Anisotropic Diffusion-Based Enhancement of Scene Segmentation with Instance Labels

Kleitsiotis, Ioannis; Mariolis, Ioannis; Giakoumis, Dimitrios; Likothanassis, Spiridon D.; Tzovaras, Dimitrios

doi:10.1007/978-3-030-89131-2_35

Cited by 2 publications

(2 citation statements)

References 30 publications

(32 reference statements)

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“…Robotic systems and artificial intelligence methods have been widely adopted in every aspect of agricultural operations, including apple picking [15], strawberries harvesting [16], and grape detection [17].…”

Section: Related Workmentioning

confidence: 99%

An Autonomous Navigation Framework for Holonomic Mobile Robots in Confined Agricultural Environments

Tsiakas,

Papadimitriou,

Pechlivani

et al. 2023

Robotics

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Due to the accelerated growth of the world’s population, food security and sustainable agricultural practices have become essential. The incorporation of Artificial Intelligence (AI)-enabled robotic systems in cultivation, especially in greenhouse environments, represents a promising solution, where the utilization of the confined infrastructure improves the efficacy and accuracy of numerous agricultural duties. In this paper, we present a comprehensive autonomous navigation architecture for holonomic mobile robots in greenhouses. Our approach utilizes the heating system rails to navigate through the crop rows using a single stereo camera for perception and a LiDAR sensor for accurate distance measurements. A finite state machine orchestrates the sequence of required actions, enabling fully automated task execution, while semantic segmentation provides essential cognition to the robot. Our approach has been evaluated in a real-world greenhouse using a custom-made robotic platform, showing its overall efficacy for automated inspection tasks in greenhouses.

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Section: Related Workmentioning

confidence: 99%

An Autonomous Navigation Framework for Holonomic Mobile Robots in Confined Agricultural Environments

Tsiakas,

Papadimitriou,

Pechlivani

et al. 2023

Robotics

Self Cite

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“…Within this project, for effective grasping, I. Sarantopoulos et al [74,75] proposed approaches to detect the desired object in a cluttered environment and to make enough space around it for easier picking by pushing the surrounding objects. Instant segmentation was investigated by I. Kleitsiotis et al [76].…”

Section: Sensing and Perceptionmentioning

confidence: 99%

Mobile Manipulators in Industry 4.0: A Review of Developments for Industrial Applications

Ghodsian,

Benfriha,

Olabi

et al. 2023

Sensors

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In the realm of Industry 4.0, diverse technologies such as AI, Cyber-Physical Systems, IoT, and advanced sensors converge to shape smarter future factories. Mobile manipulators (MMs) are pivotal, fostering flexibility, adaptability, and collaboration in industrial processes. On one hand, MMs offer a remarkable level of flexibility, adaptability, and collaboration in industrial processes, facilitating swift production line changes and efficiency enhancements. On the other hand, their integration into real manufacturing environments requires meticulous considerations, such as safety, human–robot interaction, and cybersecurity. This article delves into MMs’ essential role in achieving Industry 4.0’s automation and adaptability by integrating mobility with manipulation capabilities. The study reviews MMs’ industrial applications and integration into manufacturing systems. The most observed applications are logistics (49%) and manufacturing (33%). As Industry 4.0 advances, the paper emphasizes updating and aligning MMs with the smart factory concept by networks of sensors and the real-time analysis of them, especially for an enhanced human–robot interaction. Another objective is categorizing considerations for MMs’ utilization in Industry 4.0-aligned manufacturing. This review methodically covers a wide range of considerations and evaluates existing solutions. It shows a more comprehensive approach to understanding MMs in Industry 4.0 than previous works. Key focus areas encompass perception, data analysis, connectivity, human–robot interaction, safety, virtualization, and cybersecurity. By bringing together different aspects, this research emphasizes a more integrated view of the role and challenges of MMs in the Industry 4.0 paradigm and provides insights into aspects often overlooked. A detailed and synthetic analysis of existing knowledge was performed, and insights into their future path in Industry 4.0 environments were provided as part of the contributions of this paper. The article also appraises initiatives in these domains, along with a succinct technology readiness analysis. To sum up, this study highlights MMs’ pivotal role in Industry 4.0, encompassing their influence on adaptability, automation, and efficiency.

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Anisotropic Diffusion-Based Enhancement of Scene Segmentation with Instance Labels

Cited by 2 publications

References 30 publications

An Autonomous Navigation Framework for Holonomic Mobile Robots in Confined Agricultural Environments

An Autonomous Navigation Framework for Holonomic Mobile Robots in Confined Agricultural Environments

Mobile Manipulators in Industry 4.0: A Review of Developments for Industrial Applications

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