Coverage Path Planning with Track Spacing Adaptation for Autonomous Underwater Vehicles

• URL: http://arxiv.org/abs/2006.12896v1
• Date: Tue, 23 Jun 2020 11:04:32 GMT
• Title: Coverage Path Planning with Track Spacing Adaptation for Autonomous Underwater Vehicles
• Authors: Veronika Yordanova, Bart Gips
• Abstract summary: We propose a coverage path planning method that adapts the AUV track spacing with the objective of collecting better data. We achieve this by shifting the coverage overlap at the tail of the sensor range where the lowest data quality is expected.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we address the mine countermeasures (MCM) search problem for an autonomous underwater vehicle (AUV) surveying the seabed using a side-looking sonar. We propose a coverage path planning method that adapts the AUV track spacing with the objective of collecting better data. We achieve this by shifting the coverage overlap at the tail of the sensor range where the lowest data quality is expected. To assess the algorithm, we collected data from three at-sea experiments. The adaptive survey allowed the AUV to recover from a situation where the sensor range was overestimated and resulted in reducing area coverage gaps. In another experiment,the adaptive survey showed a 4.2% improvement in data quality for nearly 30% of the 'worst' data.

Related papers

• A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques [63.10251271444959]
  Large language models are first pre-trained on trillions of tokens and then instruction-tuned or aligned to specific preferences. We conduct an in-depth investigation of the impact of popular choices for three crucial axes. Our setup spanning over 300 experiments reveals consistent trends and unexpected findings.
  arXiv  Detail & Related papers  (2024-06-07T12:25:51Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Automated Automotive Radar Calibration With Intelligent Vehicles [73.15674960230625]
  We present an approach for automated and geo-referenced calibration of automotive radar sensors. Our method does not require external modifications of a vehicle and instead uses the location data obtained from automated vehicles. Our evaluation on data from a real testing site shows that our method can correctly calibrate infrastructure sensors in an automated manner.
  arXiv  Detail & Related papers  (2023-06-23T07:01:10Z)
• Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration [55.41644538483948]
  This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format. The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
  arXiv  Detail & Related papers  (2023-04-27T20:21:18Z)
• Optimal Transport for Change Detection on LiDAR Point Clouds [16.552050876277242]
  Unsupervised change detection between airborne LiDAR data points is difficult due to unmatching spatial support and noise from the acquisition system. We propose an unsupervised approach based on the computation of the transport of 3D LiDAR points over two temporal supports. Our method allows for unsupervised multi-class classification and outperforms the previous state-of-the-art unsupervised approaches by a significant margin.
  arXiv  Detail & Related papers  (2023-02-14T13:08:07Z)
• Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources using Autonomous Surface Vehicles [0.0]
  This work proposes a framework for monitoring large water resources with autonomous vehicles. Using information gain as a measure of the uncertainty reduction over data, it is proposed a Deep Q-Learning algorithm improved by a Q-Censoring mechanism for model-based obstacle avoidance.
  arXiv  Detail & Related papers  (2022-10-12T07:33:46Z)
• Dense Air Quality Maps Using Regressive Facility Location Based Drive By Sensing [4.264192013842096]
  We present an efficient vehicle selection framework that incorporates smoothness in neighboring locations and autoregressive time correlation. We evaluate our framework on selecting a subset from the fleet of public transport in Delhi, India.
  arXiv  Detail & Related papers  (2022-01-20T18:20:37Z)
• Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation [127.6168183074427]
  We propose a new task, Lifelong Unsupervised Domain Adaptive (LUDA) person ReID. This is challenging because it requires the model to continuously adapt to unlabeled data of the target environments. We design an effective scheme for this task, dubbed CLUDA-ReID, where the anti-forgetting is harmoniously coordinated with the adaptation.
  arXiv  Detail & Related papers  (2021-12-13T13:19:45Z)
• Feature Space Exploration For Planning Initial Benthic AUV Surveys [11.497731809524536]
  Special-purpose Autonomous Underwater Vehicles (AUVs) are utilised for benthic (seafloor) surveys. Due to the small-sensor footprint of the cameras and the vast areas to be surveyed, these AUVs can not feasibly collect full coverage imagery of areas larger than a few tens of thousands of square meters. This research proposes methods for planning initial AUV surveys that efficiently explore a feature space representation of the bathymetry.
  arXiv  Detail & Related papers  (2021-05-25T01:20:18Z)
• Towards Adaptive Benthic Habitat Mapping [9.904746542801838]
  We show how a habitat model can be used to plan efficient Autonomous Underwater Vehicles (AUVs) surveys. A Bayesian neural network is used to predict visually-derived habitat classes when given broad-scale bathymetric data. We demonstrate how these structured uncertainty estimates can be utilised to improve the model with fewer samples.
  arXiv  Detail & Related papers  (2020-06-20T01:03:41Z)
• Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach [88.45509934702913]
  We design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed. We incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time.
  arXiv  Detail & Related papers  (2020-02-21T07:29:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.