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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Shi, Qiuyu Zhao, Xuan Liao, Haisu Lin, Yongji Huang, Ruisen Wu, Xinyu Gao, Fei Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin 999077 Hong Kong

ISBN: (纸本)9798350325706

Magnetorheological (MR) fluids-based devices such as MR dampers and brakes can output a continuously controllable damping force via modulating the magnitude of the applied magnetic field. Owing to low energy consumption and fast response, recently vast MR dampers and brakes are widely studied and explored for lower limb prostheses and exoskeletons. This paper proposed a novel MR brake with adjustable interdigitated comb for lower limb prostheses. Through actively adjusting the area the of the comb engagement, the maximum viscous and field-induced damping torque can be increased or decreased. Because of that, the dynamic adjustment interval of the output damping torque can be increased to meet the function requirements of the prosthetic knee in different walking modes. Magnetic analysis via ANSYS is performed to check the magnetic density of the MR fluid under different excitations. Mathematical models of the damping torque are built. Finally, a prototype of the novel MR brake is fabricated. © 2023 IEEE.

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Self-Organized Sensor Eggs for Decentralized Localization and Sensing on Vulcano Island – A Glimpse into Future Space Exploration with Swarms

Self-Organized Sensor Eggs for Decentralized Localization an...

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International Conference on Information Fusion

作者： Fabio Broghammer Thomas Wiedemann Siwei Zhang Armin Dammann Christian Gentner Petar Djuric Institute of Communications and Navigation German Aerospace Center (DLR) Germany Perception for Intelligent Systems Munich Institute of Robotics and Machine Intelligence TU Munich Germany Department of Electrical and Computer Engineering Stony Brook University USA

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Robotic swarms or portable sensor networks are emerging technologies for sensing physical processes that are spatially distributed- and temporally dynamic, both on Earth and in future Moon/Mars exploration missions. We develop a portable network composed of a multitude of self-organized “sensor eggs”. These eggs are equipped with ultra-wideband (UWB) transceivers, providing precise time and position information without additional infrastructures like Global Navigation Satellite Systems (GNSSs). Each egg is additionally equipped with environmental sensors, for example, a Sulfur dioxide gas sensor to explore volcanic activity. We use a real time decentralized particle filter (DPF) to estimate the a-posteriori probability density functions (PDFs) of the egg positions. These PDFs are then used in a static state binary Bayes filter for estimating the gas sources with potentially complex structures such as cracks on the volcano surface. The proposed sensor network is verified with an in-field experiment at La Fossa volcano on the island of Vulcano, Italy, in 2023.

关键词： Location awareness Volcanic activity Probability density function Robot sensing systems Particle filters Transceivers Sensors Timing Surface cracks Ultra wideband technology

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A Systematic Method for Optimal Gain Auto-Tuning of Pid Control for Nonlinear Quadrotor System Using Deep Reinforcement Learning

SSRN

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SSRN 2023年

作者： Moin, Hassan Shah, Umer Hameed Khan, Muhammad Jawad Sajid, Hasan Department of Robotics and Intelligent Machine Engineering School of Mechanical and Manufacturing Engineering National University of Sciences and Technology Islamabad Pakistan Artificial Intelligence Research Center Department of Mechanical Engineering College of Engineering and Information Technology Ajman University Ajman United Arab Emirates

Quadcopters have under-actuated, highly nonlinear, and coupled dynamics, which makes their control a challenging endeavor. However, PID controllers have exhibited remarkable performance for such systems in a variety of circumstances, including obstacle avoidance, trajectory tracking, and route planning. The outer loop handles mission-level goals, while the inner loop is responsible for its stability and control. Nevertheless, gain optimization for nonlinear systems using heuristics or rule-based approaches is a laborious, time-consuming, and challenging task. Using a deep actor-critic Reinforcement Learning (RL) algorithm with continuous observation and action spaces, this study implements an optimal gain auto-tuning framework for the altitude, attitude, and position controllers of a 6 DoF nonlinear drone system. The state equations are determined using Lagrange's (energy-based) technique, whilst the aerodynamic coefficients are computed numerically using Blade Element Momentum Theory (BEMT). In addition, the asymptotic stability of the cascaded closed loop nonlinear system is investigated using the theory of Lyapunov. The suggested technique is validated by simulations, which demonstrate that the quadcopter can closely follow a specified trajectory by using the optimized gains. Most notably, these optimal gains fulfil the constraints derived from Lyapunov's stability analysis, suggesting that the RL technique is an incredibly powerful instrument that can evaluate the uncertainties present in any complicated nonlinear system. © 2023, The Authors. All rights reserved.

关键词： intelligent systems

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Multi-Agent Reinforcement Learning for Distributed Workflow Orchestration at the Tactical Edge

Multi-Agent Reinforcement Learning for Distributed Workflow ...

