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arXiv 2024年

作者： Mellet, Julien Berra, Andrea Seisa, Achilleas Santi Sankaranarayanan, Viswa Gamage, Udayanga G.W.K.N. Soto, Miguel Ángel Trujillo Heredia, Guillermo Nikolakopoulos, George Lippiello, Vincenzo Ruggiero, Fabio PRISMA Lab Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy CATEC Advanced Center for Aerospace Technologies Seville Spain Robotics and AI Team Department of Computer Electrical and Space Engineering LuleåUniversity of Technology Luleå Sweden Automation and Control Group Department of Electrical Engineering and Photonics Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400 g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop kinematic chain, employs two deported servomotors. Each joint is actuated with a single tendon for active motion control in compression of the arm at the end-effector. Its elasto-mechanical design reduces weight and provides flexibility, allowing passive-compliant interactions without impacting the motors’ integrity. Notably, the arm’s damping can be adjusted based on the proposed inner frictional bulges. Experimental applications showcase the aerial system performance in both free-flight and physical interaction. The presented work may open safer applications for Micro Aerial Vehicle (MAV) in real environments subject to perturbations during interaction. © 2024, CC BY.

关键词： End effectors

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Vision-based Terrain Perception of Quadruped Robots in Complex Environments

Vision-based Terrain Perception of Quadruped Robots in Compl...

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IEEE International Conference on robotics and Biomimetics

作者： Kexin Wang Teng Chen Jian Bi Yibin Li Xuewen Rong Center for Robotics School of Control Science and Engineering Shandong University China

ISBN: (纸本)9781665405362

Compared with wheeled robots and tracked robots, legged robots have higher flexibility and environmental adaptability, it can perform tasks better in complex environments. In this article, the SDUQuad-48 quadruped robot, developed by Shandong University, equipped with a depth camera to realize the perception of environmental terrain. We build a terrain height map based on the point cloud data, and use an algorithm based on the potential field to determine a terrain passability plan. Each cell in the terrain passability map is set to a specific state based on the movement of the robot to provide the selection of foothold for the robot’s subsequent motion plan. Synthesize locomotion and depth vision realize the robot’s perception and adaptation to the environmental terrain automatically. In the experimental part of this article, it is shown that our SDUQuad-48 quadruped robot can successfully cross obstacles of different heights vary from 4 to 7 cm with a steady speed of 0.2 m/s.

关键词： Legged locomotion Point cloud compression Conferences Biomimetics Robot vision systems Cameras Quadrupedal robots

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KNTU-RoboGlove: Design of a Pneumatic Soft Robotic Glove

KNTU-RoboGlove: Design of a Pneumatic Soft Robotic Glove

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Tanin Boka M. Navid Nikkhah S. Ali A. Moosavian Dept of Mechanical Engineering K. N. Center of Excellence in Robotics and Control Advanced Robotics & Automated Systems (ARAS) Laboratory Toosi University of Technology Tehran Tehran Iran Dept of Mechanical Engineering K. N. Toosi University of Technology Tehran Tehran Iran

This paper introduces an innovative advancement in the field of wearable assistive technology by presenting a revolutionary portable soft robotic glove designed to assist individuals with hand impairments during rehabilitation exercises and daily tasks. The glove offers both active finger flexion and passive extension to aid in hand support and rehabilitative training. It achieves this by utilizing integrated soft actuators, including a variable stiffness layer and a pneumatic-driven layer, which guide specific bending, twisting, and extending movements when air pressure is applied. Notably, the variable stiffness layer takes inspiration from pangolins, whose scales are pliable during daily activities but become rigid when facing threats. To provide increased power with enhanced stiffness, a toothed pneumatic actuator is specially designed. The three-finger soft glove is manufactured by molding super-elastic material, creating the actuator mold using a 3D printer. The glove’s grasping abilities have been thoroughly tested, and experimental results demonstrate that, when compared to previously developed elastomeric-based actuators, these actuators can generate substantial force and torque to facilitate finger flexion even at lower air pressure levels. This capability allows the glove to grasp a wide range of objects, assisting individuals in their rehabilitative training. Furthermore, a control system for grasping has been developed, which includes motion processing unit sensors for collecting feedback signals aligned with the actuators, along with a closed-loop controller for regulating the pressure. In contrast to existing devices, this glove, thanks to its portable design, has the potential to significantly enhance user autonomy and independence.

