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arXiv 2023年

作者： Hou, Chang Deng, Nan Noack, Bernd R. School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518055 China

In this study, we propose a novel data-driven reduced-order model for complex dynamics, including nonlinear, multi-attractor, multi-frequency, and multiscale behaviours. The starting point is a fully automatable cluster-based network model (CNM) (Li et al. J. Fluid Mech. vol. 906, 2021, A21) which kinematically coarse-grains the state with clusters and dynamically predicts the transitions in a network model. In the proposed dynamics-augmented CNM (dCNM), the prediction error is reduced with trajectory-based clustering using the same number of centroids. The dCNM is first exemplified for the Lorenz system and then demonstrated for the three-dimensional sphere wake featuring periodic, quasi-periodic and chaotic flow regimes. For both plants, the dCNM significantly outperforms the CNM in resolving the multi-frequency and multiscale dynamics. This increased prediction accuracy is obtained by stratification of the state space aligned with the direction of the trajectories. Thus, the dCNM has numerous potential applications to a large spectrum of shear flows, even for complex dynamics. © 2023, CC BY.

关键词： Wakes

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Design of a Mesh Crawling Robot with Beetle-Inspired Variable Stiffness Tarsus

Design of a Mesh Crawling Robot with Beetle-Inspired Variabl...

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IEEE International Conference on robotics and Biomimetics

作者： Hao Chen Kai Hu Longlong Hu Ziqi Zhou Yao Li Bing Li Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Compared with large robots, micro-robots have the advantages of small size, low power consumption, and high concealment. For complex terrain, the crawling performance of micro-robots will be significantly reduced. In this study, the mesh surface was used as the crawling base of the robot, and a variable stiffness tarsus was designed with the beetle as a reference. This tarsus composed of multiple bending parts achieves a large range of stiffness changes, 0.07~0.495 N/mm. We have achieved stiffness control of the tarsus in a single actuation by trajectory planning of the claw ends. The proposed robot can achieve a big stiffness of the tarsus to resist the deformation of the tarsus due to external forces, which means that the robot can increase the adhesion ability when crawling at the stance phase. Therefore, even when the load was increased from 10g to 40g, the average crawling speed of the robot was not significantly reduced. This new tarsus can improve the crawling performance of the micro-robot on the mesh.

关键词： Legged locomotion Power demand Trajectory planning Force Kinematics Resists Bending Robots Substrates Trajectory optimization

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Distribution Steering for Discrete-Time Uncertain Ensemble Systems

arXiv

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arXiv 2024年

作者： Wu, Guangyu Tsiotras, Panagiotis Lindquist, Anders The Department of Automation Shanghai Jiao Tong University Shanghai China The School of Aerospace Engineering The Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta United States School of Artificial Intelligence Anhui University Hefei China The Smart Sensor Fusion Research Laboratory Shanghai China

Ensemble systems appear frequently in many engineering applications and, as a result, they have become an important research topic in control theory. These systems are best characterized by the evolution of their underlying state distribution. Despite the work to date, few results exist dealing with the problem of directly modifying (i.e., "steering") the distribution of an ensemble system. In addition, in most of the existing results, the distribution of the states of an ensemble of discrete-time systems is assumed to be Gaussian. However, in case the system parameters are uncertain, it is not always realistic to assume that the distribution of the system follows a Gaussian distribution, thus complicating the solution of the overall problem. In this paper, we address the general distribution steering problem for first-order discrete-time ensemble systems, where the distributions of the system parameters and the states are arbitrary with finite first few moments. Both linear and nonlinear system dynamics are considered using the method of power moments to transform the original infinite-dimensional problem into a finite-dimensional one. We also propose a control law for the ensuing moment system, which allows us to obtain the power moments of the desired control inputs. Finally, we solve the inverse problem to obtain the feasible control inputs from their corresponding power moments. We provide numerical results to validate our theoretical developments. These include cases where the parameter distribution is uniform, Gaussian, non-Gaussian, and multi-modal, respectively. © 2024, CC BY-NC-ND.

关键词： Method of moments

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CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face Anti-Spoofing

CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face A...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Ying Zhang Xiangyu Zhu Ajian Liu Xun Lin Jun Wan Jing Zhang Zhen Lei State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Computer Science and Engineering Beihang University School of Computer Science and Engineering Macau University of Science and Technology Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Face anti-spoofing (FAS) is pivotal in safeguarding the integrity of face recognition systems. Flexible-modal FAS utilizes multi-modal data and trains a unified model adaptable to any single-modal testing scenario. This innovation addresses the shortcomings of conventional multi-modal FAS approaches, which typically demand separate model training and deployment for each modality. However, existing flexible-modal FAS approaches activate specific network branches based on the modality of the tested sample. This not only increases the model’s parameters but also necessitates the provision of the image’s modality for testing, thereby constraining deployment flexibility. To address the issue, we present Compound Prompt Learning CLIP (CPL-CLIP), a novel method for flexible-modal FAS. This approach capitalizes on a learned textual prompt that is nearly independent of modality, thus bolstering class-based classification across arbitrary modalities. Specifically, our CPL-CLIP introduces a Dual-Branch Prompt (DBP), consisting of class and modal prompts that describe and guide classification, where each prompt is composed of learnable vectors and fixed templates. To further render the class prompt as modality-agnostic as possible, a Cosine Similarity Loss (CSL) is proposed to facilitate the maximal separation of the class prompt from the modality prompt. With only the class prompt utilized during testing, CPL-CLIP enables deployment in diverse modal testing scenarios without the necessity of the test image’s modality to be known. Extensive experiments demonstrate CPL-CLIP’s superiority over existing methods on several flexible-modal FAS benchmarks.

关键词： Training Adaptation models Technological innovation Accuracy Face recognition Benchmark testing Vectors Robustness Data models Compounds

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Non-cooperative Stochastic Target Encirclement by Anti-synchronization Control via Range-only Measurement

Non-cooperative Stochastic Target Encirclement by Anti-synch...

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IEEE International Conference on robotics and automation (ICRA)

作者： Fen Liu Shenghai Yuan Wei Meng Rong Su Lihua Xie Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control School of Automation Guangdong University of Technology Guangzhou the Centre for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper investigates the stochastic moving target encirclement problem in a realistic setting. In contrast to typical assumptions in related works, the target in our work is non-cooperative and capable of escaping the circle containment by boosting its speed to maximum for a short duration. In extreme conditions, where GPS signals are not available, weight restrictions are present, and ground guidance is absent, the agents can rely solely on their onboard single-modality perception tools to measure the distances to the target. The distance measurement allows for creating a position estimator by providing a target position-dependent variable. Furthermore, the construction of the unique distributed anti-synchronization controller (DASC) can guarantee that the two agents track and encircle the target swiftly. The convergence of the estimator and controller is rigorously evaluated using the Lyapunov technique. A real-world UAV-based experiment is conducted to illustrate the performance of the proposed methodology in addition to a simulated Matlab numerical sample. Our video demonstration can be found in the URL https://***/EDVLvP-bk8M.

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RH-Map: Online Map Construction Framework of Dynamic Objects Removal Based on Region-wise Hashmap Structure

arXiv

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arXiv 2023年

作者： Yan, Zihong Wu, Xiaoyi Jian, Zhuozhu Xueqian Wang, Bin Lan Liang, Bin The Center for Artificial Intelligence and Robotics Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Jianghuai Advance Technology Center 230022 China

Mobile robots operating in outdoor environments frequently encounter the issue of undesired traces left by dynamic objects and manifested as obstacles on the map, impeding the robot’s ability to achieve accurate localization and navigation performance. To address this problem, we present a novel online map construction framework called RH-Map. Our framework leverages a newly proposed 3D region-wise hashmap data structure for efficiently removing dynamic objects in real-time. It comprises a real-time dynamic object removal front-end module S2M-R and a lightweight back-end module for further removal. We conducted extensive experiments on the SemanticKITTI dataset, and the results demonstrate that our proposed method performs favorably compared to state-of-the-art approaches, and we further validated the proposed framework in real-world environment. The source code is released and available for the community1 Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile robots

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Adaptive Neural Network Tracking for a Magnetic Microrobot in Presence of Model Uncertainty and Unknown Disturbance

Adaptive Neural Network Tracking for a Magnetic Microrobot i...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Haoyu Wang Yueyue Liu Qigao Fan Xiaoyu Wu Yingbai Hu School of Internet of Things Engineering Institute of Automation Jiangnan University Wuxi China Department of Biomedical Engineering National University of Singapore Singapore Multi-Scale Medical Robotics Centre The Chinese University of Hong Kong Hong Kong China

Magnetic microrobots have potential applications in many medical diagnosis and treatment applications due to the unique characteristics in biological fine manipulation, such as precise drug delivery and being able to be used in complex biological environments. Precise and robust trajectory tracking control of magnetic microrobots under complex disturbances and uncertain dynamic model is still one of the most important and challenging steps in achieving ideal applications. In this paper, an adaptive neural network control algorithm is proposed to address the uncertain dynamic parameters and complex external disturbances. The developed state feedback control strategy based on a radial basis function neural network (RBFNN) can effectively estimate the uncertainties online from the state and desired trajectories, rather than the model-based control. The controller require as little knowledge of system parameters as possible. Subsequently, the closed-loop stable system is guaranteed based on Lyapunov theory. Finally, the two experimental researches are conducted on our developed electromagnetic manipulation system to validate the effectiveness of the proposed controller.

