At present, it is still a great challenge to improve the observation accuracy of two-photon fluorescence microscope. A new method that using auxiliary microspheres as movable components on the sample is proposed to so...
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Inter-robot collisions pose a significant safety risk when multiple robotic arms operate in close proximity. We present an online collision avoidance methodology leveraging 3D convex shape-based High-Order control Bar...
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Natural swarms have inspired various controlling algorithms for swarm robotics, while few of them were programmed in a similar way as the natural swarms. Defining a reliable programming method is still a daunting chal...
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Designing control inputs that satisfy safety requirements is crucial in safety-critical nonlinear control, and this task becomes particularly challenging when full-state measurements are unavailable. In this work, we ...
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ISBN:
(数字)9798350382655
ISBN:
(纸本)9798350382662
Designing control inputs that satisfy safety requirements is crucial in safety-critical nonlinear control, and this task becomes particularly challenging when full-state measurements are unavailable. In this work, we address the problem of synthesizing safe and stable control for control-affine systems via output feedback (using an observer) while reducing the estimation error of the observer. To achieve this, we adapt control Lyapunov function (CLF) and control barrier function (CBF) techniques to the output feedback setting. Building upon the existing CLF-CBF-QP (Quadratic Program) and CBF-QP frameworks, we formulate two confidence-aware optimization problems and establish the Lipschitz continuity of the obtained solutions. To validate our approach, we conduct simulation studies on two illustrative examples. The simulation studies indicate both improvements in the observer's estimation accuracy and the fulfillment of safety and control requirements.
The capability to navigate safely in an unstructured environment is crucial when deploying robotic systems in real-world scenarios. Recently, control barrier function (CBF) based approaches have been highly effective ...
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This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the availab...
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ISBN:
(数字)9798350357882
ISBN:
(纸本)9798350357899
This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the available datasets for litter detection and segmentation. The reviewed datasets are used to train a Mask-RCNN neural network for instance segmentation. The neural network is off-board deployed on an edge computing device and used for litter position estimation. Based on the estimated litter position, we plan a path based on a quadratic Bezier curve for the litter pickup. We compare different trajectory generation methods for the object pickup. The system is verified in a laboratory environment. Eventually, we present practical considerations and improvements necessary to enable autonomous litter collection with MRAV.
Bionic limb prosthetics continue to face significant challenges in terms of artificial limb embodiment, often leading to prosthesis abandonment in patients. Following recent results in literature on digital trainings,...
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Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classifi...
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In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the ...
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Designing control inputs that satisfy safety requirements is crucial in safety-critical nonlinear control, and this task becomes particularly challenging when full-state measurements are unavailable. In this work, we ...
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