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IEEE SENSORS

作者： Elisabetta Leogrande Francesco Dell'Olio Stefano Mazzoleni Calogero Maria Oddo Mariangela Filosa Department of Electrical and Information Engineering Polytechnic University of Bari Italy Department of Excellence in Robotics & AI The BioRobotics Institute Sant' Anna School of Advanced Studies Pisa Italy Interdisciplinary Research Center Health Science Sant’ Anna School of Advanced Studies Pisa Italy

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

The field of collaborative robotics is constantly growing given the central role that safe human-robot interactions are gaining in the current era of Industry 4.0. To act and interact, robotic systems are increasingly being either natively equipped with or adapted to house sensing devices. In this scenario, tactile sensors are fundamental to detect impacts, accurately manipulate objects and enable learning from demonstration strategies. The development of such systems requires technological efforts in terms of both design, for their seamless integration in robots, and software programming, for achieving reliable and prompt reactions to relevant events. In this study, an edge AI algorithm deployed on an embedded device for the recognition of contacts on an FBG (Fiber Bragg Grating)-based e-skin is presented. A Convolutional Neural Network has been trained offline to localize touch events occurred on the e-skin. The performance of the algorithm assessed on the test set reached a mean absolute error on the three cartesian coordinates of less than 4 mm. The algorithm has been then ported to a microcontroller board for real-time testing of both simulated and concurrent contacts. The achievements of this work pave the way to new opportunities for collaborative robotics applications through intelligent FBG-based artificial tactile skins.

关键词： Service robots Robot kinematics Software algorithms Tactile sensors Collaboration Edge AI Fiber gratings Software Software reliability Testing

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Cooperative Aerial Robot Inspection Challenge: A Benchmark for Heterogeneous Multi-UAV Planning and Lessons Learned

arXiv

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arXiv 2025年

作者： Cao, Muqing Nguyen, Thien-Minh Yuan, Shenghai Anastasiou, Andreas Zacharia, Angelos Papaioannou, Savvas Kolios, Panayiotis Panayiotou, Christos G. Polycarpou, Marios M. Xu, Xinhang Zhang, Mingjie Gao, Fei Zhou, Boyu Chen, Ben M. Xie, Lihua Robotics Institute Carnegie Mellon University United States School of Electrical and Electronic Engineering Nanyang Technological University Singapore KIOS Research Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Cyprus School of Artificial Intelligence Sun Yat-sen University China Zhejiang University China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

We propose the Cooperative Aerial Robot Inspection Challenge (CARIC), a simulation-based benchmark for motion planning algorithms in heterogeneous multi-UAV systems. CARIC features UAV teams with complementary sensors, realistic constraints, and evaluation metrics prioritizing inspection quality and efficiency. It offers a ready-to-use perception-control software stack and diverse scenarios to support the development and evaluation of task allocation and motion planning algorithms. Competitions using CARIC were held at IEEE CDC 2023 and the IROS 2024 Workshop on Multi-Robot Perception and Navigation, attracting innovative solutions from research teams worldwide. This paper examines the top three teams from CDC 2023, analyzing their exploration, inspection, and task allocation strategies while drawing insights into their performance across scenarios. The results highlight the task’s complexity and suggest promising directions for future research in cooperative multi-UAV systems. © 2025, CC BY-NC-ND.

关键词： Motion planning

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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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Systematic control of Electro-hydraulic Velocity Servomechanism using Fractional Order Technique

Systematic Control of Electro-hydraulic Velocity Servomechan...

