Multi-Depth Uniform Coverage Path Planning for Unmanned Surface Vehicle Surveying

Maider Larrazabal; Tong Yang; Izaro Goienetxea; Jaime Valls Miro

cs.ROMay 13, 2026

Multi-Depth Uniform Coverage Path Planning for Unmanned Surface Vehicle Surveying

Authors: Maider Larrazabal, Tong Yang, Izaro Goienetxea, Jaime Valls Miro

Abstract

This paper introduces a novel automatic coverage path planning algorithm for bathymetry surveying with unmanned surface vehicles. The detection range of the mapping sensor employed - a multibeam echo sounder - is heavily influenced by local seafloor depths. Hence, a path designed to uniformly cover the sea surface does not guarantee uniform coverage of the seafloor. Yet this is currently the typical process for bathymetric surveys, with the simplistic boustrophedon scheme along manually selected waypoints at constant depths being the most widespread planner used. The proposed scheme incorporates coarse prior depth information to pre-process the target region and adaptively guide path generation and sensing range configuration. By explicitly accounting for depth variations, the proposed algorithm designs a coverage path with optimised spacing between survey passes that adjusts the sensing beam aperture to achieve more consistent seafloor coverage. The proposed method is shown to offer significant improvements in both synthetic and real-world scenarios. Validations in challenging synthetic terrains achieves coverage ratios beyond 99%, a marked improvement when compared with traditional boustrophedon paths revealing a maximum 75% coverage. The same trend appears in realistic simulations using real bathymetric data from a coastal harbour, with coverage reaching over 92%, and significantly surpassing boustrophedon sweeps with coverage rates below 65%. Beyond improved performance, the scheme also brings a fully automated design, suitable for autonomous marine vehicles, thus offering practical utilities for real-world applications.

Explore similar work

Mar 4, 2026cs.RO

OmniPlanner: Universal Exploration and Inspection Path Planning Across Robot Morphologies

Autonomous robotic systems are increasingly deployed for mapping, monitoring, and inspection in complex and unstructured environments. However, most existing path planning approaches remain domain-specific (i.e., in the air, on land, or at sea), limiting their scalability and cross-platform applicability. This article presents OmniPlanner, a unified planning framework for autonomous exploration and inspection across aerial, ground, and underwater robots. The method integrates volumetric exploration and viewpoint-based inspection, alongside target reach behaviors within a single modular architecture, complemented by a platform abstraction layer that captures morphology-specific sensing, traversability and motion constraints. This enables the same planning strategy to generalize across distinct mobility domains with minimal retuning. The framework is validated through extensive simulation studies and field deployments in underground mines, industrial facilities, forests, submarine bunkers, and structured outdoor environments. Across these diverse scenarios, OmniPlanner demonstrates robust performance, consistent cross-domain generalization, and improved exploration and inspection efficiency compared to representative state-of-the-art baselines. Videos presenting the OmniPlanner framework and demonstrating its field deployments across aerial, ground, and underwater robots are available at https://ntnu-arl.github.io/omniplanner, and the source code is publicly available at https://github.com/ntnu-arl/gbplanner_ros/tree/gbplanner3.

Angelos Zacharia, Mihir Dharmadhikari, Mohit Singh +1

Robot Planning Underwater Robots Aerial Robots Planning Methods Autonomous Exploration Strategies Inspection

Ask PDF

Jul 2, 2026cs.RO

Path planning for unmanned naval surface vehicles

There nowadays is a myriad of approaches to real-time avoidance of fixed obstacles for unmanned surface vehicles (USVs) and, to a lesser extent, also the task of avoiding moving obstacles such as boats, ships, swimmers, and other USVs, but both topics still present challenges. This paper offers novel approaches to both of these problems. It uses a combination of a global path planner, which finds a path from a start point to a goal point that avoids fixed obstacles (given that their locations are known in advance), and a local path planner, which can circumnavigate a moving obstacle (as well as any previously unknown fixed obstacles). The global planner is novel in that it employs a combination of three path planners, one known in the literature as Grassfire, one that is a new modification of Grassfire, and one that is a new, and arguably more intuitive, version of the well-known Probabilistic Roadmap. The local planner is novel in that it employs a higher-level decision logic based on its observations regarding the direction of movement of the obstacle relative to the USVs global path. This logic enables the USV to determine the best strategy for avoiding the obstacle by systematically routing the vehicle behind the obstacle rather than running parallel to it until the opportunity to pass appears. Simulations are provided that validate these claims. For comparison with other systems, the simulations include an implementation of the well-known D* algorithm, and the discussion covers additional dynamic path planning systems, which, like D*, do not necessarily route the vehicle behind the moving obstacle.

Daniel G. Schwartz

Coverage Path Planning Obstacle Avoidance Collision Avoidance

Ask PDF

Sep 15, 2026cs.RO

Multi-Session Multimodal Underwater Mapping with Acoustic and Optical Imaging

Accurate seafloor mapping is essential for marine science, archaeology, and environmental monitoring. However, integrating data from different sensors, such as side-scan sonar and optical cameras, collected across separate survey sessions, remains challenging due to positioning drift and sensor offsets. This paper presents a multi-session, multimodal underwater mapping framework based on factor graph optimization. The method jointly optimizes vehicle trajectories, 3D landmark positions, sensor extrinsics, and per-session global alignment transformations. By combining rigid inter-session corrections with local trajectory deformations, it compensates for both inter-session offsets and intra-session distortions from accumulated navigation errors. The proposed methodology was validated on real-world datasets collected along the Catalan coast. Results show measurable improvements in map consistency over both unoptimized and rigid-alignment baselines across all metrics, including Pixel Accuracy and mean Intersection over Union. The method achieves a 3.4% improvement in pixel accuracy over the unoptimized baseline, corresponding to improved semantic labelling across approximately 14700

\text{m}^2

of mapped area. Qualitative results further show consistent co-registration between sonar and optical maps, even in the presence of significant trajectory distortions and inter-session misalignments. These findings demonstrate the potential of the proposed framework to generate coherent multimodal seafloor maps from heterogeneous underwater surveys.

Precious Philip-Ifabiyi, Valerio Franchi, Fausto Ferreira +1

Underwater Imagery Underwater Image Enhancement Mapping Acoustic Factor Graphs Trajectory Prediction

Ask PDF