Autonomous Robot Navigation with Rich Information Mapping in Nuclear Storage Environments

Abstract

This paper presents our approach to develop a method for an unmanned ground vehicle (UGV) to perform inspection tasks in nuclear environments using rich information maps. To reduce inspectors exposure to elevated radiation levels, an autonomous navigation framework for the UGV has been developed to perform routine inspections such as counting containers, recording their ID tags and performing gamma measurements on some of them. In order to achieve autonomy, a rich information map is generated which includes not only the 2D global cost map consisting of obstacle locations for path planning, but also the location and orientation information for the objects of interest from the inspector's perspective. The UGV's autonomy framework utilizes this information to prioritize locations to navigate to perform the inspections. In this paper, we present our method of generating this rich information map, originally developed to meet the requirements of the International Atomic Energy Agency (IAEA) Robotics Challenge. We demonstrate the performance of our method in a simulated testbed environment containing uranium hexafluoride (UF6) storage container mock ups.

An automated heterogeneous robotic system for radiation surveys: Design and field testing

“…The presented solution is, however, suitable for obstacle‐free areas only. The same UGV platform was deployed in a nuclear storage facility to perform inspections (Wang et al, 2018). In such a GPS‐denied environment, localization embodies the essential task; thus, a light detection and ranging (LiDAR) sensor is utilized to execute simultaneous localization and mapping (SLAM), facilitating navigation inside an unknown territory.…”

Using an Automated Heterogeneous Robotic System for Radiation Surveys

“…The presented solution is, however, suitable for obstacle-free and non-complex areas only. The same UGV platform was deployed in a nuclear storage facility to perform inspections (Wang et al, 2018). In such a GPS-denied environment, localization represents the essential task.…”

SLAM-OR: Simultaneous Localization, Mapping and Object Recognition Using Video Sensors Data in Open Environments from the Sparse Points Cloud

“…In order to find correspondence between existing objects and temporary objects authors used Kd-Tree. The similar approaches that incorporates RGB-D sensor, SLAM and YOLO is proposed in [ 52 ]. Paper presents approach to develop a method for an unmanned ground vehicle to perform inspection tasks in nuclear environments using rich information maps.…”

“…In this paper, we propose a novel approach that enables simultaneous localization, mapping and objects recognition using visual sensors data in open environments that is capable to work on sparse data point clouds. Our approach is most similar to [ 51 , 52 ]; however, we do not use RGB-D sensors, rather monocular RGB sensor. In the proposed algorithm the ORB-SLAM uses the current and previous monocular visual sensors video frame to determine observer position and to determine a cloud of points that represent objects in the environment, while the deep neural network uses the current frame to detect and recognize objects.…”