Article
Engineering, Electrical & Electronic
Dong Pu, Xueyong Wei, Wenxin Zhu, Kai Chen, Zhuangde Jiang, Ronghua Huan
Summary: In this study, a sensing enhancement technique based on synchronization is proposed to improve the performance of a resonant micromechanical charge sensor. Experimental results demonstrate that the sensitivity and frequency stability of the sensor can be significantly improved by frequency locking induced by synchronization.
SENSORS AND ACTUATORS A-PHYSICAL
(2022)
Article
Computer Science, Interdisciplinary Applications
Yanzhe Wang, Lai Wei, Kunpeng Du, Gongping Liu, Qian Yang, Yanding Wei, Qiang Fang
Summary: The paper proposes a new online collision avoidance trajectory planning algorithm for ensuring human safety during collaborative tasks. The algorithm consists of trajectory generation and local optimization. A neural network trajectory planner called CWP-net is introduced, which generates key waypoints for dynamic obstacle avoidance based on DPGMM distribution learning. The generated trajectories are then locally optimized using an improved STOMP algorithm, constraining the optimization range and direction through the DPGMM model. Simulations and real experiments demonstrate the algorithm's ability to smoothly adjust paths and effectively avoid collisions in human-robot collaboration scenarios.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2023)
Article
Automation & Control Systems
DongHyun Ahn, Baek-Kyu Cho
Summary: This article presents a strategy for generating an optimal jumping motion in real time for legged robots. The proposed method divides the jumping trajectory into vertical and horizontal directions. The vertical motion ensures continuous center of mass position, speed, and acceleration, while minimizing torque and joint speed. The horizontal motion uses a novel model predictive control with a height varying inverted pendulum model to achieve stable jumping and desired take-off velocity.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Aerospace
Xin Wang, Pei Dai, Xiaoming Cheng, Yunzhao Liu, Jiashan Cui, Lihua Zhang, Dongzhu Feng
Summary: This paper applies a neural network to generate the online ascent trajectory of a solid rocket, overcoming the poor real-time performance of traditional trajectory optimization methods. The neural network approximation reduces the burden of onboard computation, and the offline training of two neural networks based on different environment characteristics enables real-time control and trajectory optimization. Numerical simulations demonstrate the effectiveness and robustness of the proposed scheme.
AEROSPACE SCIENCE AND TECHNOLOGY
(2022)
Article
Optics
Ziwei Xu, Huan Tian, Zhen Zeng, Lingjie Zhang, Yali Zhang, Xinhai Zou, Zhiyao Zhang, Shangjian Zhang, Heping Li, Yong Liu
Summary: An approach is proposed and verified for generating chaotic signals with low time-delay signatures (TDSs) from a semiconductor laser (SCL) based on optoelectronic hybrid feedback. By using a chirped fiber Bragg grating (CFBG) to provide distributed feedback and assisted by nonlinear optoelectronic feedback, the relaxation oscillation effect in the SCL is effectively suppressed, resulting in a chaotic signal with a low TDS and an enlarged effective bandwidth.
Article
Mathematics, Applied
Antonio Mihara, Everton S. Medeiros, Anna Zakharova, Rene O. Medrano-T
Summary: The emergence of synchronized behavior in networking dynamical systems is influenced by the density of connections. Sparse networks can have multiple coexistent solutions, making the convergence to complete synchronized states uncertain. This study discovers the phenomenon of sparsity-driven synchronization and presents a method to determine the minimum number of links to ensure complete synchronization.
