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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yajun Ding, Yulian Li, Aimin Fei, Rong Guo, Xie Wu, Fangxiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9781665468190

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs' functions. © 2022 IEEE.

关键词： Embeddings

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A Continuous Off-Policy Reinforcement Learning Scheme for Optimal Motion Planning in Simply-Connected Workspaces

A Continuous Off-Policy Reinforcement Learning Scheme for Op...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Department of Electrical and Computer Engineering University of Patras Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in multiply connected manifolds, and is necessary for formulating continuous solutions. The current method generalizes and improves upon previous results, where parametrized controllers hindered the method in scope and results. Through enhancing the traditional reactive paradigm with RL, the proposed scheme is demonstrated to outperform both previous reactive methods as well as an RRT* method in path length, cost function values and execution times, indicating almost global optimality.

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Passivity-Based Distributed Event-Triggered Flocking control of Port-Hamiltonian Systems

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IEEE control Systems Letters 2025年 9卷 342-347页

作者： Aranda-Escolástico, Ernesto Abdelrahim, Mahmoud Fernández-Amorós, David Guinaldo, María Colombo, Leonardo Department of Software and Systems Engineering Universidad Nacional de Educación a Distancia (UNED) Juan del Rosal 16 Madrid 28040 Spain Prince Sultan University Renewable Energy Laboratory College of Engineering Riyadh 11586 Saudi Arabia Computer Science and Automatic Control Department Universidad Nacional de Educación a Distancia (UNED) Juan del Rosal 16 Madrid 28040 Spain Centre for Automation and Robotics (CSIC-UPM) Ctra. M300 Campo Real Km 0200 Madrid Arganda del Rey 28500 Spain

This letter addresses the problem of flocking motion in the context of port-Hamiltonian systems. The port-Hamiltonian framework offers advantages in modeling networks, dissipation, and heterogeneous agents. In this regard, designing flocking controllers in port-Hamiltonian form significantly increases their practical applications. Additionally, event-triggered control has proven effective in reducing bandwidth consumption. However, challenges remain in combining flocking requirements with energy-dissipating terms. Therefore, we propose a novel event-triggered flocking controller suitable for port-Hamiltonian systems. The method is validated through numerical simulations of a group of hexarotors. © 2017 IEEE.

关键词： flocking passivity Port-Hamiltonian systems event-triggered control multi-agent systems

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CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival Prediction for Mobile Robots

CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jonas le Fevre Sejersen Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

This study presents the conflict-aware multi-agent estimated time of arrival (CAMETA) framework, a novel approach for predicting the arrival times of multiple agents in unstructured environments without predefined road infrastructure. The CAMETA framework consists of three components: a path planning layer generating potential path suggestions, a multi-agent ETA prediction layer predicting the arrival times for all agents based on the paths, and lastly, a path selection layer that calculates the accumulated cost and selects the best path. The novelty of the CAMETA framework lies in the heterogeneous map representation and the heterogeneous graph neural network architecture. As a result of the proposed novel structure, CAMETA improves the generalization capability compared to the state-of-the-art methods that rely on structured road infrastructure and historical data. The simulation results demonstrate the efficiency and efficacy of the multi-agent ETA prediction layer, with a mean average percentage error improvement of 29.5% and 44% when compared to a traditional path planning method (A *) which does not consider conflicts. The performance of the CAMETA framework shows significant improvements in terms of robustness to noise and conflicts as well as determining proficient routes compared to state-of-the-art multi-agent path planners.

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Reinforcement Learning-Based Optimal Multiple Waypoint Navigation

Reinforcement Learning-Based Optimal Multiple Waypoint Navig...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Christos Vlachos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos Department of Electrical and Computer Engineering University of Patras Greece School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to minimize a cost function. The main novelty of the proposed scheme lies in the method that provides optimal policies for multiple final positions, in contrast to most existing methodologies that consider a single final configuration. An assessment of the optimality of our results is conducted by comparing our novel motion planning scheme against a RRT* method.

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Enhancement of Two-Photon Fluorescence Microscopy Through Auxiliary Microspheres for Microscopic Observation

Enhancement of Two-Photon Fluorescence Microscopy Through Au...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Feng Zhang Zijian Jin Chaodi Jiang Shuai Yuan Yongliang Yang Xiaoduo Wang School of Electrical & Control Engineering Shenyang Jianzhu University Shenyang Liaoning Province China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang Liaoning Province China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

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 solve this challenge in this study, it can improve high-resolution imaging. In experiments using two-photon fluorescence microscopy, the half-width height (FWHM) of red fluorescent nanoparticles with a diameter of 20 nm was observed to be 296 nm using a primitive microscope system. In this paper by combining barium titanate microspheres with two-photon fluorescence microscopy, the accuracy of the point spread function of the nanoparticles can be improved by a factor of about 1.65, which results in a significant reduction of their half-width height measurements to 179 nm. The research is of great significance to improve the resolution of two-photon fluorescence microscope. This progress is very valuable for studying biological tissues and cell structures, it provides a new impetus for the sustainable development in the microscopic imaging field.

