# Autonomous Robot Navigation with Rich Information Mapping in Nuclear Storage Environments

## TL;DR

This paper introduces an autonomous UGV system for nuclear storage inspections that uses rich information maps combining obstacle data and object details to enhance navigation and inspection tasks, reducing radiation exposure.

## Contribution

A novel method for generating rich information maps that integrate obstacle and object data for autonomous nuclear environment inspections.

## Key findings

- Successful simulation of the method in a nuclear storage environment
- Effective prioritization of inspection locations based on rich information maps
- Enhanced safety and efficiency in nuclear storage inspections

## 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.