MallARD: An Autonomous Aquatic Surface Vehicle for Inspection and Monitoring of Wet Nuclear Storage Facilities

Abstract

Inspection and monitoring of wet nuclear storage facilities such as spent fuel pools or wet silos is performed for a variety of reasons, including nuclear security and characterisation of storage facilities prior to decommissioning. Until now such tasks have been performed by personnel or, if the risk to health is too high, avoided. Tasks are often repetitive, time-consuming and potentially dangerous, making them suited to being performed by an autonomous robot. Previous autonomous surface vehicles (ASVs) have been designed for operation in natural outdoor environments and lack the localisation and tracking accuracy necessary for operation in a wet nuclear storage facility. In this paper the environmental and operational conditions are analysed, applicable localisation technologies selected and a unique aquatic autonomous surface vehicle (ASV) is designed and constructed. The ASV developed is holonomic, uses a LiDAR for localisation and features a robust trajectory tracking controller. In a series of experiments the mean error between the present ASV’s planned path and the actual path is approximately 1 cm, which is two orders of magnitude lower than previous ASVs. As well as lab testing, the ASV has been used in two deployments, one of which was in an active spent fuel pool.

Model Identification of a Small Fully-Actuated Aquatic Surface Vehicle Using a Long Short-Term Memory Neural Network

“…However, this vehicle would not be suitable for use in a smaller confined environment, such as a nuclear fuel storage pool, where a vehicle must be able to control its path and position with much higher accuracy. The need for smaller surface vehicles with high tracking accuracy led researchers at the University of Manchester to develop MallARD (Small Autonomous Robotic Duck) [2]. MallARD is an ASV that was primarily designed to operate in spent nuclear fuel storage pools and achieves a control tracking accuracy of less than 1 cm using a 2D LiDAR as its main sensor.…”

Model Identification of a Small Omnidirectional Aquatic Surface Vehicle: a Practical Implementation

“…This paper presents a mathematical model of a small overactuated ASV, MallARD (sMall Autonomous Robotic Duck) [2], and provides a method of determining model coefficients that is both fast and practical. The model developed has three principal uses:…”

“…MallARD can be operated with varying levels of autonomy, from joypad operation to autonomous waypoint navigation with a closed loop trajectory tracking controller. A full description of the design is provided in [2].…”

“…In the present work, two different thrust allocation schemes are used. The first is a standard method used previously by the authors in [2], termed here as the MP (Moore-Penrose) allocation. For this allocation, individual thruster commands F 1 F 2 F 3 F 4 T are calculated by multiplying the vector of joypad commands F u F v F r T by the inverted thrust allocation matrix T † :…”

Design and Implementation of Autonomous Surface Vehicle for Inland Water

“…Autonomous surface vehicles (ASVs) have many applications ranging from civil to military purposes. Among the civil applications are environmental monitoring (Bayram et al, 2016;Hitz et al, 2017;Jung et al, 2017;Patel et al, 2019), water quality assessment (Ferri et al, 2014;Fornai et al, 2016;Hitz et al, 2012), inspection of wet nuclear storage facilities (Groves et al, 2019), bridge inspection (Murphy et al, 2011) and oceanographic research (Caccia et al, 2005). In the military domain, ASVs are used for patrolling shorelines or harbors (Wolf et al, 2017) and maritime interdiction (See, 2017).…”