Coverage Path Planning with Real-time Replanning and Surface Reconstruction for Inspection of Three-dimensional Underwater Structures using Autonomous Underwater Vehicles

Abstract

We present a novel method for planning coverage paths for inspecting complex structures on the ocean floor using an autonomous underwater vehicle (AUV). Our method initially uses a 2.5-dimensional (2.5D) prior bathymetric map to plan a nominal coverage path that allows the AUV to pass its sensors over all points on the target area. The nominal path uses a standard mowing-the-lawn pattern in effectively planar regions, while in regions with substantial 3D relief it follows horizontal contours of the terrain at a given offset distance. We then go beyond previous approaches in the literature by considering the vehicle's state uncertainty rather than relying on the unrealistic assumption of an idealized path execution. Toward that end, we present a replanning algorithm based on a stochastic trajectory optimization that reshapes the nominal path to cope with the actual target structure perceived in situ. The replanning algorithm runs onboard the AUV in real time during the inspection mission, adapting the path according to the measurements provided by the vehicle's range-sensing sonars. Furthermore, we propose a pipeline of state-of-the-art surface reconstruction techniques we apply to the data acquired by the AUV to obtain 3D models of the inspected structures that show the benefits of our planning method for 3D mapping. We demonstrate the efficacy of our method in experiments at sea using the GIRONA 500 AUV, where we cover part of a breakwater structure in a harbor and an underwater boulder rising from 40 m up to 27 m depthThis research has been sponsored by the Government of Spain (COMAROB Project, DPI2011-27977-C03-02), the MORPH EU FP7-Project (grant agreement FP7-ICT-2011-7-288704), and the Eurofleets2 EU FP7-Project (grant agreement FP7-INF-2012-312762). The authors are grateful to Lluis Magi, Carles Candela, and Arnau Carrera for helping with the GIRONA 500 operation

Multiobjective coverage path planning: Enabling automated inspection of complex, real-world structures

“…The two methods we compare our algorithm with are circularly sweeping plans, which is part of several different algorithms for continuous coverage path planning for 3D objects [23][24][25], and sampling-based coverage path planning, which was explored in a series of papers by Englot and Hover, both for continuous [22] and discrete [15][16][17] 3D coverage path planning. We consider these the two most relevant methods for this comparison, since they have both been suggested in recent papers on inspection path planning, they are able to generate continuous inspection plans, and it is possible to generalize them to generate plans without complete coverage.…”

“…An alternative to time-consuming optimization techniques is to generate a continuous coverage path rapidly through geometrical calculations [23]. Coverage plans for 3D structures can be generated by "slicing" the structure at evenly spaced depth levels, and circling around each slice at a fixed distance [23][24][25].…”

“…Ideas related to covering a 3D object by following several circling trajectories at a fixed distance from its sides are present in several algorithms [23][24][25]. While the details differ, the common idea is moving around the target structure at a fixed distance at a single depth level, before moving to the next depth (the distance between which depends on the field of view of the vehicle's sensors), making a new sweep around the edges of the target, and so on.…”

Opportunistic Planning in Autonomous Underwater Missions

“…An example is that a part-submerged section of an anchor chain, or other structure, might be spotted during the execution of a mission [2], [29]. This event provides an opportunity to perform an unplanned inspection, or chain-following [30], [31] activity, provided that resources permit the execution of the necessary extra actions. Opportunities are not modelled or anticipated by the planner, and they can be managed without requiring the planner to reason with probabilities.…”

“…According to the topography of the space, some paths might require additional intermediate locations to have been added to the state. Waypoints are generated using an RRT algorithm [30]. The minimum distance between waypoints is 1 meter and the maximum is 5 meters.…”