DUGi

Online Mapping and Motion Planning Under Uncertainty for Safe Navigation in Unknown Environments

http://dugi.udg.edu/item/http:@@@@hdl.handle.net@@10256@@22949

Safe autonomous navigation is an essential and challenging problem for robots operating in highly unstructured or completely unknown environments. Under these conditions, not only robotic systems must deal with limited localization information but also their maneuverability is constrained by their dynamics and often suffers from uncertainty. In order to cope with these constraints, this article proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety guarantees. The proposed approach deals with the motion, probabilistic safety, and online computation constraints by: 1) incrementally mapping the surroundings to build an uncertainty-aware representation of the environment and 2) iteratively (re)planning trajectories to goal that is kinodynamically feasible and probabilistically safe through a multilayered sampling-based planner in the belief space. In-depth empirical analyses illustrate some important properties of this approach, namely: 1) the multilayered planning strategy enables rapid exploration of the high-dimensional belief space while preserving asymptotic optimality and completeness guarantees and 2) the proposed routine for probabilistic collision checking results in tighter probability bounds in comparison to other uncertainty-aware planners in the literature. Furthermore, real-world in-water experimental evaluation on a nonholonomic torpedo-shaped autonomous underwater vehicle and simulated trials in an urban environment on an unmanned aerial vehicle demonstrate the efficacy of the method and its suitability for systems with limited onboard computational power. Note to Practitioners—Emergent robotic applications require operating in previously unmapped scenarios. This article presents a unified mapping–planning strategy that enables robots to navigate autonomously and safely in harsh environments

This work was supported in part by the School of Engineering and Physical Sciences (EPS), Heriot-Watt University, as part of the Centre for Doctoral Training (CDT) in Robotics and Autonomous Systems (Heriot-Watt University and The University of Edinburgh); in part by the Scottish Informatics and Computer Science Alliance (SICSA), ORCA Hub EPSRC (EP/R026173/1), and consortium partners; and in part by the EXCELLABUST and ARCHROV projects under Grant H2020-TWINN2015, CSA, ID:691980 and Grant DPI2014-57746-C3-3-R, respectively, for conducting the experiments in Section VII-A at the Computer Vision and Robotics Institute (VICOROB), University of Girona

IEEE