Compliant mechanisms have been used in several robotics applications, including locomotion and grasping. Variable stiffness joints are part of these compliant mechanisms and generally use one passive element per joint...
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ISBN:
(数字)9798331517519
ISBN:
(纸本)9798331517526
Compliant mechanisms have been used in several robotics applications, including locomotion and grasping. Variable stiffness joints are part of these compliant mechanisms and generally use one passive element per joint. This paper presents the design, model, and characterization of a Variable Stiffness Multi-joint Mechanism (VSMM) using a single carbon fibre rod as its passive element. The VSMM employs a variable lever mechanism, allowing the modulation of the joints’ stiffness. We present an analytical model of the mechanism, providing insights into its mechanical behaviour and enabling the prediction of stiffness variations under different design parameters. Through experimental testing, the mechanism demonstrates a stiffness range of 0.19N/ mm to 0.27N/ mm, offering variable compliance that can change to suit specific task requirements.
In the rapidly evolving landscape of digital content creation, the demand for fast, convenient, and autonomous methods of crafting detailed 3D reconstructions of humans has grown significantly. Addressing this pressin...
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
In the rapidly evolving landscape of digital content creation, the demand for fast, convenient, and autonomous methods of crafting detailed 3D reconstructions of humans has grown significantly. Addressing this pressing need, our AirNeRF system presents an innovative pathway to the creation of a realistic 3D human avatar. Our approach leverages Neural Radiance Fields (NeRF) with an automated drone-based video capturing method. The acquired data provides a swift and precise way to create high-quality human body reconstructions following several stages of our system. The rigged mesh derived from our system proves to be an excellent foundation for freeview synthesis of dynamic humans, particularly well-suited for the immersive experiences within gaming and virtual reality.
Accurate disturbance estimation is essential for safe robot operations. The recently proposed neural moving horizon estimation (NeuroMHE), which uses a portable neural network to model the MHE’s weightings, has shown...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Accurate disturbance estimation is essential for safe robot operations. The recently proposed neural moving horizon estimation (NeuroMHE), which uses a portable neural network to model the MHE’s weightings, has shown promise in further pushing the accuracy and efficiency boundary. Currently, NeuroMHE is trained through gradient descent, with its gradient computed recursively using a Kalman filter. This paper proposes a trust-region policy optimization method for training NeuroMHE. We achieve this by providing the second-order derivatives of MHE, referred to as the MHE Hessian. Remarkably, we show that many of the intermediate results used to obtain the gradient, especially the Kalman filter, can be efficiently reused to compute the MHE Hessian. This offers linear computational complexity with respect to the MHE horizon. As a case study, we evaluate the proposed trust region NeuroMHE on real quadrotor flight data for disturbance estimation. Our approach demonstrates highly efficient training in under 5 min using only 100 data points. It outperforms a state-of-the-art neural estimator by up to 68.1% in force estimation accuracy, utilizing only 1.4% of its network parameters. Furthermore, our method showcases enhanced robustness to network initialization compared to the gradient descent counterpart.
Prostate cancer is a common condition among elderly men, significantly impacting quality of life. It accounts for a large percentage of cancer cases, necessitating effective diagnostic methods. Needle biopsy is a vita...
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ISBN:
(数字)9798331509644
ISBN:
(纸本)9798331509651
Prostate cancer is a common condition among elderly men, significantly impacting quality of life. It accounts for a large percentage of cancer cases, necessitating effective diagnostic methods. Needle biopsy is a vital procedure for accurate detection and grading of prostate cancer, allowing for targeted treatment. The most common approach is transper-ineal prostate biopsy, guided by real-time ultrasound and MRI fusion. The traditional free-hand biopsy technique is highly dependent on the surgeon's expertise and has limitations in both effectiveness and precision. Robotic-assisted biopsy aims to address these issues by enhancing the accuracy and efficiency of the procedure, reducing the need for extensive surgeon experience. This paper introduces an active six-degree-of-freedom (DOF) and one Z-direction passive DOF transperineal prostate biopsy robot incorporating a 2-DOF ultrasonic probe unit and a biopsy unit with 4-DOF movement to replace the manual operation. This system ensures precise orientation and positioning of the biopsy needle, minimizes patient risk, and supports both 2D and 3D ultrasound imaging, thus facilitating a safe and effective biopsy with improved workflow for surgeons.
Advances in deep learning have resulted in steady progress in computer vision with improved accuracy on tasks such as object detection and semantic segmentation. Nevertheless, deep neural networks are vulnerable to ad...