Article
Automation & Control Systems
Huanzhong Chen, Xuechao Chen, Chencheng Dong, Zhangguo Yu, Qiang Huang
Summary: This article explores a running pattern generation method that combines the advantages of a simplified model and a full-body model, and can optimize the whole-body motion online. By utilizing a three-body model and reference-tracking dynamics, the scale of the problem is greatly reduced and the problem is further simplified.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Automation & Control Systems
Alexey Bobtsov, Bowen Yi, Romeo Ortega, Alessandro Astolfi
Summary: This paper addresses the problem of estimating constant parameters from a standard vector linear regression equation in the absence of sufficient excitation in the regressor. It proposes transforming the equation into a set of scalar ones and generating new scalar exciting regressors. The superior performance of a classical gradient estimator using the new regressor is illustrated through comprehensive simulations.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Engineering, Electrical & Electronic
Liangjie Sun, Wai-Ki Ching, Shiyong Zhu, Jianquan Lu
Summary: This paper investigates the synchronization design problem for both Boolean networks (BNs) and singular BNs. It proposes a method based on state observers for designing synchronized response (singular) BNs. By constructing response (singular) BNs that achieve synchronization with a given (singular) BN, the gain matrix of the designed state observer can be obtained. The method allows for determining and adjusting the accurate estimated time for the state observers and designing all possible state observers in the considered form.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Article
Automation & Control Systems
Xiang Meng, Zhangguo Yu, Xuechao Chen, Zelin Huang, Fei Meng, Qiang Huang
Summary: This paper investigates the problem of online motion generation for humanoid robots on non-flat terrain. It proposes an Efficient Behavior Generator (EBG) and a Nonlinear Centroidal Model Predictive Controller (NC-MPC) as the sequential components. The EBG optimizes physically feasible whole-body template behaviors, providing reliable warm-starts for NC-MPC and reducing computational effort. The NC-MPC generates reactive motion online to adapt to the real local environment. The effectiveness of synthesizing EBG and NC-MPC for humanoid locomotion on non-flat terrain is validated through simulation and experiments with the humanoid robot BHR7P.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Acoustics
Leonardo Gabrielli, Stefano D'Angelo, Pier Paolo La Pastina, Stefano Squartini
Summary: In the last decades, efficient methods have been proposed to generate waveforms with reduced aliasing, proving the field's maturity. However, the introduction of Antiderivative Antialiasing (AA) methods for reducing aliasing in nonlinear discrete-time processing sheds new light on bandlimited oscillators and provides a general method for dealing with aliasing in arbitrary waveform generation.
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Wentao Liu, Dong Li, Tianhao Liang, Tingting Zhang, Zhi Lin, Naofal Al-Dhahir
Summary: This paper studies the optimization problem of information collection in UAV-assisted network and proposes a deep reinforcement learning algorithm. The experimental results demonstrate that the proposed algorithm can significantly improve the performance of UAV-assisted network.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2023)
Article
Polymer Science
Kai Li, Fenghui Gan, Changshen Du, Guojun Cai, Junxiu Liu
Summary: This paper investigates the synchronization of two coupled oscillators in a self-oscillating system composed of a passive oscillator and an active liquid crystal elastomer self-oscillator. Three synchronous regimes are identified: static, in-phase, and anti-phase. The mechanisms of self-oscillations in the in-phase and anti-phase regimes are elucidated by calculating key physical parameters. The study also explores the effects of various factors on the self-oscillations and provides critical conditions for triggering them.
Article
Computer Science, Artificial Intelligence
Luca Rossi, Marina Paolanti, Roberto Pierdicca, Emanuele Frontoni
Summary: Human trajectory prediction is a complex subject that involves challenges such as human-space interaction, human-human interaction, multimodality, and generalizability. This study proposes new deep learning models and datasets to address these challenges and achieve better generalizability in predicting human trajectories. Experimental results demonstrate that the proposed models and datasets outperform state-of-the-art works and better capture the complexities of multimodal scenarios.
PATTERN RECOGNITION
(2021)
Article
Energy & Fuels
Xinyu Long, Mingwei Sun, Minnan Piao, Zengqiang Chen
Summary: This paper optimizes and tracks the trajectory of a high-altitude parafoil in a parameterized manner, considering the complex dynamic characteristics of the parafoil and the requirements of a high-altitude controller. By reformulating the dynamic trajectory optimization problem using the Radau pseudospectral method, the parameterized optimal trajectory with maximum net power generation is obtained. The study demonstrates the effectiveness of the proposed parameterized trajectory optimization and control strategies in regulating the complex nonlinear dynamics of the parafoil.
