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arXiv 2024年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J.P.C. School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Electrical and Information Engineering Zhengzhou University the Henan International Joint Laboratory of Intelligent Health Information System The National Telemedicine Center The National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom The Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of School of Software Nanjing University of Information Science and Technology Nanjing210044 China Amazonas State University AM Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC systems. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU’s utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 92.11%, increasing the final user utility function value by 0.24%, and decreasing the variance of results by 95.45%. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Real-time force control of an SEA-based body weight support unit with the 2-DOF control structure

arXiv

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arXiv 2018年

作者： Sun, Yubo Lei, Yuqi Zou, Wulin Li, Jianmin Yu, Ningbo Institute of Robotics and Automatic Information Systems Nankai University Haihe Education Park Tianjin300353 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Haihe Education Park Tianjin300353 China Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States School of Mechanical Engineering Tianjin University Tianjin300072 China

Body weight support (BWS) is a fundamental technique in rehabilitation. Along with the dramatic progressing of rehabilitation science and engineering, BWS is quickly evolving with new initiatives and has attracted deep research effort in recent years. We have built up a novel gravity offloading system, in which the patient is allowed to move freely in the three-dimensional Cartesian space and receives support against gravity. Thus, the patients, especially for those that suffer from neurological injury such as stroke or spinal cord injury, can focus their residual motor control capabilities on essential therapeutic trainings of balance and gait. The realtime force control performance is critical for the BWS unit to provide suitable support and avoid disturbance. In this work, we have re-designed our BWS unit with a series elastic actuation structure to improve the human-robot interaction performance. Further, the 2 degrees of freedom (2-DOF) control approach was taken for accurate and robust BWS force control. Both simulation and experimental results have validated the efficacy of the BWS design and real-time control methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Force control

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Energy Efficient Train-Ground mmWave Mobile Relay System for High Speed Railways

arXiv

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arXiv 2022年

作者： Wang, Lei Ai, Bo Niu, Yong Zhong, Zhangdui Mao, Shiwen Wang, Ning Han, Zhu The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Peng Cheng Laboratory Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering Auburn University AuburnAL36949-5201 United States The School of Information Engineering Zhengzhou University Zhengzhou450001 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

The rapid development of high-speed railways (HSRs) puts forward high requirements on the corresponding communication system. Millimeter wave (mmWave) can be a promising solution due to its wide bandwidth, narrow beams, and rich spectrum resources. However, with the large number of antenna elements employed, energy-efficient solutions at mmWave frequencies are in great demand. Based on a mmWave HSR communication system with multiple mobile relays (MRs) on top of the train, a dynamic power-control scheme for train-ground communications is proposed. The scheme follows the regular movement characteristics of high-speed trains and considers three phases of train movement: the train enters the cell, all MRs are covered in the cell, and the train leaves the cell. The transmit power is further refined according to the number of MRs in the cell and the distance between the train and the remote radio head. By minimizing energy consumption under the constraints of the transmitted data and transmit power budget, the transmit power is allocated to multiple MRs through the multiplier punitive function-based algorithm. Comprehensive simulation results, where the velocity estimation error is taken into account, are provided to demonstrate the effectiveness of the proposed scheme over several baseline schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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Trajectory Tracking of Tractor-Trailer Wheeled Mobile Robots via Dynamic Feedback Linearization in Forward and Backward Motion

Trajectory Tracking of Tractor-Trailer Wheeled Mobile Robots...

