<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Devin K. Grady</style></author><author><style face="normal" font="default" size="100%">Moll, Mark</style></author><author><style face="normal" font="default" size="100%">Chinmay Hegde</style></author><author><style face="normal" font="default" size="100%">Aswin C. Sankaranarayanan</style></author><author><style face="normal" font="default" size="100%">Richard G. Baraniuk</style></author><author><style face="normal" font="default" size="100%">Kavraki, Lydia E.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Look Before You Leap: Predictive Sensing and Opportunistic Navigation</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop on Progress and Open Problems in Motion Planning at the IEEE/RSJ Conf. on Intelligent Robots and Systems</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">distributed</style></keyword><keyword><style  face="normal" font="default" size="100%">kavrakilab</style></keyword><keyword><style  face="normal" font="default" size="100%">multi-robot</style></keyword><keyword><style  face="normal" font="default" size="100%">opportunistic</style></keyword><keyword><style  face="normal" font="default" size="100%">planning</style></keyword><keyword><style  face="normal" font="default" size="100%">safety</style></keyword><keyword><style  face="normal" font="default" size="100%">sensing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">25/09/2011</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">San Francisco</style></pub-location><abstract><style face="normal" font="default" size="100%">This paper describes a novel method for identifying multiple targets with multiple robots in a partially known environment. Two main issues are addressed. The first relates to the use of motion planning algorithms to determine whether robots can reach ``good'' positions that offer the most informative measurements. The second concerns the use of predictive sensing to decide where sensor measurements should be taken. The problem is formulated similar to a next-best-view problem with differential constraints on the robots' motion, with additional layers of complexity due to visual occlusions as well as navigational obstacles. We propose a new distributed sensing strategy that exploits the structure of image manifolds to predict the utility of the measurements at a given position. This information is encoded in a cost map that guides a motion planning algorithm. Coordination among robots is achieved by incorporating additional information in each robot's cost map. A range of simulations indicates that our approach outperforms current approaches and demonstrates the advantages of predictive sensing and accounting for reachability constraints.</style></abstract></record></records></xml>
