Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System

Tassetti, Anna Nora; Galdelli, Alessandro; Pulcinella, Jacopo; Mancini, Adriano; Bolognini, Luca

doi:10.3390/s22030839

Cited by 21 publications

(13 citation statements)

References 56 publications

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“…The purpose of the robotic system is to check for planogram compliance, SOOS situations, and PA by capturing a whole shelf both horizontally and vertically, as explained in [20]. Once one of these events has been identified, these are instantaneously and securely recorded on a Hyperledger Fabric blockchain network hosted on Amazon Managed Blockchain [28], leveraging IoT and Cloud technology [29], [30]. This decentralized ledger offers an immutable, time-stamped record of all such events, thereby providing an efficient system of accountability and enabling streamlined operations through real-time response to stock management needs.…”

Section: Resultsmentioning

confidence: 99%

Retail Robot Navigation: A Shopper Behavior-Centric Approach to Path Planning

Galdelli,

Pietrini,

Mancini

et al. 2024

IEEE Access

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In the ever-evolving landscape of retail, understanding shopper behavior is pivotal for optimizing sales and effectively managing product availability and placement. This study explores the integration of autonomous mobile robots into the shelf inspection process, leveraging advancements in automation, information, and robotics technology. Performing mapping tasks, these robots incorporate insights into customer behavior by exploiting various sources of behavioral data, including trajectories and product interactions. Motivated by the complex and dynamic nature of modern stores, our research seeks to bridge the gap in retail inventory management. Our unique contribution lies in the development of a novel path planning method for robots, specifically tailored for an automated inventory management system. By focusing on customer trajectories and product interactions, we aim to enhance the arrangement and positioning of products within retail spaces. Our research is motivated by the need to address the challenges faced by retailers in optimizing store layouts and product placements. The proposed strategy utilizes a heatmap and a vision-based system to analyze spatial and temporal patterns of shopper behavior. This information is then employed to optimize robot navigation in both highly and less-visited areas. Trajectories and product interactions data from real store installations were utilized in simulation, providing valuable insights into optimal planning for mobile robots to visit Points of Interest (PoI). The active shopping cart tracking system generated heatmaps, while a vision-based system collected shopper-products interactions data. Subsequently, our approach was deployed on a real retail robot for inventory management, and the path planning source code was released. Our findings demonstrate that the path planned by our approach not only avoids collisions with static store sections but also optimizes paths in areas with significant customer-shelf activity.

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Section: Resultsmentioning

confidence: 99%

Retail Robot Navigation: A Shopper Behavior-Centric Approach to Path Planning

Galdelli,

Pietrini,

Mancini

et al. 2024

IEEE Access

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“…Nevertheless, the collection of SSFs positional information is still not mandatory and the absence of an official legislative regulation regarding data collection further hampers the overall understanding of the activity and the spatial dynamic of this fleet segment 27 , 34 – 37 . Indeed, the challenge of managing marine resources is often hindered in SSFs data-poor scenarios.…”

Section: Background and Summarymentioning

confidence: 99%

Bridging the gap in fishing effort mapping: a spatially-explicit fisheries dataset for Campanian MPAs, Italy

Lattanzi,

Pulcinella,

Battaglia

et al. 2024

Sci Data

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Recent technological advancements have facilitated the extensive collection of movement data from large-scale fishing vessels, yet a significant data gap remains for small-scale fisheries. This gap hinders the development of consistent exploitation patterns and meeting the information needs for marine spatial planning in fisheries management. This challenge is specifically addressed in the Campania region of Italy, where several Marine Protected Areas support biodiversity conservation and fisheries management. The authors have created a spatially-explicit dataset that encompasses both large-scale (vessels exceeding 12 meters in length) and small-scale (below 12 meters) fishing efforts. This dataset (available at https://doi.org/10.6084/m9.figshare.23592006) is derived from vessel tracking data and participatory mapping. It offers insights into potential conflicts between different fishing segments and their interactions with priority species and habitats. The data can assist researchers and coastal management stakeholders in formulating policies that reduce resource competition and promote ecosystem-based fisheries management. Furthermore, the provided mapping approach is adaptable for other regions and decision-making frameworks, as we are committed to sharing the tools and techniques we employed.

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“…The vessel monitoring system (VMS) has provided more precise spatial and quantitative data about industrial fishing over the past ten years [32]. One of the technologies frequently utilized in several nations for monitoring and surveillance of fishing boats is the VMS.…”

Section: Vessel Monitoring Systemmentioning

confidence: 99%

A review on features and methods of potential fishing zone

Ya’acob,

Nik Dzulkefli,

Abdul Aziz

et al. 2024

IJECE

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This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.

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Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System

Cited by 21 publications

References 56 publications

Retail Robot Navigation: A Shopper Behavior-Centric Approach to Path Planning

Retail Robot Navigation: A Shopper Behavior-Centric Approach to Path Planning

Bridging the gap in fishing effort mapping: a spatially-explicit fisheries dataset for Campanian MPAs, Italy

A review on features and methods of potential fishing zone

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