Robotic Exploration of an Unknown Nuclear Environment Using Radiation Informed Autonomous Navigation

Abstract

This paper describes a novel autonomous ground vehicle that is designed for exploring unknown environments which contain sources of ionising radiation, such as might be found in a nuclear disaster site or a legacy nuclear facility. While exploring the environment, it is important that the robot avoids radiation hot spots to minimise breakdowns. Broken down robots present a real problem: they not only cause the mission to fail but they can block access routes for future missions. Until now, such robots have had no autonomous gamma radiation avoidance capabilities. New software algorithms are presented that allow radiation measurements to be converted into a format in which they can be integrated into the robot’s navigation system so that it can actively avoid receiving a high radiation dose during a mission. An unmanned ground vehicle was fitted with a gamma radiation detector and an autonomous navigation package that included the new radiation avoidance software. The full system was evaluated experimentally in a complex semi-structured environment that contained two radiation sources. In the experiment, the robot successfully identified both sources and avoided areas that were found to have high levels of radiation while navigating between user defined waypoints. This advancement in the stateoftheart has the potential to deliver real benefit to the nuclear industry, in terms of both increased chance of mission success and reduction of the reliance on human operatives to perform tasks in dangerous radiation environments.

Real-Time Avoidance of Ionising Radiation Using Layered Costmaps for Mobile Robots

“…Access to suitable ionising radiation sources can be prohibitive for research experimentation due to the availability of facilities and safety issues. Previous studies have used proxies for radiation sources, such as radio emitters ( Groves et al, 2021 ). However, in the current work, experiments are performed in both simulation and in an active environment with real ionising radiation sources.…”

“…Radiation-hardened electronic components offer higher TID performance, but can be three orders of magnitude more expensive than their commercial off-the-shelf (COTS) equivalents ( Merl and Graham, 2016 ) and may fall short of the required computational resources used in many modern robotic and AI solutions. Autonomous behaviours such as collision avoidance, mapping, path planning, and object recognition ( Tsitsimpelis et al, 2019 ; Schneider and Wildermuth, 2011 ; Groves et al, 2021 ) are becoming reliant on more powerful algorithms and even specialist GPU acceleration hardware, particularly for machine learning and vision processing. Furthermore, the UK nuclear sector has expressed a preference for COTS parts and components over bespoke systems to reduce cost and time to deployment and provide better technology readiness levels and evidence of continued operation ( Smith et al, 2020 ).…”

The Operation of UAV Propulsion Motors in the Presence of High External Magnetic Fields

“…If the high magnetic fields are limited to specific areas or time periods (for example under fault conditions), then UAV flights may be feasible. Areas of high field strength could be treated as environmental obstacles in the same way that nuclear inspection robots treat radiation [28].…”

Path Planning for Autonomous Mobile Robots: A Review

“…One of them is the solar radiation [ 66 , 70 ], which can be modeled as a dynamic function [ 71 ]. Groves et al [ 72 ] mapped other kinds of radiation that may harm the robot, as in scenarios of nuclear dismantlement. Moreover, the concept of risk can be of high importance to prevent the robot from getting into a dangerous situation [ 73 ], such as increasing the cost with the proximity to obstacles [ 74 ].…”