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Risk-Aware Non-Myopic Motion Planner for Large-Scale Robotic Swarm Using CVaR Constraints

Risk-Aware Non-Myopic Motion Planner for Large-Scale Robotic...

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

作者： Xuru Yang Yunze Hu Han Gao Kang Ding Zhaoyang Li Pingping Zhu Ying Sun Chang Liu Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China Department of Automation Tsinghua University Beijing China Department of Computer Sciences and Electrical Engineering (CSEE) Marshall University Huntington WV USA School of Electrical Engineering and Computer Science The Pennsylvania State University State College PA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Swarm robotics has garnered significant attention due to its ability to accomplish elaborate and synchronized tasks. Existing methodologies for motion planning of swarm robotic systems mainly encounter difficulties in scalability and safety guarantee. To address these limitations, we propose a Risk-aware swarm mOtion planner using conditional ValuE-at-Risk (ROVER) that systematically navigates large-scale swarms through cluttered environments while ensuring safety. ROVER formulates a finite-time model predictive control (FTMPC) problem predicated upon the macroscopic state of the robot swarm represented by a Gaussian Mixture Model (GMM) and integrates conditional value-at-risk (CVaR) to ensure collision avoidance. The key component of ROVER is imposing a CVaR constraint on the distribution of the Signed Distance Function between the swarm GMM and obstacles in the FTMPC to enforce collision avoidance. Utilizing the analytical expression of CVaR of a GMM derived in this work, we develop a computationally efficient solution to solve the non-linear constrained FTMPC through sequential linear programming. Simulations and comparisons with representative benchmark approaches demonstrate the effectiveness of ROVER in flexibility, scalability, and safety guarantee.

关键词： Navigation Scalability Swarm robotics Benchmark testing Safety Planning Trajectory Synchronization Collision avoidance Robots

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Riemannian Online Optimistic Algorithms with Dynamic Regret

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IEEE Transactions on Automatic Control 2025年

作者： Wang, Xi Yuan, Deming Hong, Yiguang Hu, Zihao Wang, Lei Shi, Guodong University of New South Wales School of Electrical Engineering & Telecommunications SydneyNSW2052 Australia Nanjing University of Science and Technology School of Automation Nanjing210094 China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Hong Kong University of Science and Technology Department of Mathematics Hong Kong Zhejiang University College of Control Science and Engineering Zhejiang 310058 China The University of Sydney Australian Center for Robotics School of Aerospace Mechanical and Mechatronic Engineering NSW2006 Australia

In this paper, we consider Riemannian online convex optimization with dynamic regret, which involves minimizing the cumulative loss difference between a learner's decisions and a sequence of adaptive decisions across dynamic environments over time on Riemannian manifolds. First, we propose Riemannian Online Optimistic Gradient Descent (R-OOGD), which extends the classical optimistic algorithms to Riemannian manifolds, requiring one gradient inquiry per round. We then propose Riemannian Adaptive Online Optimistic Gradient Descent (R-AOOGD) by incorporating the meta-expert framework into R-OOGD. We analyze the dynamic regret of R-OOGD and R-AOOGD in terms of the regularity of the sequence of cost functions and comparators. The dynamic regret bound matches the results in Euclidean space and with a scale adjustment of O(1+κ D2/√1-KD2) related to the sectional curvature lower bound κ, sectional curvature upper bound and feasible set diameter. Finally, we provide numerical experiments to illustrate the performance of the proposed algorithms. © 1963-2012 IEEE.

关键词： Optimization algorithms

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Whole-Body Control for Velocity-Controlled Mobile Collaborative Robots Using Coupling Dynamic Movement Primitives

Whole-Body Control for Velocity-Controlled Mobile Collaborat...

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IEEE-RAS International Conference on Humanoid Robots

作者： Zhangjie Tu Tianwei Zhang Lei Yan Tin lun Lam Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) Shenzhen School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen School of Science and Engineering The Chinese University of Hong Kong Shenzhen

ISBN: (纸本)9798350309805

In this paper, we propose a unified whole-body control framework for velocity-controlled mobile collaborative robots which can distribute task motion into the arm and mobile base according to specific task requirements by adjusting weighting factors. Our framework focuses on addressing two challenging issues in whole-body coordination: 1) different dynamic characteristics of the mobile base and the arm; 2) avoidance of violating both safety and configuration con-straints. In addition, our controller involves Coupling Dynamic Movement Primitive to enable the essential capabilities for collaboration and interaction applications, such as obstacle avoidance, human teaching, and compliance control. Based on these, we design an adaptive motion mode for intuitive physical human-robot interaction through adjusting the weighting factors. The proposed controller is in closed-form and thus quite computationally efficient. Several typical experiments carried out on a real mobile collaborative robot validate the effectiveness of the proposed controller.

关键词： Couplings Robot kinematics Dynamics Collaboration Humanoid robots Human-robot interaction Manipulators

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