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MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time systems

ACM Transactions on Design Automation of Electronic systems 2025年第3期30卷 1-29页

作者： Ahmadi-Pour, Sallar Saha, Sangeet Mcdonald-Maier, Klaus Drechsler, Rolf Institute of Computer Science University of Bremen Faculty 3 Mathematics and Computer Science Bremen Bremen Germany Robotics and Embedded Systems Research Group University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Cyber-Physical Systems DFKI Rheinland-Pfalz Kaiserslautern Germany

Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today's Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this article, we consider the challenge of scheduling an RT application modeled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTG on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6% and area usage reductions of 46.3% are possible compared to full Software (SW) and HW execution, respectively. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Integer programming

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Modular Robot Task Execution Framework for Industrial PCB Handling: A Behavior Tree Approach

Modular Robot Task Execution Framework for Industrial PCB Ha...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Sharath Chandra Akkaladevi Markus Ganglbauer Kapil Deshpande Sebastian Ukleja Andreas Pichler Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria Robotics and Automation Systems Profactor GmbH Steyr-Gleink Austria

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Handling printed circuit boards (PCBs) in industrial environments presents significant challenges due to design variability, high precision requirements, and the need for scalability. Traditional manual workflows are laborintensive and error-prone, while fixed robotic systems lack the adaptability to manage diverse PCB types and task variations. This paper proposes a Behavior Tree (BT)-based framework for automating PCB handling tasks, integrating advanced robotic skills such as detection, grasping, and precise placement. The framework is enhanced by a multimodal Vision System for robust pose estimation, an intuitive Human Interface for task reconfiguration, and a modular Robotic Manipulation Module for reliable execution. The BT-based architecture ensures modularity, and adaptability, addressing the demands of dynamic industrial workflows. Experimental validation demonstrates the framework's ability to handle PCB variability and execute precision-critical tasks efficiently, showcasing its potential for scalable deployment in industrial settings.

关键词： Automation Service robots Scalability Machine vision Printed circuits Pose estimation Grasping Manuals Reliability engineering Robustness

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Performance Characteristics of EMG Controlled Prosthetic Hand 23

Performance Characteristics of EMG Controlled Prosthetic Han...

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6th International Conference on Advances in robotics, AIR 2023

作者： Kalita, Amlan Jyoti Chanu, Maibam Pooya Kakoty, Nayan M. Vinjamuri, Ramana Kumar Borah, Satyajit Embedded Systems and Robotics Laboratory Tezpur University Tezpur India Vinjamuri Lab University of Maryland Baltimore County Baltimore United States Tezpur Institute of Medical Sciences Pvt. Ltd. Assam Tezpur India

ISBN: (纸本)9781450399807

Much development has been seen in commercial and laboratory prototypes of prosthetic hands during last two decades. However, prosthetic hands emulating human hand characteristics are very limited. In order to emulate human hand, performance characteristics evaluation of prosthetic hands is of paramount importance. This paper explains the performance characteristics of an EMG CoNtrolled PRosthetIC Hand called ENRICH involving end users' feedback from clinical testing. ENRICH is a real time EMG controlled prosthetic hand that can perform grasping operations in 250 ± 1.1 milliseconds satisfying the neuromuscular constraint of human hand. The performance characteristics of ENRICH vis-à-vis commercial and laboratory prototypes are evaluated in terms of weight, size, degrees of freedom, finger joint range of motion, control strategy, operation time and clinical testing. This evaluation establishes ENRICH as one of the promising prosthetic hands with tangible benefits to amputees. © 2023 ACM.

关键词： Degrees of freedom (mechanics)

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Multi-Agent Reinforcement Learning for Cooperative Vehicle Motion Control

Multi-Agent Reinforcement Learning for Cooperative Vehicle M...

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International Conference on Intelligent Transportation