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MILCOM, Military Communications Conference

作者： Alessandro Amato Alessandro Morelli Mattia Fogli Raffaele Galliera Niranjan Suri Department of Intelligent Systems & Robotics The University of West Florida (UWF) Pensacola FL USA Florida Institute for Human and Machine Cognition (IHMC) Pensacola FL USA Department of Engineering University of Ferrara Italy US Army DEVCOM Army Research Laboratory (ARL) Adelphi MD USA

ISBN: (数字)9798350374230

ISBN: (纸本)9798350374247

The dynamic nature of tactical edge networks has led to the design of architectures that enable real-time data processing and analytics at the edge, to ensure the continuation of operations when the connection to the headquarters is unavailable. However, workflow orchestration faces unique challenges over frequently disconnected, intermittent, and limited (DIL) networks, where traditional approaches, mainly developed for cloud-like environments, lack the flexibility to react promptly to ever-changing conditions. This paper presents a novel decentralized partially observable Markov decision process (DEC-POMDP) formulation for the distributed workflow orchestration problem, where agents need to cooperate to maximize the computation efficiency while reducing the data transmission time. We propose a solution based on multi-agent reinforcement learning (MARL) that leverages graph convolutional reinforcement learning (DGN) and graph attention networks (GAT) to enable agents to share information with each other, capture the network’s structural information, ensure scalability, and eliminate the needs for global knowledge of the network. Training and experiments, which compare our solution with the corresponding constraint satisfaction problem (CSP), are conducted in a simulated 2D urban scenario that mimics nodes’ mobility and communications, showing promising results.

关键词： Training Military communication Knowledge engineering Scalability Markov decision processes MIMICs Reinforcement learning Real-time systems Faces Electronics packaging

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Mechanically Induced Piezotronic Effect in Flexible Two-Dimensional Pdse2 Thin Films

SSRN

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SSRN 2024年

作者： Shim, Jaesool Mallem, Siva Pratap Reddy Puneetha, Peddathimula Lee, Dong-Yeon An, Sung Jin Advanced Material Research Center Kumoh National Institute of Technology Gumi39177 Korea Republic of Department of Robotics and Intelligent Machine Engineering College of Mechanical and IT Engineering Yeungnam University Gyeongsan38541 Korea Republic of School of Mechanical Engineering Yeungnam University Gyeongsan38541 Korea Republic of Department of Materials Science and Engineering Kumoh National Institute of Technology Gumi39177 Korea Republic of

Flexible devices with the capacity to develop adaptive and ideal interactions between machines and humans are valuable in healthcare, artificial intelligence, and the Internet of Things (IoTs). In this study, we fabricated a flexible strain-sensitive sensor using graphene and a two-dimensional PdSe2 thin film through chemical vapor deposition (CVD) and placed it onto a PET film. Mechanically induced piezotronic effect in compressive and tensile modes was used to control the Schottky barrier height based on piezoelectric charge polarization, and therefore, to synergistically modulate the current conduction process. The piezotronic device exhibited seamless performance with a strain-gauge value of 715 at a compressive strain of -0.24%, which is 9.48 times more than that of a typical strain-gauge value. Furthermore, at low temperatures (240 K) the device showed a larger gauge factor at an applied strain of 0.24% owing to the decreased screening effect that underlays the increase in piezocharges. Our findings provide evidence for the mechanically induced piezotronic effect in PdSe2 thin films, a significant structure that enable versatile functionalities in innovative micro- and nanoelectromechanical systems and devices. © 2024, The Authors. All rights reserved.

关键词： Thin films

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Multi-contact Stochastic Predictive Control for Legged Robots with Contact Locations Uncertainty

arXiv

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arXiv 2023年

作者： Gazar, Ahmad Khadiv, Majid Del Prete, Andrea Righetti, Ludovic Max Planck Institute for Intelligent Systems Tuebingen Germany Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Industrial Engineering Department University of Trento Italy Tandon School of Engineering New York University New York United States

Trajectory optimization under uncertainties is a challenging problem for robots in contact with the environment. Such uncertainties are inevitable due to estimation errors, control imperfections, and model mismatches between planning models used for control and the real robot dynamics. This induces control policies that could violate the contact location constraints by making contact at unintended locations, leading to unsafe motion plans. This work addresses the problem of robust kino-dynamic whole-body trajectory optimization using stochastic nonlinear model predictive control (SNMPC) by considering additive uncertainties on the model dynamics subject to contact location chance constraints as a function of the robot’s full kinematics. We demonstrate the benefit of using SNMPC over classic nonlinear MPC (NMPC) for whole-body trajectory optimization in terms of contact location constraint satisfaction (safety). We run extensive Monte-Carlo simulations for a quadruped robot performing agile trotting and bounding motions over small stepping stones, where contact location satisfaction becomes critical. Our results show that SNMPC can perform all motions safely with 100% success rate, while NMPC failed 48.3% of all motions. © 2023, CC BY.

