ICRA 2011 Paper Abstract


Paper TuP211.5

Hummel, Robert (MIT), Poduri, Sameera (University of Southern California), Hover, Franz (MIT), Mitra, Urbashi (University of Southern California), Sukhatme, Gaurav (University of Southern California)

Mission Design for Compressive Sensing with Mobile Robots

Scheduled for presentation during the Regular Sessions "Marine and Underwater Robotics II" (TuP211), Tuesday, May 10, 2011, 16:25−16:40, Room 5F

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on April 2, 2020

Keywords Marine Robotics, Autonomous Agents, Motion and Path Planning


This paper considers mission design strategies for mobile robots whose task is to perform spatial sampling of a static environmental field, in the framework of compressive sensing. According to this theory, we can reconstruct compressible fields using O(log(n)) nonadaptive measurements (where n is the number of sites of the spatial domain), in a basis that is "incoherent" to the representation basis; random uncorrelated measurements satisfy this incoherence requirement. Because an autonomous vehicle is kinematically constrained and has finite energy and communication resources, it is an open question how to best design missions for compressive sensing reconstruction. We compare a two-dimensional random walk, a TSP approximation to pass through random points, and a randomized boustrophedon (lawnmower) strategy. Not unexpectedly, all three approaches can yield comparable reconstruction performance if the planning horizons are long enough; if planning occurs only over short time scales, the random walk will have an advantage.



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