Robotics: Science and Systems XI

Long-horizon Robotic Search and Classification using Sampling-based Motion Planning

Geoffrey Hollinger


This paper presents the Rapidly-exploring Adaptive Search and Classification (ReASC) algorithm, a sampling-based algorithm for planning the trajectories of mobile robots performing real-time target search and classification tasks in the field. The proposed algorithm incrementally builds up a tree of solutions and evaluates the utility of each solution for identifying targets in an environment. An optimistic approximation for the classification utility is used, which reduces the computational cost of evaluating trajectories and makes real-time adaptive planning feasible. The proposed algorithm is tested on an autonomous aquatic vehicle and are shown to outperform myopic methods by up to 36% in a lake monitoring scenario.



    AUTHOR    = {Geoffrey Hollinger}, 
    TITLE     = {Long-horizon Robotic Search and Classification using Sampling-based Motion Planning}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2015}, 
    ADDRESS   = {Rome, Italy}, 
    MONTH     = {July},
    DOI       = {10.15607/RSS.2015.XI.010}