Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Real-Time Avoidance of Ionising Radiation Using Layered Costmaps for Mobile Robots
AU - West, Andrew
AU - Wright, Thomas
AU - Tsitsimpelis, Ioannis
AU - Groves, Keir
AU - Joyce, Malcolm J.
AU - Lennox, Barry
PY - 2022/3/17
Y1 - 2022/3/17
N2 - Humans in hazardous environments take actions to reduce unnecessary risk, including limiting exposure to radioactive materials where ionising radiation can be a threat to human health. Robots can adopt the same approach of risk avoidance to minimise exposure to radiation, therefore limiting damage to electronics and materials. Reducing a robot’s exposure to radiation results in longer operational lifetime and better return on investment for nuclear sector stakeholders. This work achieves radiation avoidance through the use of layered costmaps, to inform path planning algorithms of this additional risk. Interpolation of radiation observations into the configuration space of the robot is accomplished using an inverse distance weighting approach. This technique was successfully demonstrated using an unmanned ground vehicle running the Robot Operating System equipped with compatible gamma radiation sensors, both in simulation and in real-world mock inspection missions, where the vehicle was exposed to radioactive materials in Lancaster University’s Neutron Laboratory. The addition of radiation avoidance functionality was shown to reduce total accumulated dose to background levels in real-world deployment and up to a factor of 10 in simulation.
AB - Humans in hazardous environments take actions to reduce unnecessary risk, including limiting exposure to radioactive materials where ionising radiation can be a threat to human health. Robots can adopt the same approach of risk avoidance to minimise exposure to radiation, therefore limiting damage to electronics and materials. Reducing a robot’s exposure to radiation results in longer operational lifetime and better return on investment for nuclear sector stakeholders. This work achieves radiation avoidance through the use of layered costmaps, to inform path planning algorithms of this additional risk. Interpolation of radiation observations into the configuration space of the robot is accomplished using an inverse distance weighting approach. This technique was successfully demonstrated using an unmanned ground vehicle running the Robot Operating System equipped with compatible gamma radiation sensors, both in simulation and in real-world mock inspection missions, where the vehicle was exposed to radioactive materials in Lancaster University’s Neutron Laboratory. The addition of radiation avoidance functionality was shown to reduce total accumulated dose to background levels in real-world deployment and up to a factor of 10 in simulation.
KW - Robotics and AI
KW - nuclear
KW - radiation
KW - inspection
KW - autonomy
KW - ROS
KW - field robotics
KW - ALARP
KW - ALARA
U2 - 10.3389/frobt.2022.862067
DO - 10.3389/frobt.2022.862067
M3 - Journal article
VL - 9
JO - Frontiers in Robotics and AI
JF - Frontiers in Robotics and AI
SN - 2296-9144
M1 - 862067
ER -