Autonomous Robot Navigation with Rich Information Mapping in Nuclear Storage Environments
This work addresses the problem of reducing human exposure to radiation in nuclear inspections, though it is incremental as it builds on existing autonomous navigation methods for a specific domain.
The paper tackles autonomous robot navigation for inspection tasks in nuclear storage environments by developing a rich information map that includes obstacle locations and object details, enabling an unmanned ground vehicle to perform tasks like container counting and gamma measurements in a simulated testbed with uranium hexafluoride mock-ups.
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.