marine Robots
		 Driven by Visual Cues},
  booktitle    = {Canadian Conference on Computer and Robot Vision (CRV)},
  year	       = {2017},
  pages        = {87-94},
  month        = {May},
  address      = {Edmonton, AB, Canada}

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