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International Conference on control, Automation and Diagnosis (ICCAD)

作者： Muhammad Majid Gulzar Fahad Usman Khan Daud Sibtain Adnan Shakoor Muhammad Khalid Department of Control & Instrumentation Engineering Department of Electrical Engineering University of Central Punjab Lahore Pakistan Energy Center of Excellence (ECoE) Harbin Electric International Harbin China Electrical Engineering Department Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

Electro-Hydraulic ServoMechanism (EHSM) is a nonlinear system with tentative dynamics, whose control is a difficult task. In this research, an alternative to the existing technique, a fractional order PID control-based approach, is presented. Initially, a SIMULINK based nonlinear model is constructed. The linear model is obtained by approximating the non-linearity using the bilinear transformation. Pole placement is blended with the Ziegler-Nichols tuning method to carry out the controller design and controller parameter tuning. The results are compared with conventional PID, and stability analysis is also analyzed. The results of the fractional order controller show satisfactory improvement than the results of conventional PID.

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Fuzzy control system design for wheel slip prevention and tracking of desired speed profile in electric trains

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Asian Journal of control 2022年第1期24卷 388-400页

作者： Moaveni, Bijan Rashidi Fathabadi, Fatemeh Molavi, Ali Center of excellence for Modelling and Control of Complex Systems Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Western Michigan University MI United States Institute of Robotics and Industrial Informatics Polytechnical University of Catalonia Barcelona Spain

This study proposes an anti-slip control system for electric trains based on the fuzzy logic theory, which prevents the wheels from slipping during the acceleration and simultaneously tracks the desired speed profile. To improve the control performance, the train longitudinal velocity and the slip ratio are estimated. By using a field-oriented control (FOC), the angular speed of the traction motor is controlled. The fuzzy control system determines the desired angular speed of the traction motor as the reference input of FOC to obtain the desired slip ratio and track the desired speed profile. Simulation results show the effectiveness of the control system in various wheel–rail surface conditions based on the real parameters of ER24PC locomotive. © 2020 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Traction motors

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End-to-end Autonomous Driving in Heterogeneous Traffic Scenario Using Deep Reinforcement Learning

End-to-end Autonomous Driving in Heterogeneous Traffic Scena...

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European control Conference (ECC)

作者： Soumyajit Chakraborty Subhadeep Kumar Nirav Bhatt Ramkrishna Pasumarthy Department of Electrical Engineering Indian Instiute of Technology Madras Chennai India Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence Indian Institute of Technology Madras Chennai India Department of Biotechnology Center of Data Sciences and Artificial Intelligence Prospective Center of Excellence for Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai India Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence and Prospective Center of Excellence for Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai India

In this paper, we propose an end-to-end autonomous driving architecture for safe maneuvering in heterogeneous traffic using a reinforcement learning (RL) algorithm. Using the proposed architecture we develop an RL agent that can make driving decisions directly from the sensor data. We formulate the autonomous driving problem as a Markov Decision Process and propose different architectures using Deep $Q -$Networks for two types of sensor data - top view images of the autonomous vehicle (AV) and its surrounding vehicles and information on relative position and velocities of the surrounding vehicles w.r.t the AV. We consider a highway scenario and analyze the performance of the RL agent using the proposed architectures using the highway-env simulator. We compare the driving performance of the AV for both sensor types and discuss their efficacy under varying traffic densities.

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Design and control of an Omnidirectional Aerial Robot with a Miniaturized Haptic Joystick for Physical Interaction

Design and Control of an Omnidirectional Aerial Robot with a...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Julien Mellet Andrea Berra Salvatore Marcellini Miguel Ángel Trujillo Soto Guillermo Heredia Fabio Ruggiero Vincenzo Lippiello Department of Electrical Engineering and Information Technology PRISMA Lab University of Naples Federico II Naples Italy CATEC Advanced Center for Aerospace Technologies Seville Spain Leonardo Innovation Labs Rome Italy GRVC Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

Fully actuated aerial robots have shown superiority in Aerial Physical Interaction (APhI) in recent years. This work presents a minimal setup for aerial telemanipulation, improving accessibility to such technologies. The design and control of a 6-Degrees of Freedom (DoF) joystick with 4-DoF haptic feedback are detailed. It is the first haptic device with standard Remote controller (RC) form factor for APhI. Miniaturizing the haptic device adds sense of touch to RC, enhancing physical awareness. The goal is to provide operators with an extra sense-beyond vision and sound-to support safe APhI. To the best of the authors' knowledge, this is the first 6- DoF aerial teleoperation system capable of decoupling single-axis input commands. The proposed robot hardware design reduces number of components, aiming for easier maintenance and improved force and thrust-to-weight ratios. Open-source physics-based simulation and successful early flight tests high-light the tool's promise for future APhI applications.

