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Stability Analysis and Performance Evaluation of Delayed Bilateral Telerobotic Systems over a Lossy Communication Channel

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Journal of Systems Science & Complexity 2021年第1期34卷 157-179页

作者： BAKHSHI Ali ALFI Alireza TALEBI Heidar Ali SOURATGAR Amir Aboulfazl YOUSEFI Mahdi Faculty of Electrical and Robotic Engineering Shahrood University of TechnologyShahrood 36199-95161Iran Power Plants Control Systems Technology Development Plan Department of monitoring and ControlNiroo Research InstituteTehran 14665-517Iran Real-Time and Robotics system Laboratory Faculty of Electrical EngineeringAmirkabir University of TechnologyHafez AvenueTehran 15914Iran Electrical and Computer Engineering Department The University of British Columbia2385East MallVan couverBC V6T 1Z4Canada

The focus of this paper is to address a novel control technique for stability and transparency analysis of bilateral telerobotic systems in the presence of data loss and time delay in the communication channel. Different control strategies have been reported to compensate the effects of time delay in the communication channel;however, most of them result in poor performance under data loss. First, a model for data loss is proposed using a finite series representation of a set of periodic continuous *** improve the performance and data reconstruction, a holder circuits is also introduced. The passivity of the overall system is provided via the wave variable technique based on the proposed model for the data loss. The stability analysis of the system is then derived using the Lyapunov theorem under the time delay and the data loss. Finally, experimental results are given to illustrate the capability of the proposed control technique.

关键词： Lossy channel passivity stability telerobotic transparency

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CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

arXiv

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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AXI-REALM: A Lightweight and Modular Interconnect Extension for Traffic Regulation and Monitoring of Heterogeneous Real-Time SoCs

AXI-REALM: A Lightweight and Modular Interconnect Extension ...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Thomas Benz Alessandro Ottaviano Robert Balas Angelo Garofalo Francesco Restuccia Alessandro Biondi Luca Benini Integrated Systems Laboratory ETH Zurich Switzerland Department of Electrical Electronic and Information Engineering University of Bologna Italy Department of Computer Science and Engineering UC San Diego San Diego CA USA Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa Italy

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The increasing demand for heterogeneous functionality in the automotive industry and the evolution of chip manufac-turing processes have led to the transition from federated to integrated critical real-time embedded systems (CRTESs). This leads to higher integration challenges of conventional timing predictability techniques due to access contention on shared resources, which can be resolved by providing system-level observability and controllability in hardware. We focus on the interconnect as a shared resource and propose AXI-REALM, a lightweight, modular, and technology - independent real-time extension to industry-standard AXI4 interconnects, available open-source. AXI-REALM uses a credit-based mechanism to distribute and control the bandwidth in a multi-subordinate system on periodic time windows, proactively prevents denial of service from malicious actors in the system, and tracks each manager's access and interference statistics for optimal budget and period selection. We provide detailed performance and implementation cost assessment in a 12nm node and an end-to-end functional case study implementing AXI-REALM into an open-source Linux-capable RISC-V SoC. In a system with a general-purpose core and a hardware accelerator's DMA engine causing interference on the interconnect, AXI-REALM achieves fair bandwidth distribution among managers, allowing the core to recover 68.2 % of its performance compared to the case without contention. Moreover, near-ideal performance (above 95 %) can be achieved by distributing the available bandwidth in favor of the core, improving the worst-case memory access latency from 264 to below eight cycles. Our approach minimizes buffering compared to other solutions and introduces only 2.45 % area overhead compared to the original SoC.

关键词： Bandwidth Interference Real-time systems Hardware Regulation Timing System-on-chip

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Feature Correlation Aggregation: on the Path to Better Graph Neural Networks

Feature Correlation Aggregation: on the Path to Better Graph...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Jieming Zhou Tong Zhang Pengfei Fang Lars Petersson Mehrtash Harandi College of Engineering and Computer Science Australian National University Canberra Australia Image and Visual Representation Laboratory EPFL Lausanne Switzerland Imaging and Computer Vision Group CSIRO Canberra Australia Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia

Prior to the introduction of Graph Neural Networks (GNNs), modelling and analyzing irregular data, particularly graphs, was thought to be the Achilles’ heel of deep learning. The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, and many GNNs’ designs have demonstrated its success. However, most only focus on using the first-order information between a node and its neighbors. In this paper, we introduce a central node permutation variant function through a frustratingly simple and innocent-looking modification to the core operation of a GNN, namely the Feature cOrrelation aGgregation (FOG) module, which learns the second-order information from feature correlation between a node and its neighbors in the pipeline. By adding FOG into existing variants of GNNs, we empirically verify this second-order information complements the features generated by original GNNs across a broad set of benchmarks. A tangible boost in the model’s performance is observed where the model surpasses previous state-of-the-art results by a significant margin while employing fewer parameters. e.g., 26.202% improvement on a real-world molecular dataset using graph convolutional networks.

