Mission Design for Compressive Sensing with Mobile Robots
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 1; 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 CS 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.
Robert Hummel Sameera Poduri Franz Hover Urbashi Mitra Guarav Sukhatme
Department of Mechanical Engineering at the Massachusetts Institute of Technology,Cambridge,MA 02139 Computer Science Department at the University of Southern California,Los Angeles,CA 90089 USA Department of Electrical Engineering at the University of Southern California,Los Angeles,CA 90089 U Department of Electrical Engineering and the Department of Computer Science at the University of Sou
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
2362-2367
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）