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26th International Conference on Miniaturized systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Durrer, Jan Agrawal, Prajwal Ahmed, Daniel Acoustic Robotics Systems Laboratory Institute for Mechanical Systems Department of Mechanical and Process Engineering ETH Zurich Rushlikon8803 Switzerland

ISBN: (纸本)9781733419048

With the development of microfluidics, fluid manipulation is becoming an increasingly critical process. In the macroscale environment, numerous robotic systems are commercially available, however development of these systems at the microscale requires expensive equipment's and reagents, as well as sample preparation at the microscale is relatively difficult. This work integrates robotics and microfluidics in order to demonstrate a robot-assisted acoustofluidic microscale manipulation system (RAMS), incorporating an acoustofluidic end effector and a robotic arm. This RAMS device will open the door to new potential applications such as micromixing, pumping, particle manipulation, and transfection, for example. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized systems for Chemistry and Life Sciences. All rights reserved.

关键词： End effectors

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A multi-step Gaussian filtering approach to reduce the effect of non-Gaussian distribution in aerial localization of an RF source in NLOS condition

A multi-step Gaussian filtering approach to reduce the effec...

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2013 1st RSI/ISM International Conference on robotics and Mechatronics, ICRoM 2013

作者： Dehghan, Seyyed M. Mehdi Moradi, Hadi Advanced Robotics and Intelligent Systems Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781467358118

The hybrid localization using Angle of Arrival (AOA) and Received Strength Signal Indicator (RSSI) of an RF source, such as a cell phone in a search and rescue mission, with unknown power and None Line Of Sight (NLOS) condition have been proven to be advantageous compared to using each method separately. The hybrid approach has been proposed to benefit from both RSSI and AOA measurements. In this paper, the initial hybrid method, which was implemented using particle filters due to the multi-modal/non-Gaussian nature of localization in NLOS condition, has been replaced by a multi-step Gaussian filtering approach which provides nearly similar accuracy with better performance. The proposed method has been implemented using extended Kalman filter and Unscented Kalman filter. The simulation results show that the multi-step Gaussian filtering is comparable to particle filter in all cases with better performance. For further evaluation, the effects of uncertainty in the propagation parameters have been studied to show the robustness of each filter to these uncertainties. © 2013 IEEE.

关键词： Extended Kalman filters

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Human Localization in Warehouse Environments based on a Wearable Camera Sensor Suite and Dynamic Ultra-Wide Band Nodes 20

Human Localization in Warehouse Environments based on a Wear...

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20th International Conference on Advanced robotics, ICAR 2021

作者： Popovic, Goran Cviic, Igor Markovic, Ivan Petrovic, Ivan Laboratory For Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9781665436847

Modern warehouses, equipped with an autonomous robots fleet, suffer from an efficiency deficit during human interventions in the shop floor area, since, during such interventions, all the robots must stop for safety reasons. This particularly affects large warehouses which could extremely benefit from a solution that would allow human-robot collaboration during such interventions. This solution should only stop the robots that are in the vicinity of the human and reroute the others, and it requires the following prerequisites: (i) reliable relative ranging of robots with respect to human workers that would ensure safety, and (ii) reliable localization of human workers in the warehouse. In this paper we propose a system for reliable human localization that is based on fusing cues from measured relative ranges with respect to moving robots and a camera system that is worn by the human worker. Since global locations of the robots in the warehouse are known, we use the relative ranges to robots in an indoor GPS-like fashion to provide global human location updates, while the wearable cameras provide visual odometry, i.e., the relative motion of the human, and another global cue by detecting ground fiducial markers used by robots. The fusion is performed within a graph-based optimization framework that also includes a visual odometry error model. We carried out an experimental analysis on three different datasets to validate the performance of the proposed approach and compared it to a state-of-the-art visual SLAM solution, namely ORB-SLAM2. The results demonstrate that our approach yields reliable human location within warehouse like environments. © 2021 IEEE.

关键词： Warehouses

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Visibility augmentation of the proportional navigation guidance

Visibility augmentation of the proportional navigation guida...

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AIAA Guidance, Navigation, and Control Conference, 2017

作者： Tardioli, Luca Franzini, Giovanni Pollini, Lorenzo Innocenti, Mario Automation Robotics and Autonomous Systems Laboratory Department of Information Engineering University of Pisa Pisa56122 Italy

ISBN: (数字)9781624104503

ISBN: (纸本)9781624104503

The paper presents a visibility augmentation method for a classical proportional navigation guidance law that helps the pursuer maintaining visual contact with the target, and also allows to recover loss of line-of-sight. The concept of visibility is formally introduced in a modified pursuit-evasion differential game, and it appears in the Lagrangian term of the players’ cost functions. Target visibility is then quantified by means of a visibility function, which adds an augmentation term to the proportional navigation structure. Numerical simulations are used for validation. A further simplification of the guidance law is also presented, using the same arguments that lead to the proportional navigation guidance simplest expression, i.e. considering the players on a nominal collision course. The new simplified law is validated in simulation, and compared against its original expression. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Visibility

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Computationally efficient dense moving object detection based on reduced space disparity estimation

Computationally efficient dense moving object detection base...

