Design of a Semi-Humanoid Telepresence Robot for Plant Disaster Response and Prevention

Cardenas, Irvin Steve; Kim, Jong-Hoon

doi:10.1109/iros40897.2019.8967859

Cited by 14 publications

(3 citation statements)

References 13 publications

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“…Harikumar et al [5] employed multiple UAVs in firefighting operations, as they can endure high temperatures and hazardous environments that pose significant risks to human firefighters. Nishikawa et al [6] developed a disaster response robot that could play a crucial role in debris removal, rescue of trapped victims, and support of reconstruction efforts. Sun et al [7] developed a dual-arm mobile robot for narrow space and complex rescue operations.…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Learning-Based Bio-Inspired Neural Network for Real-Time Collision-Free Rescue of Multirobot Systems

Li,

Yang

2024

IEEE Trans. Ind. Electron.

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Natural disasters and urban accidents drive the demand for rescue robots to provide safer, faster, and more efficient rescue trajectories. In this paper, a feature learning-based bio-inspired neural network (FLBBINN) is proposed to quickly generate a heuristic rescue path in complex and dynamic environments, as traditional approaches usually cannot provide a satisfactory solution to real-time responses to sudden environmental changes. The neurodynamic model is incorporated into the feature learning method that can use environmental information to improve path planning strategies. Task assignment and collision-free rescue trajectory are generated through robot poses and the dynamic landscape of neural activity. A dual-channel scale filter, a neural activity channel, and a secondary distance fusion are employed to extract and filter feature neurons. After completion of the feature learning process, a neurodynamics-based feature matrix is established to quickly generate the new heuristic rescue paths with parameter-driven topological adaptability. The proposed FLBBINN aims to reduce the computational complexity of the neural network-based approach and enable the feature learning method to achieve real-time responses to environmental changes. Several simulations and experiments have been conducted to evaluate the performance of the proposed FLBBINN. The results show that the proposed FLBBINN would significantly improve the speed, efficiency, and optimality for rescue operations.

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Section: Introductionmentioning

confidence: 99%

A Novel Feature Learning-Based Bio-Inspired Neural Network for Real-Time Collision-Free Rescue of Multirobot Systems

Li,

Yang

2024

IEEE Trans. Ind. Electron.

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“…underwater exploration [25], mobile robotics assistant [26], disaster relief [27], tele-echography [28] and surveillance due to the existence of risks to humans or unreachable physical distance. A general teleoperation system includes a human operator, master devices, communication channel, following robot and perception module etc.…”

Section: Introductionmentioning

confidence: 99%

“…Teleoperation has been a key driver for robotic research, and it stems from the pragmatic need to perform tasks in remote environments [22,23]. After the decades of development, the teleoperation technology has been widely used in various fields, for example, space exploration [24], underwater exploration [25], mobile robotics assistant [26], disaster relief [27], tele‐echography [28] and surveillance due to the existence of risks to humans or unreachable physical distance. A general teleoperation system includes a human operator, master devices, communication channel, following robot and perception module etc.…”

Section: Introductionmentioning

confidence: 99%

A review on manipulation skill acquisition through teleoperation‐based learning from demonstration

Wang

Yang

2021

Cognitive Comp and Systems

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Manipulation skill learning and generalisation have gained increasing attention due to the wide applications of robot manipulators and the spurt of robot learning techniques. Especially, the learning from demonstration method has been exploited widely and successfully in the robotic community, and it is regarded as a promising direction to realise the manipulation skill learning and generalisation. In addition to the learning techniques, the immersive teleoperation enables the human to operate a remote robot with an intuitive interface and achieve the telepresence. Thus, it is a promising way to transfer manipulation skills from humans to robots by combining the learning methods and teleoperation, and adapting the learned skills to different tasks in new situations. This review, therefore, aims to provide an overview of immersive teleoperation for skill learning and generalisation to deal with complex manipulation tasks. To this end, the key technologies, for example, manipulation skill learning, multimodal interfacing for teleoperation and telerobotic control, are introduced. Then, an overview is given in terms of the most important applications of immersive teleoperation platform for robot skill learning. Finally, this survey discusses the remaining open challenges and promising research topics. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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AARON: Assistive Augmented Reality Operations and Navigation System for NASA’s Exploration Extravehicular Mobility Unit (xEMU)

Cardenas

Lenhoff

Park

et al. 2021

Lecture Notes in Computer Science

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Design of a Semi-Humanoid Telepresence Robot for Plant Disaster Response and Prevention

Cited by 14 publications

References 13 publications

A Novel Feature Learning-Based Bio-Inspired Neural Network for Real-Time Collision-Free Rescue of Multirobot Systems

A Novel Feature Learning-Based Bio-Inspired Neural Network for Real-Time Collision-Free Rescue of Multirobot Systems

A review on manipulation skill acquisition through teleoperation‐based learning from demonstration

AARON: Assistive Augmented Reality Operations and Navigation System for NASA’s Exploration Extravehicular Mobility Unit (xEMU)

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