Underwater confined space mapping by resource‐constrained autonomous vehicle

Abstract

For marine industrial inspection, archaeology, and geological formation study, the ability to map unknown underwater enclosed and confined spaces is desirable and well suited for robotic vehicles. To date, there are few solutions thoroughly tested in the field designed to perform this specific task, none of which operate autonomously. With a small, low‐cost biomimetic platform known as the U‐CAT, we developed a mapping‐mission software architecture in which the vehicle executes three key sensor‐based reactive stages: entering, exploring, and exiting. Encapsulated in the exploring stage are several state‐defined navigation strategies, called patterns, which were designed and initially tested in simulation. The results of simulation work informed the selection of two patterns that were executed in field trials at a submerged building in Rummu Quarry Lake, Estonia, as part of several full mapping missions. Over the course of these trials, the vehicle was capable of observing the majority (78–97%) of 49.9 explorable square meters within 7 minutes. Based on these results, we demonstrate the capability of a low‐cost and resource‐constrained vehicle to perform confined space mapping under sensor uncertainty. Further, the observations made by the vehicle are shown to be suitable for a target site reconstruction and analysis in postprocessing, which is the intended outcome of this type of mission in practical applications.

Diver tracking in open waters: A low‐cost approach based on visual and acoustic sensor fusion

“…U-CAT is a low-cost, resource constrained robot, that has a small size allowing it to manoeuver in confined environments (Preston et al, 2018). Its design is user-friendly, and its weight does not exceed 19 kilograms, which allows for quick and easy deployment.…”

Guidance for Autonomous Underwater Vehicles in Confined Semistructured Environments

“…The volume of works in the literature addressing the exploration of confined flooded spaces such as caves and tunnels, relating mostly to marine archaeology and geological prospecting [ 26 ], narrows down dramatically, which serves as an indicator of the bigger technical challenges posed by such environments. These challenges reflect on three main aspects of the UVs tailored for them [ 27 ]: their constrained mechanical design relating both shape and size, and their necessary high maneuverability; their difficulties in acquiring detailed positional information; and their required autonomy.…”

“…The need for high maneuverability and positional control has been signaled in the literature with the departure from the forward-moving torpedo-shaped submersibles typical from open water exploration [ 20 , 21 ] to, often, ellipsoidal [ 28 ], spherical [ 23 ], or cubical [ 22 ] shapes, able to perform movements in six degrees of freedom (DOF) and with hovering capabilities: hence their generic name, hovering AUVs (HAUVs). Following this line of work, the DEPTHX UV was carried out the exploration and mapping of hot springs [ 28 ]; the submersibles Sentry [ 29 ] and Mesobot [ 22 ] performed oceanographic surveys and observations of marine life; and U-CAT [ 26 ], IMOTUS-1 [ 23 ], and and SUNFISH [ 27 ] were used to autonomously explore and map archaeological sites, storage tanks, and underwater caves, respectively. These three latter works, which can be considered the closest existing works to our UX-1 robot not only in terms of their field of application but also regarding their guidance, are discussed further below.…”

“…Existing UVs for confined flooded spaces base therefore their positioning on inputs on their relative pose captured on-board, either with respect to their position in previous time steps like the inertial measurement units (IMUs), fiber-optic gyroscopes (FOGs), and Doppler velocity logs (DVLs) or with respect to their environment, like the sonar [ 23 , 26 , 27 ]. These inputs, partial and noisy, in the case of sonar because acoustic signals suffer from multipath in narrow and intricate pathways, are integrated using accumulative processing such as dead reckoning, Kalman filters, or Simultaneous Localization and Mapping (SLAM) techniques [ 21 , 26 ].…”

“…Second and foremost, given the frequent clutter of the consider environments and their extension the risk of potential entanglement in protruding elements such as ladders, cranks and levers, or simply rocks is unaffordable. This lack of communication leaves the UV alone with its autonomous capabilities, both in terms of sensing and environmental mapping and of decision making [ 26 ]. The autonomy of a vehicle, at least in terms of displacement decisions, is greatly embodied in the concept of path planning [ 31 ], i.e., deciding which sequence of locations in the space to follow in order to reach a determined target location, which shows a tight interdependence with the representation of the known environment of the robot in the form of occupancy maps [ 26 ], ensembles of features [ 23 ], or different but analogous structures [ 27 , 32 ].…”