Look Before You Leap: Predictive Sensing and Opportunistic Navigation

D. K. Grady, M. Moll, C. Hegde, A. C. Sankaranarayanan, R. G. Baraniuk, and L. E. Kavraki, “Look Before You Leap: Predictive Sensing and Opportunistic Navigation,” in Workshop on Progress and Open Problems in Motion Planning at the IEEE/RSJ Conf. on Intelligent Robots and Systems, San Francisco, 2011.

Abstract

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.