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International Conference on control, Instrumentation, and Automation

作者： Mohammad Olyai Khalil Alipour Bahram Tarvirdizadeh Majid Sorouri Mohammad Ghamari Department of Mechatronics Eng. School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology Advanced Service Robots (ASR) Laboratory University of Tehran Tehran Iran Department of Electronic Engineering Maynooth University Maynooth Co. Kildare Ireland Department of Computer and Mechatronics Engineering Science and Research Branch Islamic Azad University Tehran Iran Electrical Engineering Department California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798331509972

ISBN: (纸本)9798331509989

One type of wheeled mobile robot widely used in public transportation and for carrying high payloads is the tractor-trailer wheeled robots (TTWRs). This study considers a differentially-driven tractor under pure rolling conditions, which is subject to nonholonomic constraints. controlling a TTWR in both backward and forward motion is challenging due to inherent instability. To address this issue and achieve trajectory tracking control for these systems, this paper employs dynamic feedback linearization (DFL) to overcome the limitations of static feedback linearization (SFL). The system’s response will be examined under various trajectories and initial conditions for both forward and backward motion.

关键词： Trajectory tracking Dynamics Wheels Kinematics Reinforcement learning Feedback linearization Trajectory Mobile robots Public transportation Payloads

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Path Following of a Tractor-Trailer System via Dynamic Extension in Forward and Backward Motion

Path Following of a Tractor-Trailer System via Dynamic Exten...

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

作者： Mohammad Olyai Khalil Alipour Bahram Tarvirdizadeh Majid Sorouri Mohammad Ghamari Department of Mechatronics Eng. Advanced Service Robots (ASR) Laboratory School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology University of Tehran Tehran Iran Department of Electronic Engineering Maynooth University Maynooth Co. Kildare Ireland Department of Computer and Mechatronics Engineering Science and Research Branch Islamic Azad University Tehran Iran Electrical Engineering Department California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Tractor-trailer wheeled robots (TTWRs) have gained significant attention across various industries and control studies due to their complex dynamics and non-minimum phase behavior, which pose substantial control challenges. This study investigates the control of a differentially-driven tractor to follow a specified path under pure rolling conditions in both forward and backward motion. Notably, it has been established that using static feedback linearization (SFL) leads to instability during backward motion. In contrast, this research uses dynamic feedback linearization (DFL), also known as dynamic extension, for controlling the tractor-trailer system in both directions for the first time. Our findings demonstrate that the internal dynamics of the system remain stable in both forward and backward motions, enabling successful adherence to the desired path.

关键词： Industries Mechatronics Service robots Dynamics Wheels Optimal control Reinforcement learning Agricultural machinery Feedback linearization Mobile robots

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State Estimation for Linear systems with Quadratic Outputs

arXiv

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arXiv 2023年

作者： Berkane, Soulaimane Theodosis, Dionysis Hamel, Tarek Dimarogonas, Dimos V. Département d’informatique et d’ingénierie Université du Québec en Outaouis GatineauQC Canada theDepartment of Electrical Engineering Lakehead University ON Canada the Dynamic Systems and Simulation Laboratory Technical University of Crete Greece the Divi-sion of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden I3S-CNRS University Côte d’Azur Institut universitaire de France France

This letter deals with the problem of state estimation for a class of systems involving linear dynamics with multiple quadratic output measurements. We propose a systematic approach to immerse the original system into a linear time-varying (LTV) system of a higher dimension. The methodology extends the original system by incorporating a minimum number of auxiliary states, ensuring that the resulting extended system exhibits both linear dynamics and linear output. Consequently, any Kalman-type observer can showcase global state estimation, provided the system is uniformly observable. © 2023, CC BY.

关键词： Time varying systems

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Adaptive Optimal electrical Resistance Tomography for Large-Area Tactile Sensing

Adaptive Optimal Electrical Resistance Tomography for Large-...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wendong Zheng Huaping Liu Di Guo Wuqiang Yang Department of Computer Science and Technology Tsinghua University Beijing China the State Key Laboratory of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Electronic Engineering The University of Manchester Manchester U.K.