Article
Automation & Control Systems
Runwei Guan, Shanliang Yao, Lulu Liu, Xiaohui Zhu, Ka Lok Man, Yong Yue, Jeremy Smith, Eng Gee, Yutao Yue
Summary: With the development of Unmanned Surface Vehicles (USVs), the perception of inland waterways has become significant. Traditional RGB cameras cannot work effectively in adverse weather and at night, which has led to the emergence of 4D millimeter-wave radar as a new perception sensor. However, the radar suffers from water-surface clutter and irregular shape of point cloud. To address these issues, this paper proposes a high-performance panoptic perception model called Mask-VRDet, which fuses features of vision and radar using graph neural network.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Adrien Le Reun, Kevin Subrin, Anthony Dubois, Sebastien Garnier
Summary: This study aims to evaluate the quality and health of aerospace parts using a high-dimensional robotic cell. By utilizing X-ray Computed Tomography devices, the interior of the parts can be reconstructed and anomalies can be detected. A methodology is proposed to assess both the raw process capability and the improved process capability, with three strategies developed to improve the robot behavior model and calibration.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Weiming Ba, Jung-Che Chang, Jing Liu, Xi Wang, Xin Dong, Dragos Axinte
Summary: This paper proposes a hybrid scheme for kinematic control of continuum robots, which avoids errors through tension supervision and accurate piecewise linear approximation. The effectiveness of the controller is verified on different continuum robotic systems.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Gabriele Abbate, Alessandro Giusti, Viktor Schmuck, Oya Celiktutan, Antonio Paolillo
Summary: In this study, a learning-based approach is proposed to predict the probability of human users interacting with a robot before the interaction begins. By considering the pose and motion of the user, the approach labels the robot's encounters with humans in a self-supervised manner. The method is validated and deployed in various scenarios, achieving high accuracy in predicting user intentions to interact with the robot.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Tiago Cortinhal, Eren Erdal Aksoy
Summary: This work presents a new depth-and semantics-aware conditional generative model, named TITAN-Next, for cross-domain image-to-image translation between LiDAR and camera sensors. The model is able to translate raw LiDAR point clouds to RGB-D camera images by solely relying on semantic scene segments, and it has practical applications in fields like autonomous vehicles.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Marios Krestenitis, Emmanuel K. Raptis, Athanasios Ch. Kapoutsis, Konstantinos Ioannidis, Elias B. Kosmatopoulos, Stefanos Vrochidis
Summary: This paper addresses the issue of informative path planning for a UAV used in precision agriculture. By using a non-uniform scanning approach, the time spent in areas with minimal value is reduced, while maintaining high precision in information-dense regions. A novel active sensing and deep learning-based coverage path planning approach is proposed, which adjusts the UAV's speed based on the quantity and confidence level of identified plant classes.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Shota Kokubu, Pablo E. Tortos Vinocour, Wenwei Yu
Summary: In this study, a new modular soft actuator was proposed to improve the support performance of soft rehabilitation gloves (SRGs). Objective evaluations and clinical tests were conducted to demonstrate the effectiveness and functionality of the proposed actuator and SRG.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Jinliang Zhu, Yuanxi Sun, Jie Xiong, Yiyang Liu, Jia Zheng, Long Bai
Summary: This paper proposes an active prosthetic knee joint with a variable stiffness parallel elastic actuation mechanism. Numerical verifications and practical experiments demonstrate that the mechanism can reduce torque and power, thus reducing energy consumption and improving the endurance of the prosthetic knee joint.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Yong You, Jingtao Wu, Yunlong Meng, Dongye Sun, Datong Qin
Summary: A new power-cycling variable transmission (PCVT) is proposed and applied to construction vehicles to improve transmission efficiency. A shift correction strategy is developed based on identifying the changes in construction vehicles' mass and gradient. Simulation results show that the proposed method can correct shift points, improve operation efficiency, and ensure a safer operation process.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Shaorui Liu, Wei Tian, Jianxin Shen, Bo Li, Pengcheng Li
Summary: This paper proposes a two-objective optimization technique for multi-robot systems, addressing the issue of balancing productivity and machining performance in high-quality machining tasks.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Pengchao Ding, Faben Zhu, Hongbiao Zhu, Gongcheng Wang, Hua Bai, Han Wang, Dongmei Wu, Zhijiang Du, Weidong Wang
Summary: We propose an autonomous approaching scheme for mobile robot traversing obstacle stairwells, which overcomes the restricted field of vision caused by obstacles. The scheme includes stair localization, structural parameter estimation, and optimization of the approaching process.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Pedro Azevedo, Vitor Santos
Summary: Accurate detection and tracking of vulnerable road users and traffic objects are vital tasks for autonomous driving and driving assistance systems. This paper proposes a solution for object detection and tracking in an autonomous driving scenario, comparing different object detectors and exploring the deployment on edge devices. The effectiveness of DeepStream technology and different object trackers is assessed using the KITTI tracking dataset.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Benjamin Beiter, Divya Srinivasan, Alexander Leonessa
Summary: Powered exoskeletons can significantly reduce physical workload and have great potential impact on future labor practices. To truly assist users in achieving task goals, a shared autonomy control framework is proposed to separate the control objectives of the human and exoskeleton. Positive Power control is introduced for the human-based controller, while 'acceptance' is used as a measure of matching the exoskeleton's control objective to the human's. Both control objectives are implemented in an optimization-based Whole-Body-Control structure. The results verify the effectiveness of the control framework and its potential for improving cooperative control for powered exoskeletons.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)