作者： Kenan Ahmic Johannes Ultsch Jonathan Brembeck Darius Burschka Institute of System Dynamics and Control at the German Aerospace Center (DLR) Wessling Germany Chair of Robotics Artificial Intelligence and Embedded Systems Technical University of Munich (TUM) Munich Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The longitudinal and lateral low-level motion control of multiple vehicles within a platoon is a challenging task, since several different control objectives need to be solved: (i) Each vehicle in the platoon needs to follow the reference path, (ii) the leading vehicle needs to drive with a desired reference velocity, and (iii) the following vehicles need to maintain a safe spacing distance to their respective preceding vehicle. Typically, several distinct controllers are developed for each task individually, which increases both the engineering effort as well as the susceptibility to errors. We address this issue and present a cooperative low-level vehicle motion controller based on Multi-Agent Reinforcement Learning (MARL) that is able to solve all of the above-mentioned control objectives for both the leading vehicle and the following vehicles. Therefore, we apply parameter sharing within MARL to update a single control policy in a centralized fashion using the experiences of all vehicles in the environment. Additionally, we utilize the concept of agent indication during the training process and enable the policy to specialize on the control objectives of the vehicle it is currently controlling. This leads to a unifying control approach and makes the development of further controllers redundant. The simulative assessment demonstrates the effectiveness of learned policy and shows that it is able to successfully solve all of the above-mentioned control objectives for both vehicles roles, even on unseen paths.

关键词： Training Process control Reinforcement learning Drives Vectors Robustness Collision avoidance Intelligent transportation systems

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EEG-Based Hand Movement Recognition: Feature Domain and Level of Decomposition 1

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13th National Conference on Industrial Problems on Machines and Mechanism, IPRoMM 2020

作者： Phukan, Nabasmita M. Kakoty, Nayan Gupta, Nipun Baruah, Neelanjana Jorhat Engineering College Jorhat India Embedded Systems and Robotics Laboratory Tezpur University Tezpur India Madhav Institute of Technology and Science Gwalior India

ISBN: (数字)9789811617690

ISBN: (纸本)9789811617683

This manuscript reports feature domains for the recognition of right and left hand movements using Electroencephalogram (EEG). A 21-channel EEG dataset of seven subjects during right and left hand fist open and close movements was collected from PhysioNet of the BCI2000 Instrumentation system. Features in time, frequency, and time-frequency domains have been explored. Support vector machine with radial basis function kernel was used for the recognition of right and left hand fist open and close movements. The recognition with time, frequency, and time-frequency domain features resulted in an accuracy of 90%, 92%, 97.5%, respectively. Time-frequency domain features obtained through discrete wavelet transform (DWT) at four decomposition levels have resulted in maximum recognition rate. The highest recognition rate of 98.6 ± 0.6 % has resulted in DWT features at level 2. This was substantiated by the fact that DWT features at level 2 establish maximum correlation with pre-processed EEG. Experimental result shows that time-frequency is the best performing feature domain among the three. Further, correlation measure of time-frequency domain features with EEG are established as a benchmark for selecting DWT decomposition level. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Non-Axiomatic Reasoning for an Autonomous Mobile Robot

Non-Axiomatic Reasoning for an Autonomous Mobile Robot

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IEEE International Conference on robotics and Automation (ICRA)

作者： Patrick Hammer Peter Isaev Lei Feng Robert Johansson Jana Tumova Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden Department of Computer and Information Systems Temple University Philadelphia PA USA Unit of Mechatronics and Embedded Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Psychology Stockholm University Stockholm Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We present the integration of a Non-Axiomatic Reasoning System (NARS) with mobile robots for planning and decision making. NARS enables robots to effectively handle uncertainty in real-time with complete sensor and actuator integration, thereby ensuring adaptability to evolving scenarios. We discuss essential parts of the logic, the architecture and working principles of NARS, and the integration of NARS as a ROS node. A case study is provided demonstrating the system’s proficiency to carry out a garbage collection task in an open-air environment by operating a mobile robot with manipulator arm, and we demonstrate its ability to learn about the place-dependent accumulation of garbage items. Case study also reveals that our approach performs more effectively on the overall task than the Belief-Desire-Intention model we compared with.

关键词： Actuators Uncertainty Object detection Switches Robot sensing systems Manipulators Cognition

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Semantic knowledge based reasoning framework for human robot collaboration

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Procedia CIRP 2021年 97卷 373-378页

作者： Sharath Chandra Akkaladevi Matthias Plasch Michael Hofmann Andreas Pichler Robotics and Autonomous Systems Profactor GmbH Im Stadtgut A2 Steyr-Gleink 4407 Austria Institute of Networked and Embedded Systems Alpen-Adria University Klagenfurt 9020 Austria

Human robot collaborative assemblies, where humans and robots work together, are becoming the new frontier in industrial robotics. However, the aspect of humans to easily and intuitively interact/collaborate with a robot, especially for teaching new tasks , is still open. In this work, we present a semantic knowledge-based-reasoning framework that first learns to recognize human activities using perception data (action recognition and object tracking) and semantically links them to an assembly process. Thus deriving the intention of the human interaction in the teaching process. The framework then extracts relevant parameters for the robot action execution using an interactive skill-based programming approach . To resolve ambiguities during the teaching process, the reasoning framework initiates an interaction at an (semantically) abstract level with the user by exploiting previous knowledge and the current environmental setup. The reasoning framework is demonstrated in two different application scenarios involving human-robot collaborative teaching and a user guidance system respectively.