It is critical to perceive physical contact for intelligent robots to safely interact in dynamic, unstructured environments. As physical contacts can occur at any location, a well-performing tactile sensing system should be able to deploy a large area on robotic surface. Some researchers have implemented large-area tactile sensors by using sensing arrays, but it is challenging to deploy many sensing elements. electrical resistance tomography (ERT) has recently been introduced into tactile sensing to overcome some of the limitations with conventional tactile sensing arrays, and good results have been achieved for some robotic applications. However, a particular challenge is that spatial resolution is low. Although various attempts have been made to improve the performance of ERT-based tactile sensors, the intrinsic resolution issue remains unsolved. In this paper, we propose a novel adaptive optimal drive strategy for efficient ERT-based large-area tactile sensing for robotic applications, which can adaptively select the current injection and voltage measurement pattern for optimal tactile stimulus. In particular, regions of tactile contacts are preliminarily detected and localized by a base scanning pattern with only a few measurement data. According to this detected region, the adaptive strategy can select the optimal current injection and voltage measurement pattern to improve the sensing performance by maximizing the current density. To verify the effectiveness of the proposed strategy, the proposed method is comprehensively evaluated by simulation and experiments. The results revealed that the optimal strategy can effectively improve both spatial and temporal resolution.

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Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

arXiv

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arXiv 2024年

作者： Hu, Xuemin Chen, Pan Wen, Yijun Tang, Bo Chen, Long School of Artificial Intelligence Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Wuhan430062 China Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China WAYTOUS Inc. Beijing100083 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an710049 China

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but the occurring probability of an unsafe state is still high, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to enhance the shortterm state safety that the vehicle explores, while the long-term constraint enhances the overall safety of the vehicle throughout the decision-making process, both of which are jointly used to enhance the vehicle safety in the training process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Pattern Recognition for Electric Energy Consumption Prediction in a laboratory Environment

Pattern Recognition for Electric Energy Consumption Predicti...

引用

IEEE Symposium Series on Computational Intelligence

作者： Guneet Bedi Ganesh Kumar Venayagamoorthy Rajendra Singh Holcombe Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory School of Engineering University of Kwazulu-Natal Durban south Africa Department of Automotive Engineering Clemson University Clemson SC USA

Particle Swarm Optimization (PSO), a computational intelligence (CI) technique, is applied in a laboratory environment to recognize existence of a pattern between the net energy consumption by the electric loads in the building and the ambient temperature along with the occupancy state of the building;and use the detected pattern to predict energy consumption in the near-future. The electric loads under consideration include lighting and heating, ventilation and air conditioning (HVAC) units with intelligent monitoring and control capabilities using internet of things (IoT) devices and technologies. Having this prediction capability is extremely useful to ensure sufficient energy is generated to meet the demands of the electric loads at any time. This, in turn, reduces energy waste due to excess generation.

关键词： Computational intelligence Electric energy consumption prediction Internet of things Particle swarm optimization

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Decentralized Trajectory Planning for Formation Flight in Unknown and Dense Environments

Decentralized Trajectory Planning for Formation Flight in Un...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Jianxin Zeng Yaonan Wang Zhiqiang Miao Wei He Hesheng Wang Department of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Changsha China School of Intelligence Science and Technology University of Science and Technology Beiing Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

For aerial swarms, formation flight has been applied in various scenes. However, most existing works do not consider balancing the conflicting requirements among keeping formation, keeping the smoothness of trajectories, and obstacle avoidance within the limited time. To address this issue, we propose a decentralized trajectory planning framework for formation flight in unknown and dense environments. To ensure that feasible trajectories can be found within the limited time, the formation optimization problem is decoupled into formation affine transformation and iterative trajectory generation. Firstly, the optimization problem based on affine transformation is designed to obtain the optimal affine transformation sequence, which provides the formation reference of trajectory optimization. Secondly, the iterative optimization framework of trajectory planning is designed, which balances the conflicting requirements of formation, smooth flight, and obstacle avoidance. Besides, to escape the local minima caused by non-convex dense environments, the method of topological path planning is designed to provide distinctive initial solutions for trajectory optimization. Finally, the proposed methods are proven to be effective through the simulations and real-world experiments.

关键词： Trajectory planning Planning Iterative methods Collision avoidance Trajectory optimization intelligent robots

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