Autonomous Robot Navigation with Rich Information Mapping in Nuclear Storage Environments

Ahstract-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.

I. Introduction

There is an ever-increasing need to design and develop reliable robotics technologies in nuclear environments as elevated radiation levels pose dangerous conditions for humans whose jobs require them to be present in these places. In order to meet this need, many organizations are taking on initiatives to accelerate the development of autonomous robots for nuclear applications. For instance, the International Atomic Energy Agency (IAEA) Robotics Challenge held in November 2017 was aimed at developing autonomous robots to conduct in-field inspections to ensure effective nuclear safeguards. As part of of IAEA's mission, inspectors routinely enter storage facilities to count containers, record their ID tags and perform gamma measurements. 2017 IAEA Robotics Challenge required participating teams to develop autonomous robots to perform these tasks in simulated environments.