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Fault Tolerant Dynamic Task Assignment for UAV-based Search Teams

arXiv

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arXiv 2024年

作者： Nasir, Ali AlDurgam, Mohammad Interdisciplinary Research Center for Intelligent Manufacturing and Robotics KFUPM Dhahran Saudi Arabia Control and Instrumentation Engineering Department KFUPM Dhahran Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics KFUPM Dhahran Saudi Arabia Industrial and Systems Engineering Department KFUPM Dhahran Saudi Arabia

This research offers a novel framework for dynamic task assignment for unmanned aerial vehicles (UAVs) in cooperative search settings. Notably, it incorporates post-fault UAV capabilities into job assignment techniques, assuring operational dependability in the event of sensor and actuator failures. A significant innovation is the utilization of UAV battery charge to assess range relative to search objectives, hence improving job distribution while conserving battery life. This model integrates repair, recharge, and stochastic goal recurrence, hence increasing its real-world applicability. Using stochastic dynamic programming, this method makes it simpler to determine optimal assignment policies offline so they may be implemented rapidly online. This paper emphasizes the holistic aspect of the proposed model, which connects high-level task rules to low-level control capabilities. A simulation-based case study proves its usefulness, highlighting its robustness in fault-prone and battery-variable settings. Overall, this paper proposes and demonstrates a comprehensive method for assigning UAV tasks that integrates defect awareness, battery management, and multilayer control through the use of stochastic dynamic programming. © 2024, CC BY.

关键词： Stochastic systems

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A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations

A Modular Approach to the Embodiment of Hand Motions from Hu...

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IEEE-RAS International Conference on Humanoid Robots

作者： Alexander Fabisch Manuela Uliano Dennis Marschner Melvin Laux Johannes Brust Marco Controzzi Robotics Innovation Center DFKI GmbH Bremen Germany The BioRobotics Institute Scuola Superiore Sant’ Anna Pisa Italy Department of Excellence in Robotics & AI Pisa Italy Robotics Research Group University of Bremen Plan-Based Robot Control DFKI GmbH

ISBN: (纸本)9798350309805

Manipulating objects with robotic hands is a complicated task. Not only the fingers of the hand, but also the pose of the robot's end effector need to be coordinated. Using human demonstrations of movements is an intuitive and data-efficient way of guiding the robot's behavior. We propose a modular framework with an automatic embodiment mapping to transfer recorded human hand motions to robotic systems. In this work, we use motion capture to record human motion. We evaluate our approach on eight challenging tasks, in which a robotic hand needs to grasp and manipulate either deformable or small and fragile objects. We test a subset of trajectories in simulation and on a real robot and the overall success rates of about 70% are aligned.

关键词： Robot kinematics Robot vision systems Humanoid robots Reinforcement learning Grasping Cameras Motion capture

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Distributed formation control of networked mechanical systems

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IFAC-PapersOnLine 2022年第13期55卷 294-299页

作者： N. Javanmardi P. Borja M.J. Yazdanpanah J.M.A. Scherpen 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 The Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen The 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.

关键词： Autonomous systems Cooperative control Networked control systems Large-scale systems Port-Hamiltonian systems Scalability

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Design of Intelligent Backflow Prevention System

Design of Intelligent Backflow Prevention System

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Chinese control Conference (CCC)

作者： Jiale Chen Tao Dong Shiyun Wang Guili Jiao Rongrong Zhang Xincheng Tian Center for Robotics Engineering Research Center of Intelligent Unmanned System Ministry of Education School of Control Science and Engineering Shandong University Jinan China Jinan China Shandong Tianyong Intelligent Technology Co. Ltd Jinan China Shandong Hydrology Center Jinan China

With the advent of the rainy season, the chances of urban flooding are increasing, to avoid the risk of flooding people and underground garages, this paper explains our reliance on video surveillance technology and 5G cloud data technology to collect the data of anti-backflow door equipment deployed in various parts of the city, and remote control and command in the management center, which can quickly give flood control measures and early warning to protect the personnel and property of underground garages, and reduced losses.