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Robotic Learning of Haptic Skills from Expert Demonstration for Contact-Rich Manufacturing Tasks

Robotic Learning of Haptic Skills from Expert Demonstration ...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Sara Hamdan Yusuf Aydin Erhan Oztop Cagatay Basdogan Robotics and Mechatronics Laboratory and the KUIS-AI Center Koc University Istanbul Turkey Department of Electrical and Electronics Engineering MEF University Istanbul Turkey Department of Computer Science Ozyegin University Istanbul Turkey SISReC/OTRI Osaka University Japan

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

We propose a learning from demonstration (LfD) approach that utilizes an interaction (admittance) controller and two force sensors for the robot to learn the force applied by an expert from demonstrations in contact-rich tasks such as robotic polishing. Our goal is to equip the robot with the haptic expertise of an expert by using a machine learning (ML) approach while providing the flexibility for the user to intervene in the task at any point when necessary by using an interaction controller. The utilization of two force sensors, a pivotal concept in this study, allows us to gather environmental data crucial for effectively training our system to accommodate workpieces with diverse material and surface properties and maintain the contact of polisher with their surfaces. In the demonstration phase of our approach where an expert guiding the robot to perform a polishing task, we record the force applied by the human (F h ) and the interaction force (F int ) via two separate force sensors for the polishing trajectory followed by the expert to extract information about the environment (F env =F h −F int ). An admittance controller, which takes the interaction force as the input is used to output a reference velocity to be tracked by the internal motion controller (PID) of the robot to regulate the interactions between the polisher and the surface of a workpiece. A multilayer perceptron (MLP) model was trained to learn the human force profile based on the inputs of Cartesian position and velocity of the polisher, environmental force (F env ), and friction coefficient between the polisher and the surface to the model. During the deployment phase, in which the robot executes the task autonomously, the human force estimated by our system $\left({{{\hat F}_h}}\right)$ is utilized to balance the reaction forces coming from the environment and calculate the force $\left({{{\hat F}_h} - {F_{env}}}\right)$ needs to be inputted to the admittance controller to gene

关键词： Surface cleaning Force Robot sensing systems Real-time systems Haptic interfaces Trajectory Manufacturing Force sensors Admittance Robots

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Safe Navigation and Obstacle Avoidance Using Differentiable Optimization Based Control Barrier Functions

arXiv

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arXiv 2023年

作者： Dai, Bolun Khorrambakht, Rooholla Krishnamurthy, Prashanth Gonçalves, Vinícius Tzes, Anthony Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States Electrical Engineering New York University Abu Dhabi Abu Dhabi129188 United Arab Emirates

Control barrier functions (CBFs) have been widely applied to safety-critical robotic applications. However, the construction of control barrier functions for robotic systems remains a challenging task. Recently, collision detection using differentiable optimization has provided a way to compute the minimum uniform scaling factor that results in an intersection between two convex shapes and to also compute the Jacobian of the scaling factor. In this paper, we propose a framework that uses this scaling factor, with an offset, to systematically define a CBF for obstacle avoidance tasks. We provide theoretical analyses of the continuity and continuous differentiability of the proposed CBF. We empirically evaluate the proposed CBF's behavior and show that the resulting optimal control problem is computationally efficient, which makes it applicable for realtime robotic control. We validate our approach, first using a 2D mobile robot example, then on the Franka-Emika Research 3 (FR3) robot manipulator both in simulation and experiment. Index Terms-Robot safety, collision avoidance. © 2023, CC BY.

关键词： Robot applications

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A Multi-Layer Software Architecture for Aerial Cognitive Multi-Robot Systems in Power Line Inspection Tasks

arXiv

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arXiv 2023年

作者： Silano, Giuseppe Bednar, Jan Nascimento, Tiago Capitan, Jesus Saska, Martin Ollero, Anibal The Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Department of Computer Systems Universidade Federal da Paraiba Brazil The GRVC Robotics Laboratory University of Seville Spain

This paper presents a multi-layer software architecture to perform cooperative missions with a fleet of quad-rotors providing support in electrical power line inspection operations. The proposed software framework guarantees the compliance with safety requirements between drones and human workers while ensuring that the mission is carried out successfully. Besides, cognitive capabilities are integrated in the multi-vehicle system in order to reply to unforeseen events and external disturbances. The feasibility and effectiveness of the proposed architecture are demonstrated by means of realistic simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Software architecture

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Hardware-Agnostic Behavior Trees:Towards Generic Robot Behavior Design

