Characterisation of Remote Nuclear Environments

  • Thomas Wright

Student thesis: Phd


Many legacy nuclear facilities exist with the number of such facilities due to increase in the future. For a variety of reasons, some of these facilities have poorly documented blueprints and floor plans. This has led to many areas within such facilities being left unexplored and in an unknown state for some considerable time. The risk to health that these areas might pose has in some cases precluded human exploration and facilities have been maintained in a containment state for many years. However, in more recent years there has been a move to decommission such facilities. The change of strategy from containment to decommissioning will require knowledge of what it is that needs to be decommissioned. It is hoped that an autonomous or semi- autonomous robotic solution can satisfy the requirement. For successful mapping of such environments, it is required that the robot is capable of producing complete scans of the world around it. As it moves through the environment the robot will not only need to map the presence, type and extent of radioactivity, but do so in a way that is economical from the perspective of battery life. Additionally, the presence of radioactivity presents a threat to the robot electronics. Exposure to radiation will be necessary but should be minimised to prolong the functional life of the robot. Some tethered robots have been developed for such applications, but these can cause issues such as snagging or the tether inadvertently spreading contamination, due to being dragged along the floor. Nuclear environments have very unique challenges, due to the radiation. Alpha and beta radiation have a short emission distance and therefore cannot be detected until the robot is in very close proximity. Although the robot will not become disabled by these forms of radiation, it may become contaminated which is undesirable. Radiation from gamma sources can be detected at range, however pinpointing a source requires sensors to be taken close to the emitter, which has adverse effects on the robot's electronics, for example gamma radiation damages silicon based electronics. Anything entering these environments is deemed to be contaminated and will eventually require disposal. Consequently the number of entries made should ideally be minimised, to reduce the production and spread of potential waste/contamination. This thesis presents results from an investigation of ways to provide complete scans of an environment with novel algorithms which take advantage of common features found in industrial environments and thereby allow for gaps in the data set to be detected. From this data it is then possible to calculate a minimum set of way points required to be visited to allow for all of the gaps to be filled in. This is achieved by taking into account the sensor's parameters such as minimum and maximum sensor range, angle of incidence and optimal sensor distance, along with robot and environmental factors. An investigation into appropriate exploration strategies has been undertaken looking at the ways in which gamma radiation sources affect the coverage of an environment. It has discovered undesired behaviours exhibited by the robot when radiation is present. To overcome these behaviours a novel movement strategy has been presented, along with a set of linear and binary battery modifiers, which adapt common movement strategies to help improve overall coverage of an unknown environment. Collaborative exploration of unknown environments has also been investigated, looking into the specific challenges radiation and contamination offer. This work has presented new ways of allowing multiple robots to independently explore an environment, sharing knowledge as they go, whilst safely exploring unknown hazardous space where a robot may be lost due to contamination or radiation damage.
Date of Award31 Dec 2018
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorRoelof Van Silfhout (Supervisor) & Barry Lennox (Supervisor)


  • Collaborative Robotics
  • Void Detection
  • Extreme Environments
  • Robotics
  • Coverage
  • Hazardous Environments
  • Path Planning

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