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Mask Generation of Inpainting Model for Moire Pattern based 3D Reconstruction

Mask Generation of Inpainting Model for Moire Pattern based ...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Tae-Jung Kim Min-Ho Ha Saba Arshad Tae-Hyoung Park Department of Intelligent Robotics Engineering Chungbuk National University Cheongju-si Republic of Korea Department of Control and Robot Engineering Chungbuk National University Cheongju-si Republic of Korea Industrial Artificial Intelligence Research Center Chungbuk National University Cheongju-si Republic of Korea Department of Intelligent Systems and Robotics Chungbuk National University Cheongju-si Republic of Korea

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we aim to enhance the performance of 3D height reconstruction from 2D printed circuit board (PCB) moiré images by removing and reconstructing the noise caused by light reflection and shadows, which interfere with the reconstruction process. To achieve this, we conducted comparative experiments using various generative adversarial network (GAN) models applied to an inpainting model, discovering that mask images play a crucial role in this context. By applying white mask areas over the noise regions in the 2D PCB moiré images using the inpainting model, and reconstructing only those areas, we significantly improve the performance in reconstructing the heights of components within the PCB by removing and reconstructing only the specified noise regions. For setting the mask areas, we employ the fast unsupervised anomaly detection with generative adversarial networks (f-AnoGAN model) as an outlier detection model using GAN, aiming to identify anomalies between images with and without noise to set the mask areas accordingly. Through this approach, we accurately identify and reconstruct only the noise regions, proposing a method for reconstructing the heights of components within PCB using 2D PCB moiré images.

关键词： Solid modeling Three-dimensional displays Printed circuits Noise Generative adversarial networks Reflection Integrated circuit modeling Image reconstruction Anomaly detection Context modeling

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HNSRRT*:A Path Planning Algorithm Based On Heuristic Non-Uniform Sampling Method In Complex Obstacle Environment

HNSRRT*:A Path Planning Algorithm Based On Heuristic Non-Uni...

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Chinese control Conference (CCC)

作者： Zhiwen Xu Hui Zhang Bo Chen Xidong Zhou Songtao Yin Lian Yang College of Electrical and Information Engineering Changsha University of Science and Technology Hunan China College of Robotics and Robot Visual Perception and Control Technology National Engineering Research Center Hunan University Hunan China

The traditional sampling-based algorithm such as Rapidly Random-exploring Tree (RRT) and various varieties have achieved tremendous success in the area of path planning. However, their excessive exploration in the state space leads to long time to find the optimal solution, large memory usage and cannot guarantee the quality of the planned path(generally evaluated by the cost of search time and the length of path) in sophisticated space. In this article, we propose an optimal path planning algorithm based on heuristic non-uniform sampling, namely the HNSRRT*, which successfully plans path in complex obstacle environments with optimal length and minimum time cost. The HNSRRT* utilizes heuristic function to generate non-uniform sampling distribution by Gaussian distribution,and constraints on sampling points can reduce the time wasted and path length increase caused by excessive exploration. We test the proposed HNSRRT* in 2D and 3D complex obstacle environment,comparing it with the three traditional sampling-base algorithms. The simulation results indicated that the effectiveness and efficiency of HNSRRT* and have an obvious improvement in term of time cost, path length compared with the existing algorithms.

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5520-5537页

作者： Jian Liu Wei Sun Hui Yang Pengchao Deng Chongpei Liu Nicu Sebe Hossein Rahmani Ajmal Mian National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics and the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing and Communications Lancaster University Lancaster U.K. Department of Computer Science The University of Western Australia Crawley WA Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance.

关键词： Shape Solid modeling Three-dimensional displays Pose estimation Training Robots Grasping Diffusion models Real-time systems Point cloud compression

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