SSRN

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SSRN 2022年

作者： El-Ariss, Wafic Daher, Naseem Elhajj, Imad H. Logistics Robotics BMW Group Petuelring 130 Munich80809 Germany Vision and Robotics Lab Department of Mechanical Engineering American University of Beirut Riad El-Solh Beirut1107 Lebanon Vision and Robotics Lab Department of Electrical and Computer Engineering American University of Beirut Riad El-Solh Beirut1107 Lebanon

Decision-Making (DM) algorithms are an essential component of autonomous agents that control the behavior of Non-Player Characters (NPCs) in computer games, as well as the behavior of autonomous robots. Behavior Trees (BTs) have gained significant traction in recent years for having facilitated the development of complex behaviors for autonomous robots and NPCs, by offering a simple, yet powerful, task-switching architecture that relies on composing modular and reusable unit behaviors to achieve the overall desired *** is well understood how to design BT leaves, the unit behavior of BTs, for them to be reused across the behavior of a single robot system, however, there is a need for these leaves to generalize across systems of differing hardware, thus achieving hardware abstraction, and producing what we call Hardware-Agnostic Behavior Tree Leaves that would reduce the development time and costs of new *** this paper, we address this goal by first presenting a new formulation for BTs that incorporates parameterization. Subsequently, we propose a method to specify DM behaviors in a hardware-agnostic manner. Then, we apply object-oriented design methods to specify BTs. We combine the three techniques to define Hardware-Agnostic Behavior Tree Leaves , and propose a method to design them so that these unit behaviors can be reused across systems of differing hardware. We demonstrate the applicability of our proposed method by designing a completely hardware-agnostic, and thus reusable, BT software that we then apply on two real robotic systems with differing hardware used at the BMW Group, and we show that the BT software developed using our methods leads to higher reusability (as per Chidamber and Kemerer metrics) by conducting a comparison against a benchmark BT software built using state-of-the-art methods. Four out of the five used Chidamber and Kemerer metrics indicated that our method increased reusability, it resulted in software that is 7.7% shorter

关键词： Industrial robots

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Optimized Admittance Control for Manipulators Interacting with Unknown Environment 25

Optimized Admittance Control for Manipulators Interacting wi...

引用

25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Kong, Haiyi Peng, Guangzhu Li, Guang Yang, Chenguang University Of Manchester Department Of Electrical And Electronic Engineering Oxford Rd ManchesterM13 9PL United Kingdom University Of The West Of England Bristol Robotics Laboratory BristolBS16 1QY United Kingdom School Of Automation Nanjing University Of Information Science And Technology Nanjing210044 China University Of Liverpool Department Of Computer Science LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9798350340266

This paper considers the study scenario that the end-effector of a manipulator follows a desired trajectory and interacts with external environment. To maximize the interaction performance, admittance control is combined with adaptive dynamic programming (ADP). The optimal admittance parameters can be learned online without prior knowledge of the environment. A data-driven Hybrid Iteration is employed in the ADP, which can relax the initial stabilizing requirement and at the same time has a faster convergence rate compared with Value Iteration. In addition, a more accurate environment model is considered in the system control design, where a general iterative expression is proposed to describe the varying contour of the environment. At last, simulation and experimental studies are given to verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Manipulators

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SparseGTN: Human Trajectory Forecasting with Sparsely Represented Scene and Incomplete Trajectories

SparseGTN: Human Trajectory Forecasting with Sparsely Repres...

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

作者： Jianbang Liu Guangyang Li Xinyu Mao Fei Meng Jie Mei Max Q.-H. Meng Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen Key Laboratory of Robotics Perception and Intelligence and the Department of Electronic and Electrical Engineering Southern University of Science and Technology in Shenzhen China Professor Emeritus in the Department of Electronic Engineering The Chinese University of Hong Kong in Hong Kong Professor in the Department of Electrical and Computer Engineering University of Alberta in Canada

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In recent years, great progress has been made in forecasting human motion in crowded scenes. However, current methods are far from practical applications due to the unbearable high computation costs, especially for encoding scene context. In addition, neglecting the partially detected trajectories makes the predicted outcome deviate from the real trajectory distribution. To handle the aforementioned concerns, we propose to represent the scene context and partially observed trajectories with sparse graphs. Customized for this special data structure, we design a hierarchical Graph Transformer Network model SparseGTN to predict multiple possible future trajectories of the target pedestrian by digesting the sparsely represented inputs. Our approach exhibits superiority over the state-of-the-art (SOTA) methods, utilizing a mere 3.42% of the number of floating point operations (FLOPs) and 0.53% of the number of model parameters. The code will be available online ⋆ .

关键词： Pedestrians Switches Predictive models Transformers Encoding Trajectory Mobile robots Forecasting Collision avoidance Intelligent robots

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