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Global-state-Free Obstacle Avoidance for Quadrotor control in Air-Ground Cooperation

IEEE robotics and Automation Letters 2025年第7期10卷 6688-6695页

作者： Zhang, Baozhe Chen, Xinwei Chen, Qingcheng Xu, Chao Gao, Fei Cao, Yanjun Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber- Systems and Control Hangzhou310027 China Zhejiang University Huzhou Institute Huzhou313000 China Shanghai Institute of Special Equipment Inspection & Technical Research Shanghai200062 China

CoNi-MPC [1] provides an efficient framework for UAV control in air-ground cooperative tasks by relying exclusively on relative states, eliminating the need for global state estimation. However, its lack of environmental information poses significant challenges for obstacle avoidance. To address this issue, we propose a novel obstacle avoidance algorithm, Cooperative Non-inertial frame-based Obstacle Avoidance (CoNi-OA), designed explicitly for UAV-UGV cooperative scenarios without reliance on global state estimation or obstacle prediction. CoNi-OA uniquely utilizes a single frame of raw LiDAR data from the UAV to generate a modulation matrix, which directly adjusts the quadrotor's velocity to achieve obstacle avoidance. This modulation-based method enables real-time generation of collision-free trajectories within the UGV's non-inertial frame, significantly reducing computational demands (less than 5 ms per iteration) while maintaining safety in dynamic and unpredictable environments. The key contributions of this work include: (1) a modulation-based obstacle avoidance algorithm specifically tailored for UAV-UGV cooperation in non-inertial frames without global states;(2) rapid, real-time trajectory generation based solely on single-frame LiDAR data, removing the need for obstacle modeling or prediction;and (3) adaptability to both static and dynamic environments, thus extending applicability to featureless or unknown scenarios. © 2016 IEEE.

关键词： Quadrotors Collision avoidance Dynamics Autonomous aerial vehicles Trajectory Laser radar Air to ground communication Real-time systems Modulation Dynamical systems

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Robust Finite-Time Containment of networked Heterogeneous Nonlinear systems with Intermittent Measurement Only

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IEEE Transactions on Network Science and Engineering 2025年

作者： Tian, Biao Zhang, Hao Cui, Peiyu Wang, Zhuping Yan, Huaicheng Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai201804 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems China East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China

The robust finite-time containment problem of fully heterogeneous multiagent systems with uncertainties is often challenging, especially when only intermittent output measurement is used. To address the issue posed by the nonidentical system dynamics of multiple leaders, which makes the traditional distributed estimator form for the convex hull inapplicable, a distributed finite-time estimator is constructed for each follower to extract the system matrices and states of leaders. Then, in the output-triggering setting, a finite-time extended state observer driven by intermittent output measurement is developed to reconstruct the uncertainties and unmeasurable states of followers. Meanwhile, the non-continuous output measurement will lead to the differentiation of virtual control laws undefined. To solve this issue, a filtering compensation scheme-based finitetime controller via the backstepping technique is developed for nonlinear followers, ensuring practical finite-time stability (PFTS) of the closed-loop system. It is shown that the proposed algorithm steers each follower into the preset convex combination spanned by the positions of multiple leaders. The capability of the exploited control protocol is verified through simulations and experiments. 2013 IEEE.

关键词： Backstepping

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Design, Dynamic Modeling and control of a 2-DOF Robotic Wrist Actuated by Twisted and Coiled Actuators

arXiv

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arXiv 2025年

作者： Zhang, Yunsong Zhou, Xinyu Zhang, Feitian Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States