关键词： Nanoparticles Accuracy Barium Microscopy Refractive index High-resolution imaging Fluorescence Titanium compounds Sustainable development Intelligent systems

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Learning a Better control Barrier Function

arXiv

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arXiv 2022年

作者： Dai, Bolun Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

control barrier functions (CBFs) are widely used in safety-critical controllers. However, constructing a valid CBF is challenging, especially under nonlinear or non-convex constraints and for high relative degree systems. Meanwhile, finding a conservative CBF that only recovers a portion of the true safe set is usually possible. In this work, starting from a "conservative" handcrafted CBF (HCBF), we develop a method to find a CBF that recovers a reasonably larger portion of the safe set. Since the learned CBF controller is not guaranteed to be safe during training iterations, we use a model predictive controller (MPC) to ensure safety during training. Using the collected trajectory data containing safe and unsafe interactions, we train a neural network to estimate the difference between the HCBF and a CBF that recovers a closer solution to the true safe set. With our proposed approach, we can generate safe controllers that are less conservative and computationally more efficient. We validate our approach on two systems: a second-order integrator and a ball-on-beam. © 2022, CC BY.

关键词： controllers

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PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware Object Masking

PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Micha Heiß Jakob Grimm Hansen Dengyun Li Michał Kozłowski Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

This paper proposes PredictiveSLAM, a novel extension to ORB-SLAM2, which extracts features from specific regions of interest (ROI). The proposed method was designed with the risk posed both to humans and robotic systems in large-scale industrial sites in mind. The ROI are determined through an object detection network trained to detect moving human beings. The method detects and removes humans from feature extraction, predicting their potential future trajectory. This is done by omitting a specific ROI from extraction, deemed to be occluded in consecutive time steps. Two masking methods -static object and moving object trajectories - are proposed. This approach improves tracking accuracy and the performance of SLAM by removing the dynamic features from the reference for tracking and loop closures. The method is tested on data collected in a laboratory environment and compared against a state-of-the-art ground truth system. The validation data was collected from real-time experiments which aimed at simulating the typical human worker behaviours in industrial environments using an unmanned aerial vehicle (UAV). This study illustrates the advantages of the proposed method over earlier approaches, even with a highly dynamic camera setup on a UAV working in challenging environments.

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Maintaining Balance of Mobile Manipulators for Safe Pick-Up Tasks

Maintaining Balance of Mobile Manipulators for Safe Pick-Up ...

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International Conference on control, Automation, robotics and Vision (ICARCV)

作者： Francesco D'Orazio Tommaso Belvedere Spyridon G. Tarantos Giuseppe Oriolo Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy CNRS Université de Rennes Inria IRISA Rennes France Center of AI & Robotics (CAIR) and Engineering Division New York University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This paper presents a novel method to maintain the dynamic balance of a Mobile Manipulator (MM) during the pick-up of heavy objects. The approach entails the generation of a preliminary reach-to-grasp trajectory, which is subsequently refined by an Optimization-Based controller (OBC) formulated as a Quadratic Program (QP). The trajectory is modified in a minimal fashion to ensure that the robot maintains balance during the reaching phase and remains balanced when the payload is grasped. This is accomplished by incorporating a balance constraint into the OBC that predicts the Zero Moment Point (ZMP) position of the robot at the beginning of the pick-up phase. This accounts for the gravitational and inertial effects that the object has on the robot. The method is validated through simulations conducted with the TIAGo robot in Gazebo. The results demonstrate that the proposed approach effectively prevents the robot from tipping over when the payload is considered.

关键词： Simulation Wheels Stability analysis Robustness Trajectory Safety Mobile robots Robots Manipulator dynamics Payloads

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Protein-Folding Inspired Programmable control System for Robot Swarms

Protein-Folding Inspired Programmable Control System for Rob...

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IEEE International Conference on robotics and Biomimetics

作者： Feng Zhang Yuping Gu Zebin Wang Shuai Yuan Yongliang Yang School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang Liaoning China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Liaoning China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang Liaoning China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In swarm robotics, most of the control algorithms are inspired by the behaviors of animal swarms. The capability gap between robots and animals, however, hinders the reliability of these algorithms. Toward developing reliable algorithms of robot swarms, a promising solution is learning from the collective behaviors of low-intelligent natural swarms, such as cell collectives and molecular assemblies. Here, we proposed a programmable control system inspired by protein folding. Using our homemade RoboFold platform, we demonstrated a protein-folding inspired programmable control method for robot swarms. By programming and designing the sequence of a thread of protein-like robots, the developed system can mimic the formation of hierarchical structures in robot swarms as the three-level of protein structures. We quantitatively characterized the specific features of self-organized morphologies. The experimental results together demonstrated the feasibility of this method. To summarize, this work successfully translated the principles of protein-folding into programming methods to control the morphology of robot swarms. Considering that the protein folding problem is essentially a physicochemical problem, this system is not only beneficial for the control of swarm robots but also has the potential to develop into a tool for studying the protein folding problem.

关键词： Proteins Vibrations Programmable control Animals Morphology Swarm robotics Programming control systems Robustness Robots

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