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作者： Popović, Goran Hadviger, Antea Marković, Ivan Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Zagreb Croatia

Computationally efficient moving object detection and depth estimation from a stereo camera is an extremely useful tool for many computer vision applications, including robotics and autonomous driving. In this paper we show how moving objects can be densely detected by estimating disparity using an algorithm that improves complexity and accuracy of stereo matching by relying on information from previous frames. The main idea behind this approach is that by using the ego-motion estimation and the disparity map of the previous frame, we can set a prior base that enables us to reduce the complexity of the current frame disparity estimation, subsequently also detecting moving objects in the scene. For each pixel we run a Kalman filter that recursively fuses the disparity prediction and reduced space semi-global matching (SGM) measurements. The proposed algorithm has been implemented and optimized using streaming single instruction multiple data instruction set and multi-threading. Furthermore, in order to estimate the process and measurement noise as reliably as possible, we conduct extensive experiments on the KITTI suite using the ground truth obtained by the 3D laser range sensor. Concerning disparity estimation, compared to the OpenCV SGM implementation, the proposed method yields improvement on the KITTI dataset sequences in terms of both speed and accuracy. © 2018

关键词： Stereo vision

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Path planning for localization of an RF source by multiple UAVs on the Crammer-Rao lower bound

Path planning for localization of an RF source by multiple U...

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2013 1st RSI/ISM International Conference on robotics and Mechatronics, ICRoM 2013

作者： Dehghan, Seyyed M. Mehdi Tavakkoli, Mohammad Saberi Moradi, Hadi Advanced Robotics and Intelligent Systems Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781467358118

This paper presents a path planning approach of several UAVs for Radio Frequency (RF) source localization in None Line Of Sight (NLOS) propagation condition using the Received Signal Strength Indication (RSSI). The paths are planned such that the lower bound of standard deviation of localization error, which is equivalent to the Cramer-Rao Lower Bound (CRLB) of the estimation, minimized at any time. Due to the complexity of Jacobin calculation to perform global CRLB optimization, the local values of CRLBs in the current waypoint and next probable waypoints are used in the steepest decent approach to determine the best path. Furthermore, the complexity is reduced by discretizing the space for UAVs to make the computation feasible. The effect of NLOS propagation on the RSSI measurements is simulated by a log-normal distribution and the last estimation of radio source location is used to calculate the local CRLBs. The proposed approach has been simulated and compared with the basic bio-inspired approach of going toward the sensed direction of the source. The result shows better performance than the basic approach. © 2013 IEEE.

关键词： Antennas

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Relay Selection and Power Control for Mobile Underwater Acoustic Communication Networks: A Dual-Thread Reinforcement Learning Approach

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期9卷 1-1页

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Key Lab of Industrial Computer Control Engineering of Hebei Province China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China

This paper deals with a cooperation communication problem (relay selection and power control) for mobile underwater acoustic communication networks. To achieve satisfactory transmission capacity, we propose a reinforcement-learning-based cooperation communication scheme to efficiently resist the highly dynamic communication links and strongly unknown time-varying channel states caused by the mobility of Autonomous Underwater Vehicles (AUVs). Firstly, a particular Markov decision process is developed to model the dynamic relay selection process of mobile AUV in the unknown scenario. In the developed model, an experimental statistical-based partition mechanism is proposed to cope with the greatly increasing dimension of the state space caused by the mobility of AUV, reducing the search optimization difficulty. Secondly, a dual-thread reinforcement learning structure with actual and virtual learning threads is proposed to efficiently track the superior relay action. In the actual learning thread, the proposed improved probability greedy policy enables the AUV to strengthen the exploration for the reward information of potential superior relays on the current state. Meanwhile, in the virtual learning thread, the proposed upper-confidence-bound-index-based uncertainty estimation method can estimate the action-reward level of historical states. Consequently, the combination of actual and virtual learning threads can efficiently obtain satisfactory Q value information, thereby making superior relay decision-making in a short time. Thirdly, a power control mechanism is proposed to reuse the current observed action-reward information and transform the multiple unknown parameter nonlinear joint power optimization problem into a convex optimization problem, thereby enhancing network transmission capacity. Finally, simulation results verify the effectiveness of the proposed scheme. IEEE

关键词： Convex optimization

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A policy gradient algorithm integrating long and short-term rewards for soft continuum arm control

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Science China(Technological Sciences) 2022年第10期65卷 2409-2419页

作者： DONG Xiang ZHANG Jing CHENG Long XU WenJun SU Hang MEI Tao School of Electrical Engineering and Automation Anhui UniversityHefei 230601China State Key Laboratory for Control and Management of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Robotics Research Center Peng Cheng LaboratoryShenzhen 518055China

The soft continuum arm has extensive application in industrial production and human life due to its superior safety and flexibility. Reinforcement learning is a powerful technique for solving soft arm continuous control problems, which can learn an effective control policy with an unknown system model. However, it is often affected by the high sample complexity and requires huge amounts of data to train, which limits its effectiveness in soft arm control. An improved policy gradient method, policy gradient integrating long and short-term rewards denoted as PGLS, is proposed in this paper to overcome this issue. The shortterm rewards provide more dynamic-aware exploration directions for policy learning and improve the exploration efficiency of the algorithm. PGLS can be integrated into current policy gradient algorithms, such as deep deterministic policy gradient(DDPG). The overall control framework is realized and demonstrated in a dynamics simulation environment. Simulation results show that this approach can effectively control the soft arm to reach and track the targets. Compared with DDPG and other model-free reinforcement learning algorithms, the proposed PGLS algorithm has a great improvement in convergence speed and performance. In addition, a fluid-driven soft manipulator is designed and fabricated in this paper, which can verify the proposed PGLS algorithm in real experiments in the future.