关键词： Human robot collaboration Knowledge based reasoning Semantic learning

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Provably Convergent Federated Trilevel Learning

arXiv

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arXiv 2023年

作者： Jiao, Yang Yang, Kai Wu, Tiancheng Jian, Chengtao Huang, Jianwei Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach;2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes µ-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed µ-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the µ-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain ϵ-stationary point is upper bounded by (Formula Presented). Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80%. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Capacitor Minimization in Analog Synapses for Efficient and Compact Binarized SNNs

IEEE Transactions on Circuits and Systems for Artificial Int...

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IEEE Transactions on Circuits and systems for Artificial Intelligence 2025年第2期2卷 114-125页

作者： Mikail Yayla Ming-Liang Wei Simon Thomann Albi Mema Chia-Lin Yang Jian-Jia Chen Hussam Amrouch Design Automation for Embedded Systems Group TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Department of the Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Technical University of Munich (TUM) Munich Germany TUM School of Computation Information and Technology Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Munich Germany Department of Computer Science and Information Engineering Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Department of Computer Science TU Dortmund University Dortmund Germany Lamarr Institute Dortmund Germany

Accelerating the immense workloads of Neural Networks (NNs) is a critical challenge, and analog computing-based approaches present a promising solution. Among these, the Integrate-and-Fire (IF) Spiking Neural Network (SNN) stands out for its potential in efficient neural computation. However, achieving high inference accuracy through precise multiply-accumulate (MAC) operations necessitates a large membrane capacitor—224 to 547 times larger than the synapse array at the 14nm technology node—resulting in prohibitive area costs. Additionally, the large capacitor size introduces higher energy consumption and longer access times. Thus, a key research challenge is maintaining accuracy while mitigating the costs associated with the large membrane capacitor. In this work, we propose a HW/SW Codesign method, called CapMin, for capacitor size minimization in analog computing IF-SNNs. CapMin minimizes the capacitor size by reducing the number of spike times needed for accurate operation of the HW, based on the absolute frequency of MAC level occurrences in the SW. To increase the operation of IF-SNNs to current variation, we propose the method CapMin-V, which trades capacitor size for protection based on the reduced capacitor size found in CapMin. In our experiments, CapMin achieves more than a 14 $\times$ reduction in capacitor size along with a 34% reduction in energy consumption over the state of the art, while CapMin-V achieves increased variation tolerance in the IF-SNN operation, requiring only a small increase in capacitor size.

关键词： Capacitors Circuits Accuracy Capacitance Micromechanical devices Costs Neurons Integrated circuit modeling Spiking neural networks Hardware

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Semantic knowledge based reasoning framework for human robot collaboration 8

Semantic knowledge based reasoning framework for human robot...

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8th CIRP Conference of Assembly Technology and systems, CATS 2020

作者： Akkaladevi, Sharath Chandra Plasch, Matthias Hofmann, Michael Pichler, Andreas Robotics and Assistive Systems Profactor GmbH Im Stadtgut A2 Stey-Gleink4407 Austria Institute of Networked and Embedded Systems Alpen-Adria University Klagenfurt9020 Austria

Human robot collaborative assemblies, where humans and robots work together, are becoming the new frontier in industrial robotics. However, the aspect of humans to easily and intuitively interact/collaborate with a robot, especially for teaching new tasks, is still open. In this work, we present a semantic knowledge-based-reasoning framework that first learns to recognize human activities using perception data (action recognition and object tracking) and semantically links them to an assembly process. Thus deriving the intention of the human interaction in the teaching process. The framework then extracts relevant parameters for the robot action execution using an interactive skill-based programming approach. To resolve ambiguities during the teaching process, the reasoning framework initiates an interaction at an (semantically) abstract level with the user by exploiting previous knowledge and the current environmental setup. The reasoning framework is demonstrated in two different application scenarios involving human-robot collaborative teaching and a user guidance system respectively. © 2020 The Author(s).

关键词： Knowledge based systems

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