Collection of Marine Debris by Jointly Using UAV-UUV With GUI for Simple Operation

Shirakura, Naoki; Kiyokawa, Takuya; Kumamoto, Hikaru; Takamatsu, Jun; Ogasawara, Tsukasa

doi:10.1109/access.2021.3076110

Cited by 12 publications

(8 citation statements)

References 22 publications

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“…Using the articles collected in this manner, effective and efficient technologies that can be used for sorting mixed industrial waste were investigated in this study. From these investigations, domain-specific sorting approaches for underwater [40], [41], [42], [43], floating [44], [45], [46], space [47], [48], nuclear [49], [50], [51], [52], [53], [54], [55], and biomedical [56], [57] waste were excluded. Rather, Municipal and CNI [58] waste, along with ND, CND [36],…”

Section: B Search and Collection Strategymentioning

confidence: 99%

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Kiyokawa

Takamatsu

Koyanaka

2024

IEEE Trans. Automat. Sci. Eng.

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To achieve recycling of mixed industrial waste toward an advanced sustainable society, waste sorting automation through robots is crucial and urgent. For this purpose, a robot is required to recognize the category, shape, pose, and condition of different waste items and manipulate them according to the category to be sorted. This survey considers three potential difficulties in the sorting automation: 1) End-effector: to robustly grasp and manipulate different waste items with dirt and deformations; 2) Sensor: to recognize the category, shape, and pose of existing objects to be manipulated and the wet and dirty conditions of their surfaces; and 3) Planner: to generate feasible and efficient sequences and trajectories. This survey includes 76 references to studies related to automatic waste sorting and 159 references to worldwide waste recycling attempts. This pioneering investigation reveals the possibility and limitations of conventional systems; thus, providing insights on open issues and potential technologies to achieve a robot-incorporated sorter for the chaotic mixed waste is one of its contributions. This paper further presents a system design policy for readers and discusses future advanced sorters, thereby contributing to the field of robotics and automation.Note to Practitioners-Most automated sorting systems operate for limited target waste items. This study is motivated by the automation of mixed industrial waste treatment facilities using advanced robotic sorters. Emerging advances and increasing functionalities of robot system components will widen system applicability and increase use cases in the chaotic mixed industrial waste domain. This paper surveys the research conducted to date, discusses open issues and potential approaches, and presents user guides that provide practitioners with a system design policy. The user guides created according to the strengths and weaknesses of each system configuration provide future researchers and developers with a useful a priori design policy that has been thus far validated on efficiency, quality, productivity, and reliability. A question-and-answer style guide Manuscript

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Section: B Search and Collection Strategymentioning

confidence: 99%

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Kiyokawa

Takamatsu

Koyanaka

2024

IEEE Trans. Automat. Sci. Eng.

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“…UMVs can work with UAVs [ 177 , 178 , 179 ]. Images are mainly collected with UAV cameras so that the UMV can perform tasks smoothly underwater and on the surface.…”

Section: Swarm and Cooperation Between Unmanned Vehiclesmentioning

confidence: 99%

“…The APF function was established to quickly and stably assemble USVs by applying the sliding mode control method and assembling USVs without collision. Furthermore, Shirakura et al [ 178 ] conducted a study on the location and collection of floating marine waste using UAVs and UUVs. Coordinate transformation techniques and a graphical user interface (GUI) were designed to estimate the three-dimensional location of fragments floating on the surface using images from UAVs, and controllers were developed to generate trajectories using teaching playback with feedback to simplify UUV remote manipulation control.…”

Section: Swarm and Cooperation Between Unmanned Vehiclesmentioning

confidence: 99%

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Bae

Hong

2023

Sensors

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With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs), have the potential to complete maritime tasks that are unachievable for manned vehicles, lower the risk of man power, raise the power required to carry out military missions, and reap huge economic benefits. The aim of this review is to identify past and current trends in UMV development and present insights into future UMV development. The review discusses the potential benefits of UMVs, including completing maritime tasks that are unachievable for manned vehicles, lowering the risk of human intervention, and increasing power for military missions and economic benefits. However, the development of UMVs has been relatively tardy compared to that of UVs used on the ground and in the air due to adverse environments for UMV operation. This review highlights the challenges in developing UMVs, particularly in adverse environments, and the need for continued advancements in communication and networking technologies, navigation and sound exploration technologies, and multivehicle mission planning technologies to improve UMV cooperation and intelligence. Furthermore, the review identifies the importance of incorporating AI and machine learning technologies in UMVs to enhance their autonomy and ability to perform complex tasks. Overall, this review provides insights into the current state and future directions for UMV development.

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“…First, we analyzed the results of the questionnaires to evaluate mental workload and usability from a subjective perspective. Referring to [31], we asked the participants several questions regarding their usage during the trials to compare the effects of each feature implemented in each mode. To evaluate mental workload and usability, seven participants took the NASA-TLX and SUS respectively.…”

Section: B Evaluation Metricsmentioning

confidence: 99%

Fully Automated Annotation With Noise-Masked Visual Markers for Deep-Learning-Based Object Detection

Kiyokawa

Tomochika

Takamatsu

et al. 2019

IEEE Robot. Autom. Lett.

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Training deep-learning-based vision systems requires the manual annotation of a significant amount of data to optimize several parameters of the deep convolutional neural networks. Such manual annotation is highly time-consuming and labor-intensive. To reduce this burden, a previous study presented a fully automated annotation approach that does not require any manual intervention. The proposed method associates a visual marker with an object and captures it in the same image. However, because the previous method relied on moving the object within the capturing range using a fixed-point camera, the collected image dataset was limited in terms of capturing viewpoints. To overcome this limitation, this study presents a mobile application-based free-viewpoint image-capturing method. With the proposed application, users can collect multi-view image datasets automatically that are annotated with bounding boxes by moving the camera. However, capturing images through human involvement is laborious and monotonous. Therefore, we propose gamified application features to track the progress of the collection status. Our experiments demonstrated that using the gamified mobile application for bounding box annotation, with visible collection progress status, can motivate users to collect multi-view object image datasets with less mental workload and time pressure in an enjoyable manner, leading to increased engagement.

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Collection of Marine Debris by Jointly Using UAV-UUV With GUI for Simple Operation

Cited by 12 publications

References 22 publications

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Fully Automated Annotation With Noise-Masked Visual Markers for Deep-Learning-Based Object Detection

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