DOE OSTI2019
Robotic systems are increasingly used for inspection, maintenance, and disaster assistance for nuclear power plants. These robots utilize increasingly high levels of autonomy to operate in environments that are unsafe for humans to operate the robot in or require more precise control over the robot than a human can offer. In these cases, the robot must include perception and computation components to autonomously sense and navigate through the environment to fulfill its mission. This difficult task is complicated by radiation interfering with sensors' operation. The impact of this interference is largely unexplored, as existing works on radiation interference on sensors focus on estimating the incident radiation, rather than how it impacts a robotic system using the sensor. In this work, we seek to quantify and describe the impact of radiation on cameras, and how the radiation affects robotic performance. In this paper we study cameras that are suitable for small, lightweight unmanned aerial vehicles (UAVs) that are increasingly deployed to fly in confined areas in emergencies, where the surrounding environment is unsafe for workers to operate. These small UAVs lack the payload capacity to carry significant radiation shielding, so any onboard sensors will be affected by radiation. In the case of cameras, this radiation appears as noise in the camera imagery. This noise will interfere with the UAV's operation, by obscuring obstacles and interfering with localization and optical flow algorithms that detect detailed features in the environment. To date, there is a lack of information on how the radiation noise in the camera image impacts the overall robot system performance Existing work focuses on estimating the amount of incident radiation by measuring the corruption in the images. However, these works suffer several shortcomings for robotic systems. First, a robot must be able to successfully navigate its environment during a successful mission, and merely quantifying the amount of radiation noise in an image does not adequately explain how it may interfere with higher level navigation and perception tasks. Second, radiation estimation procedures rely on focusing the camera on a known and static background. This is unsuitable for robotic exploration tasks, where the robot frequently moves around an unknown environment that may be dynamically changing. Our work contains two contributions that address these shortcomings. First, we conduct a detailed analysis of the noise radiation causes in cameras by exposing cameras that are commonly used on UAVs [e.g., small form factor complementary metal-oxide-semiconductor (CMOS) cameras] to varying levels of radiation that may be present in emergency scenarios. From this exposure, we quantify not only how much noise is present, but quantify its appearance. By quantifying the appearance and amount of noise in a camera image, researchers can design mitigation strategies for the corruption, so that their robotic systems can operate in these challenging environments. Second, we demonstrate how to leverage this experimental data in robotic simulations. Using data from the first set of experiments allows us to test and verify the robots in simulation. Using simulators allows us to verify the robots will perform as expected during critical times without needing expensive test equipment or damaging valuable robot prototypes. Other researchers using robots for exploration of irradiated environments will benefit from our research by incorporating similar noise models in their research. 1 Southwest Research Institute{sup R} is a registered trademark of Southwest Research Institute in the United States and/or other countries. This will increase the reliability of robotic systems prior to deployment in emergency situations, which will in turn increase their effectiveness and speed the development of robots that would otherwise need expensive and time-consuming test procedures to validate. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