Most of the power sources of mechanical systems can only deliver limited amount of power. Due to this insufficiency of power, often the system fails to achieve a smooth passage through resonance. For example, a non-id...
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Purpose: Recent developments in robotics and artificial intelligence (AI) have led to significant advances in healthcare technologies enhancing robot-Assisted minimally invasive surgery (RAMIS) in some surgical specia...
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Purpose: Recent developments in robotics and artificial intelligence (AI) have led to significant advances in healthcare technologies enhancing robot-Assisted minimally invasive surgery (RAMIS) in some surgical specialties. However, current human-robot interfaces lack intuitive teleoperation and cannot mimic surgeon's hand/finger sensing required for fine motion micro-surgeries. These limitations make teleoperated robotic surgery not less suitable for, e.g. cardiac surgery and it can be difficult to learn for established surgeons. We report a pilot study showing an intuitive way of recording and mapping surgeon's gross hand motion and the fine synergic motion during cardiac micro-surgery as a way to enhance future intuitive teleoperation. Methods: We set to develop a prototype system able to train a Deep Neural Network (DNN) by mapping wrist, hand and surgical tool real-Time data acquisition (RTDA) inputs during mock-up heart micro-surgery procedures. The trained network was used to estimate the tools poses from refined hand joint angles. Outputs of the network were surgical tool orientation and jaw angle acquired by an optical motion capture system. Results: Based on surgeon's feedback during mock micro-surgery, the developed wearable system with light-weight sensors for motion tracking did not interfere with the surgery and instrument handling. The wearable motion tracking system used 12 finger/thumb/wrist joint angle sensors to generate meaningful datasets representing inputs of the DNN network with new hand joint angles added as necessary based on comparing the estimated tool poses against measured tool pose. The DNN architecture was optimized for the highest estimation accuracy and the ability to determine the tool pose with the least mean squared error. This novel approach showed that the surgical instrument's pose, an essential requirement for teleoperation, can be accurately estimated from recorded surgeon's hand/finger movements with a mean squared error (M
Soft robots differ fundamentally from traditional robotics. Due to their composition of soft materials, soft robots are inherently compliant and allow for large continuum-bodied motion. Although some frameworks exist ...
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Soft robots differ fundamentally from traditional robotics. Due to their composition of soft materials, soft robots are inherently compliant and allow for large continuum-bodied motion. Although some frameworks exist for describing the kinematics, the development of dynamic models intended for control-oriented applications is relatively scarce and, to some extent, underdeveloped. This paper provides a modeling framework to describe the nonlinear dynamics of a soft robot using differential geometry of spatial curves. Furthermore, we include the geometrically nonlinear and time-variant mechanical nature imposed by these soft materials into our modeling framework. Numerical simulations of the dynamic model are presented, as well as experimental validations of a study case soft robot to illustrate the accuracy. The proposed modeling framework can be used to simulate the nonlinear dynamics of soft robots but also to calculate the inverse dynamics required for model-based control.
Synchronization of quantum nonlinear oscillators has attracted much attention recently. To characterize the quantum oscillatory dynamics, we recently proposed a fully quantum-mechanical definition of the asymptotic ph...
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The problem of connectivity assessment in an asymmetric network represented by a weighted directed graph is investigated in this article. A power iteration algorithm in a centralized implementation is developed first ...
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Identification of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear time-invariant networks. Meanwhile, most real-life systems exhibit nonlinear behaviors; conside...
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The proposed ambient air pollution sensing system provide real-time low cost flexible measurement of five most important for human health urban areas air parameters (such as CO, PM 2.5 , CO 2 , temperature, and humidi...
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ISBN:
(数字)9781728171807
ISBN:
(纸本)9781728171814
The proposed ambient air pollution sensing system provide real-time low cost flexible measurement of five most important for human health urban areas air parameters (such as CO, PM 2.5 , CO 2 , temperature, and humidity) and transfer it to higher level applications for analysis and forecasting. Implementation features of developed system and proposed diagrams with their description bring a practical value.
—In the paper, a problem of forecasting promotion efficiency is raised. The authors propose a new approach, using the gradient boosting method for this task. Six performance indicators are introduced to capture the p...
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