Robotic wrists play a pivotal role in the functionality of industrial manipulators and humanoid robots, facilitating manipulation and grasping tasks. In recent years, there has been a growing interest in integrating artificial muscle-driven actuators for robotic wrists, driven by advancements in technology offering high energy density, lightweight construction, and compact designs. However, in the study of robotic wrists driven by artificial muscles, dynamic model-based controllers are often overlooked, despite their critical importance for motion analysis and dynamic control of robots. This paper presents a novel design of a two-degree-of-freedom (2-DOF) robotic wrist driven by twisted and coiled actuators (TCA) utilizing a parallel mechanism with a 3RRRR configuration. The proposed robotic wrist is expected to feature lightweight structures and superior motion performance while mitigating friction issues. The Lagrangian dynamic model of the wrist is established, along with a nonlinear model predictive controller (NMPC) designed for trajectory tracking tasks. A prototype of the robotic wrist is developed, and extensive experiments are conducted to validate its superior motion performance and the proposed dynamic model. Subsequently, extensive comparative experiments between NMPC and PID controller were conducted under various operating conditions. The experimental results demonstrate the effectiveness and robustness of the dynamic model-based controller in the motion control of TCA-driven robotic wrists. Copyright © 2025, The Authors. All rights reserved.

关键词： Industrial manipulators

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Machine Learning for Estimation and control of Quantum systems

arXiv

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arXiv 2025年

作者： Ma, Hailan Qi, Bo Petersen, Ian R. Wu, Re-Bing Rabitz, Herschel Dong, Daoyi the School of Engineering Australian National University CanberraACT2601 Australia the Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China the Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China the Department of Chemistry Princeton University PrincetonNJ08544 United States

The development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation, and sensing. This poses severe challenges in efficient control, calibration, and validation of quantum states and their dynamics. Machine learning methods have emerged as powerful tools owing to their remarkable capability to learn from data, and thus have been extensively utilized for different quantum tasks. This paper reviews several significant topics related to machine learning-aided quantum estimation and control. In particular, we discuss neural networks-based learning for quantum state estimation, gradient-based learning for optimal control of quantum systems, evolutionary computation for learning control of quantum systems, machine learning for quantum robust control, and reinforcement learning for quantum control. This review provides a brief background of key concepts recurring across many of these approaches with special emphasis on neural networks, evolutionary computation, and reinforcement learning. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Torque Coordinated control of Six-Wheeled Planetary Rovers Based on Wheel-Terrain Interaction

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Yang, Huaiguang Yang, Lintao Ding, Liang Yang, Chaojie Xu, Chongfu Gao, Haibo Yuan, Ye Deng, Zongquan Harbin Institute of Technology Harbin150001 China Huawei Technologies Co.Ltd. Shanghai200000 China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China Student Affairs Office Harbin Institute of Technology Harbin150001 China

Global interest in lunar exploration programs has heightened focus on the lunar South Pole region. With its complex terrain and irregular illumination, this region poses challenges for the mobility and energy management of planetary rovers. Consequently, to ensure adequate passing ability while minimizing energy consumption, research on the coordinated control of multi-driven wheels is essential. In this study, a torque-coordinated control approach based on wheel-terrain interaction for a six-wheeled planetary rover is proposed. First, the continuous stress distribution model of the wheel-terrain interaction is simplified into an equivalent concentrated force model. Subsequently, precise and robust tracking of the desired velocity was achieved using a sliding-mode controller to overcome the challenges posed by model simplification and rough terrain. Subsequently, based on the expected torque obtained from the sliding-mode controller, with the optimization objective of minimizing energy consumption, the forces on the wheels were analyzed using the equivalent concentrated force model, and the torque distribution was coherently controlled. Finally, the effectiveness of the control method and the accuracy of the model are verified through whole-vehicle experiments, and the energy efficiency optimization ratio varies from 9.93% to 5.16% within the resistance range of 0-80N. © 2024 IEEE.

