A critical survey on marsupial robotic teams for environmental monitoring of water bodies

The world’s oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosystem is threatened by human activities. Marine protected areas (MPAs) are distinctive spaces protected by law due to their unique characteristics, such as being the habitat of endangered marine species. Even with this protection, there are still illegal activities such as poaching or anchoring that threaten the survival of different marine species. In this context, we propose an autonomous surface vehicle (ASV) model system for the surveillance of marine areas by detecting and recognizing vessels through artificial intelligence (AI)-based image recognition services, in search of those carrying out illegal activities. Cloud and edge AI computing technologies were used for computer vision. These technologies have proven to be accurate and reliable in detecting shapes and objects for which they have been trained. Azure edge and cloud vision services offer the best option in terms of accuracy for this task. Due to the lack of 4G and 5G coverage in offshore marine environments, it is necessary to use radio links with a coastal base station to ensure communications, which may result in a high response time due to the high latency involved. The analysis of on-board images may not be sufficiently accurate; therefore, we proposed a smart algorithm for autonomy optimization by selecting the proper AI technology according to the current scenario (SAAO) capable of selecting the best AI source for the current scenario in real time, according to the required recognition accuracy or low latency. The SAAO optimizes the execution, efficiency, risk reduction, and results of each stage of the surveillance mission, taking appropriate decisions by selecting either cloud or edge vision models without human intervention.

Section: Related Workmentioning

confidence: 99%

Autonomous Marine Robot Based on AI Recognition for Permanent Surveillance in Marine Protected Areas

Molina

Salhaoui

Guerrero-González

et al. 2021

“…Bayat et al [42] studies search techniques for environmental monitoring where USVs are marginally mentioned. Marques et al [43] studies marsupial robotic teams for environment monitoring of water bodies. The authors claim marsupial robotic teams are especially tailored for water body monitoring, due to their versatility, where each robot can compensate the limitation of others.…”

Section: Groundworkmentioning

confidence: 99%

“…There are no surveys dedicated to USVs where the focus is on DM, demonstrating the novelty of this paper. This chart is organized in six categories:Disaster management [7,18,19,21,49,50,51]General surveys regarding USVs and unmanned systems [23,24,25,28,31,48]Guidance, Navigation and Control [22,26,29,32,33,35,36,39,40,47]Higher-level autonomy [37,38,46]Structure inspection [41,45]Environmental monitoring [30,42,43,44]…”

Section: Groundworkmentioning

confidence: 99%

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

Jorge

Granada

Maidana

et al. 2019

141

Disaster robotics has become a research area in its own right, with several reported cases of successful robot deployment in actual disaster scenarios. Most of these disaster deployments use aerial, ground, or underwater robotic platforms. However, the research involving autonomous boats or Unmanned Surface Vehicles (USVs) for Disaster Management (DM) is currently spread across several publications, with varying degrees of depth, and focusing on more than one unmanned vehicle—usually under the umbrella of Unmanned Marine Vessels (UMV). Therefore, the current importance of USVs for the DM process in its different phases is not clear. This paper presents the first comprehensive survey about the applications and roles of USVs for DM, as far as we know. This work demonstrates that there are few current deployments in disaster scenarios, with most of the research in the area focusing on the technological aspects of USV hardware and software, such as Guidance Navigation and Control, and not focusing on their actual importance for DM. Finally, to guide future research, this paper also summarizes our own contributions, the lessons learned, guidelines, and research gaps.

“…Robots have also been used for radiological monitoring in emergency-response situations [ 17 ]. Please refer to [ 18 , 19 ] for two surveys on the application of robotics to the environmental domain.…”

Section: Related Workmentioning

confidence: 99%

Sediment Sampling in Estuarine Mudflats with an Aerial-Ground Robotic Team

Deusdado¹,

Guedes²,

Silva³

et al. 2016

Self Cite

This paper presents a robotic team suited for bottom sediment sampling and retrieval in mudflats, targeting environmental monitoring tasks. The robotic team encompasses a four-wheel-steering ground vehicle, equipped with a drilling tool designed to be able to retain wet soil, and a multi-rotor aerial vehicle for dynamic aerial imagery acquisition. On-demand aerial imagery, properly fused on an aerial mosaic, is used by remote human operators for specifying the robotic mission and supervising its execution. This is crucial for the success of an environmental monitoring study, as often it depends on human expertise to ensure the statistical significance and accuracy of the sampling procedures. Although the literature is rich on environmental monitoring sampling procedures, in mudflats, there is a gap as regards including robotic elements. This paper closes this gap by also proposing a preliminary experimental protocol tailored to exploit the capabilities offered by the robotic system. Field trials in the south bank of the river Tagus’ estuary show the ability of the robotic system to successfully extract and transport bottom sediment samples for offline analysis. The results also show the efficiency of the extraction and the benefits when compared to (conventional) human-based sampling.