关键词： Translation Shape Autonomous aerial vehicles Robot sensing systems Hardware Stability analysis Software Haptic interfaces Maintenance Standards

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Distributed formation control of networked mechanical systems

arXiv

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arXiv 2022年

作者： Javanmardi, Najmeh Borja, P. Yazdanpanah, M.J. Scherpen, J.M.A. Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach. © 2022, CC BY.

关键词： Networked control systems

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SAD-TIME: a Spatiotemporal-fused network for depression detection with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor

arXiv

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arXiv 2024年

作者： Wang, Han-Guang Hou, Hui-Rang Jin, Li-Cheng Xu, Chen-Yang Zhang, Zhong-Yi Meng, Qing-Hao Zhang, Yong-Hui Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China Department of Psychiatry Tianjin Anding Hospital Mental Health Center Tianjin Medical University China

Background and Objective: Depression is a severe mental disorder, and accurate diagnosis is pivotal to the cure and rehabilitation of people with depression. However, the current questionnaire-based diagnostic methods could bring subjective biases and may be denied by subjects. In search of a more objective means of diagnosis, researchers have begun to experiment with deep learning-based methods for identifying depressive disorders in recent years. Methods: In this study, a novel Spatiotemporal-fused network with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor (SAD-TIME) is proposed. SAD-TIME incorporates an automated nodes’ common features extractor (CFE), a spatial sector (SpS), a modified temporal sector (TeS), and a domain adversarial learner (DAL). The CFE includes a multiscale depth-wise 1D-convolutional neural network and a time-interval embedding generator, where the unique information of each channel is preserved. The SpS fuses the functional connectivity with the distance-based connectivity containing spatial position of EEG electrodes. A multi-head-attention graph convolutional network is also applied in the SpS to fuse the features from different EEG channels. The TeS is based on long short-term memory and graph transformer networks, where the temporal information of different time-windows is fused. Moreover, the DAL is used after the SpS to obtain the domain-invariant feature. Results: Experimental results under tenfold cross-validation show that the proposed SAD-TIME method achieves 92.00% and 94.00% depression classification accuracies on two datasets, respectively, in cross-subject mode. Conclusion: SAD-TIME is a robust depression detection model, where the automatedly-generated features, the SpS and the TeS assist the classification performance with the fusion of the innate spatiotemporal information in the EEG signals. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Noise Mitigation Approach for Improving Position Estimate in Indoor Vehicle Navigation Using Inertial Measurement Unit

A Noise Mitigation Approach for Improving Position Estimate ...

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control Conference (ICC), 2019 Sixth Indian

作者： Vasumathi R Subhadeep Kumar Ramkrishna Pasumarthy Nirav Bhatt Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence Indian Institute of Technology Madras Chennai India Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence and Prospective Center of Excellence for Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai India Department of Biotechnology Robert Bosch Center of Data Sciences and Artificial Intelligence Prospective Center of Excellence for Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai India

Accurate position estimation, especially in indoor environments, presents a formidable challenge despite significant advancements in estimation methodologies. The integration of additional sensors is often imperative to refine estimates, particularly within indoor settings. The choice of additional sensors depends on the specific applications and operational range. This study focuses on devising a position estimation framework utilizing the internal sensors such as the Inertial Measurement Unit (IMU) and wheel encoders within a scaled-down Electric Vehicle. The proposed approach entails a combination of crafting a digital filter and implementing a Kalman filter to effectively attenuate noise and errors in the IMU data, thereby enhancing the position estimate, and the methodology is validated through experiments. This work contributes to the broader discourse on advancing indoor position estimation while bypassing the need for external positioning systems.

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