关键词： Rovers

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Scalable Fabrication of Uniform Fast-Response Humidity Field Sensing Array for Respiration Recognition and Contactless Human-Machine Interaction

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Advanced Functional Materials 2025年

作者： Zhang, Zhicheng Li, Jianye Chen, Huizhi Wang, Hao Luo, Yibing Si, Ranzi Xie, Ruijie Tao, Kai Yang, Bo-Ru Zhang, Daohui Liu, Fei Huo, Fengwei Wu, Jin State Key Laboratory of Optoelectronic Materials and Technologies and the Guangdong Province Key Laboratory of Display Material and Technology School of Electronics and Information Technology Sun Yat-sen University Guangzhou510275 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Xiamen University Xiang'an Campus Xiang'an South Road Fujian Xiamen361102 China Dongguan Key Laboratory of Advanced Drug Delivery and Biosensing Research and Development School of Pharmacy and Dongguan Innovation Institute Guangdong Medical University Dongguan523808 China Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace School of Mechanical Engineering Northwestern Polytechnical University Xi'an710072 China State Key Laboratory of Transducer Technology Shanghai200050 China

In the wake of the COVID-19 pandemic, there is an increased demand for humidity sensors that can accurately detect targets without direct contact, driving advancements in contactless human-machine interaction (HMI) and non-invasive medical diagnostics. However, it is difficult for traditional individual sensors to accurately acquire humidity field information for reliable HMI. Here, an ionogel film-based flexible and fast-response humidity field sensing array is developed via a scalable, efficient, and modified spin-coating-based fabrication strategy for accurate detection of humidity distribution. By optimizing the structure and constitution of the hydrophobic ionogel films, the sensors display ultrafast response and recovery time (0.65/0.85 s), a broad detection range (11-98% RH), long-term stability (120 days), excellent repeatability, flexibility, and environmental tolerances. Thanks to the one-time scalable fabrication, the sensing units in the array exhibit superior uniformity, as the device-to-device deviation is reduced to one-ninth compared to traditional multi-batch fabrication methods. With the aid of machine learning algorithms, this humidity sensing array realizes not only the accurate identification of various subtle breath abnormalities (e.g., oral breathing, apnea, left and right nasal congestions) by conformally attaching to wearable masks, but also the precise contactless HMI applications (e.g., anti-interference gesture recognition and wireless control of intelligent executive terminals). © 2025 Wiley-VCH GmbH.

关键词： Humidity sensors

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Representation of Human Arm Dynamic Intents with An Electrical-Impedance-Tomography (EIT)-Driven Musculoskeletal Model for Human-Robot Interaction

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IEEE Transactions on robotics 2025年 41卷 3278-3296页

作者： Zheng, Enhao Liu, Xiaodong Xu, Chenfeng Zhou, Zhihao Wang, Qining State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute for Artificial Intelligence Peking University Beijing100871 China Faculty of Engineering School of Advanced Manufacturing and Robotics Peking University The Institute for Artificial Intelligence Beijing100871 China

Representing human arm dynamic intent is essential for efective human-robot interaction. Accurately and robustly decoding these intentions through mathematical modeling of neuromuscular processes poses signifcant challenges. This study introduces an EIT-driven musculoskeletal model which integrates an EIT sensing system with methods for muscle identifcation, parameter estimation, and musculoskeletal system modeling. Unlike existing muscle-signal techniques, EIT captures muscle activities from the anatomical cross-sectional plane, providing both activation dynamics and morphological features. We validated our method through multi-DoF wrist kinematics estimation under varying contraction intensities, arm endpoint stifness estimation, and robotic variable admittance control. Our approach achieves accuracy comparable to state-of-the-art methods while requiring fewer training samples and a more compact sensing system. The model incorporates physiological constraints, minimizing decoding errors and ensuring interaction safety. This method enables reliable intent decoding with practical training demands. Future work will enhance the EIT system for complex tasks. © 2025 IEEE. All rights reserved.

关键词： Muscle

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Active knee joint exoskeleton for stair ascent augmentation

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Science China(Information Sciences) 2021年第3期64卷 243-245页

作者： Zongwei ZHANG Jizhuang FAN Hongzhe JIN Tianjiao ZHENG Sikai ZHAO Shun MA Jie ZHAO Yanhe ZHU State Key Laboratory of Robotics and Systems

Dear editor,Stair climbing is a strenuous daily movement that is challenging for older adults and those with lower limb *** this study, we describe a novel stairclimbing exoskeleton that was designed to help weak-knee... 详细信息

关键词： exoskeleton stair climbing hybrid control zero-force tracking switching controller

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