关键词： soft arm control Cosserat rod deep reinforcement learning policy gradient algorithm high sample complexity

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EFFICIENT DISTANCE COMPUTATION USING BEST ELLIPSOID FIT

EFFICIENT DISTANCE COMPUTATION USING BEST ELLIPSOID FIT

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1992 IEEE INTERNATIONAL SYMP ON INTELLIGENT CONTROL

作者： RIMON, E BOYD, SP Robotics Laboratory Stanford University Department of Computer Science United States Information Systems Laboratory Stanford University Department of Electrical Engineering United States

ISBN: (纸本)0780305469

Knowledge of the distance between a robot and its surrounding environment is vital for any robotic system. The robot must obtain this information rapidly in order to plan and react in real-time. Our technique first surrounds the robot links and the obstacles by optimal ellipsoids, and then computes the clearance of the links from the obstacles with a generalized distance function. This approach offers an attractive alternative to the widely used technique of computing the distance via polyhedral representation of the robot and the obstacles. In particular, our approach offers a drastic reduction in the complexity of the data structures: each polyhedron, typically represented by a list of its features and their adjacency graph;is replaced by a minimum-volume ellipsoid represented by its center and a symmetric matrix whose dimension is either two or three (the workspace dimension). Moreover, while the computation time of the distance between polyhedra is often a function of their geometrical complexity, computation time in the ellipsoidal case is essentially constant;and becomes even more rapid when it is computed repeatedly along the robot's trajectory. Our method consists of the following two algorithms: The first computes the optimal ellipsoid surrounding a convex polyhedron. The second is an analytic formula for the free margin about one ellipsoid with respect to another, that is computed as a standard eigenvalue problem. An efficient incremental version of the latter algorithm is then proposed. This system has been implemented and preliminary simulation results are provided throughout the paper. © 1992 IEEE.

关键词： Geometry

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Advanced Centroid State Estimation and Instability Detection Framework Leveraging UKF and AdvSAE-LSTM

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IEEE Sensors Journal 2025年

作者： Mao, Han Zhu, Aibin Tu, Yao Song, Jiyuan Zheng, Chunli Li, Meng Xu, Peng Shi, Lei Liu, Yang Li, Xiao Guan, Zhenpeng Xi'an Jiaotong University Institute of Robotics and Intelligent Systems Shaanxi Key Laboratory of Intelligent Robots Xi'an710049 China Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi'an710049 China Xi'an Jiaotong University School of Energy and Power Engineering Xi'an710049 China Xi'an Jiaotong University School of Mechanical Engineering Xi'an710049 China Xi'an Jiaotong University Honghui Hospital Xi'an710000 China Xi'an Jiaotong University Department of Sports Xi'an710049 China Xi'an Jiaotong University Second Affiliated Hospital Department of General Surgery Xi'an710004 China Fourth Medical Center of PLA General Hospital Department of Rehabilitation Senior Department of Orthopedics Beijing100048 China Sports Medicine and Rehabilitation National Clinical Research Center for Orthopedics Beijing100048 China Peking University Shougang Hospital Orthopedics Department Beijing100144 China

With the growing aging population, fall detection has become a significant research focus in recent years. However, most existing studies have not effectively improved the accuracy of centroid state estimation in human instability, particularly in terms of multi-source information fusion. To address this, we propose a centroid state estimation method based on the Unscented Kalman Filter (UKF), integrating data from the five-link model and trunk IMU sensors, significantly enhancing both the accuracy and robustness of centroid state estimation. Experimental results demonstrate that the proposed method achieves estimation errors of 0.0099 m and 0.0226 m/s for centroid displacement and velocity in the x-direction, and 0.0006 m and 0.0099 m/s in the y-direction, indicating high accuracy. To overcome the reliance of traditional supervised learning methods on large amounts of labeled data for instability state estimation, we propose an unsupervised learning-based instability state estimation method, featuring the AdvSAE-LSTM model. Data from different sensors are preprocessed and independent training datasets are constructed, after which the feature maps from each sensor are concatenated to form a global feature map. Compared to baseline methods, the proposed approach significantly improves instability state detection performance. Specifically, when the SAS threshold is set to 0.02, both the False Positive Rate (FPR) and False Negative Rate (FNR) are 0%, with a detection delay of only 77.16 ms, achieving highly efficient and accurate estimation of human instability states. © 2025 IEEE.

关键词： Kalman filters

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