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ISBN:
(数字)9781665479271
ISBN:
(纸本)9781665479271
Advances in deep learning have resulted in steady progress in computer vision with improved accuracy on tasks such as object detection and semantic segmentation. Nevertheless, deep neural networks are vulnerable to adversarial attacks, thus presenting a challenge in reliable deployment. Two of the prominent tasks in 3D scene-understanding for robotics and advanced driver assistance systems are monocular depth and pose estimation, often learned together in an unsupervised manner. While studies evaluating the impact of adversarial attacks on monocular depth estimation exist, a systematic demonstration and analysis of adversarial perturbations against pose estimation are lacking. We show how additive imperceptible perturbations can not only change predictions to increase the trajectory drift but also catastrophically alter its geometry. We also study the relation between adversarial perturbations targeting monocular depth and pose estimation networks, as well as the transferability of perturbations to other networks with different architectures and losses. Our experiments show how the generated perturbations lead to notable errors in relative rotation and translation predictions and elucidate vulnerabilities of the networks. (1)
Phase-only acoustic holography is a fundamental and promising technique for contactless robotic manipulation. Through independently controlling phase-only hologram (POH) of phase array of transducers (PAT) and simulta...
Phase-only acoustic holography is a fundamental and promising technique for contactless robotic manipulation. Through independently controlling phase-only hologram (POH) of phase array of transducers (PAT) and simultaneously driving each channel by sophisticated circuits, a certain acoustic field is dynamically generated in working medium (e.g., air, water or biological tissues) at certain moment. The phase profile of PAT is required dynamically and precisely as per arbitrary expected acoustic field for the sake of versatile and stable robotic manipulation. However, the most conventional methods rely on iterative optimization algorithms which are inevitably time-consuming and probably non-convergent, moreover hindering versatility and fidelity of acoustic robotic manipulation. To address these issues, this paper reports a real-time phase-only acoustic holography algorithm by virtue of iterative unsupervised learning. Using a physics model to construct two queues, which we refer to as experience pools, data pairs consisting of a target acoustic amplitude hologram in expected acoustic field and corresponding POH of PAT are collected on-the-fly, circumventing costly preparation of annotated dataset in advance. With iterative learning between neural network training and experience pools update, both the solution of objective inverse mapping and the adaptation for arbitrary desired acoustic field are mutually enhanced. The experiments and results validated that the proposed approach surpasses previous algorithms in terms of real time and precision.
The proceedings contain 15 papers. The topics discussed include: classifier selection for an ensemble of network traffic analysis machine learning models;formalization and automated enactment of winter road maintenanc...
ISBN:
(纸本)9798350399851
The proceedings contain 15 papers. The topics discussed include: classifier selection for an ensemble of network traffic analysis machine learning models;formalization and automated enactment of winter road maintenance regulatory requirements;taxonomy of risks in software development projects;application of multi-perspective modelling approach for building digital twin in smart agriculture;towards multidimensional infection risk monitoring;business process automation in retail;analysis of medical data processing technologies;ML models for winter road surface condition recognition in the case of insufficient coverage;big data-based solutions for sustainable digital services: evaluation of research methods;and evaluation of the performance of methods for classifying EEG signal processing.
Trunk rehabilitation plays a pivotal role in enhancing locomotive capacities and balance for patients with hemiplegia. In this paper, we present TrunkFlex, a cable-driven robotic system designed specifically to promot...
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ISBN:
(数字)9798350385724
ISBN:
(纸本)9798350385731
Trunk rehabilitation plays a pivotal role in enhancing locomotive capacities and balance for patients with hemiplegia. In this paper, we present TrunkFlex, a cable-driven robotic system designed specifically to promote self-engagement in post-stroke trunk rehabilitation. Unlike conventional rehabilitation robots, which often interact with patients in a rigid manner, TrunkFlex incorporates a cable-driven mechanism powered by eight servo motors, allowing for versatile rehabilitation exercises. Our introduced follow-and-stimulate module, along with posture correction and speed limit modules, is integrated into the control scheme and operates in parallel to induce patient-initiated movement, thereby encouraging self-engagement and promoting a more flexible and seamless rehabilitation process. Muscle activation experiments demonstrate a significant enhancement in active engagement, as evidenced by a marked increase in muscle activity intensity compared to passive rehabilitation methods.
In surgery, the application of appropriate force levels is critical for the success and safety of a given procedure. While many studies are focused on measuring in situ forces, little attention has been devoted to rel...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
In surgery, the application of appropriate force levels is critical for the success and safety of a given procedure. While many studies are focused on measuring in situ forces, little attention has been devoted to relating these observed forces to surgical techniques. Answering questions like "Can certain changes to a surgical technique result in lower forces and increased safety margins?" could lead to improved surgical practice, and importantly, patient outcomes. However, such studies would require a large number of trials and professional surgeons, which is generally impractical to arrange. Instead, we show how robots can learn several variations of a surgical technique from a smaller number of surgical demonstrations and interpolate learnt behaviour via a parameterised skill model. This enables a large number of trials to be performed by a robotic system and the analysis of surgical techniques and their downstream effects on tissue. Here, we introduce a parameterised model of the elliptical excision skill and apply a Bayesian optimisation scheme to optimise the excision behaviour with respect to expert ratings, as well as individual characteristics of excision forces. Results show that the proposed framework can successfully align the generated robot behaviour with subjects across varying levels of proficiency in terms of excision forces.
This work presents an approach to vision-based robotic grasping, combining scene understanding with advanced neural network architectures. The primary goal is to develop a system that enables robots to grasp a wide va...
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