关键词： Stochastic systems

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Design and preliminary testing of a piezoelectric-based sole energy harvester

Design and preliminary testing of a piezoelectric-based sole...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Wang, Yidi Zhao, Xuan Shi, Qiuyu Liao, Haisu Wu, Xinyu Gao, Fei Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin999077 Hong Kong

ISBN: (纸本)9781665481090

Harvesting energy from the human body to replenish existing energy sources is receiving substantial attention. Recently scientists have been focusing on harvesting biomechanical energy from human feet during walking. This paper develops a piezoelectric-based sole energy harvester that is designed to be worn on the bottom of shoes to harvest energy, thus powering wearable electronic devices. Specifically, a cam with piezoelectric(PZT) stacks is mounted on the heel to scavenge energy during the heel strike. The cam can change the input's direction from the vertical direction to the horizontal direction. In addition, the output power of this part can be optimized or improved by generating a specified cam profile. A bimorph piezoelectric plate is installed on the forefoot to convert the toes' bending deformation into electricity. Herein, piezoelectric Macro Fiber Composites (MFC) are employed to capture the large deformation. Preliminary testing on a healthy subject is conducted to validate this design. The average power output of the harvester is 8.83 m W. © 2022 IEEE.

关键词： Cams

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6DoF-ICTC: A Deep Image Feature Extraction Network Combining Transformers and CNNs for 6DoF Pose Estimation 12

6DoF-ICTC: A Deep Image Feature Extraction Network Combining...

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12th International Conference on CYBER Technology in Automation, Control, and intelligent Systems, CYBER 2022

作者： Zhang, Liming Zhou, Xin Zhu, Baixian Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Science and Technology of China China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In this work, we propose a new image feature extraction network for 6D pose estimation tasks. The majority of existing 6D pose estimation networks are based on the RGBD input, which is the fusion of images and point clouds. They are mainly working on keypoint extraction, point cloud feature extraction, and other tasks, however, image features, on the other hand, mainly rely on CNN networks that are currently advanced. With the development of Vision Transformer in the field of computer vision, the results on ImageNet demonstrate its benefit of extracting global features, and we see new directions that the Transformers can bring to the area of image feature extraction. ICTC is a Transformer paired with a CNN network that fuses the retrieved global features into each pixel and then does local feature extraction. This provides greater robustness to 6D pose estimation In complex situations such as shifting lighting conditions and object occlusion. Our method achieves 96% accuracy using the average ADD metric on the LineMOD dataset, demonstrating our method's effectiveness. © 2022 IEEE.

关键词： Feature extraction

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Center of Mass Kinematics Robustly Predict Multidirectional Reactive Joint Torques During Perturbed Standing

Center of Mass Kinematics Robustly Predict Multidirectional ...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Kristen L. Jakubowski Gregory S. Sawicki Lena H. Ting Department of Biomedical Engineering Emory University and Georgia Institute of Technology Atlanta GA USA George W. Woodruff School of Mechanical Engineering Insitute for Robotics and Intelligent Machines School of Biological Sciences Georgia Institute of Technology Atlanta GA USA Institute for Human Machine Cognition Pensacola FL USA Department of Rehabilitation Medicine Division of Physical Therapy Emory University Atlanta GA USA

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Recently, exoskeleton controllers have been developed to augment balance. However, controllers have not been validated against multidirectional perturbations, and only actuate a single joint without considering the coordinated multi-joint response required to maintain balance. By leveraging the human physiological response to postural perturbations, we may be able to overcome these limitations. We have developed a sensorimotor response model (SRM) for perturbed standing balance that robustly predicts the coordinated activations of lower limb muscles based on center of mass (CoM) kinematics. Recently, a modified version of the SRM was used to reconstruct sagittal plane ankle torque during perturbed standing and walking. Building on this work, we further modified the SRM so it could predict the multi-joint torque response. Using the newly modified CoM-driven SRM, we reconstructed joint torques at the ankle, knee, and hip in response to support surface perturbations in 8 different directions during standing (0, 45, 90, 135, 180, 225, 270, 315°) with good temporal accuracy. Notably, the SRM was only tuned in the 4 cardinal directions (0, 90, 180, 270°), after which it could predict the torque response in the diagonal directions (45, 135, 225, 315°). With the average R2 and VAFs across all joints and perturbation directions of 0.83 and 0.84, respectively, our results support the viability of the SRM as an exoskeleton controller to augment balance in response to perturbations in varying directions. A controller that uses a single control vector, CoM kinematics (e.g., one-to-many), rather than one that needs a control signal for every joint (e.g., one-to-one) may simplify the control of exoskeletons, as well as bipedal robots, and lower-limb prostheses.

关键词： Ankle Knee Torque Perturbation methods Exoskeletons Kinematics Predictive models Robot sensing systems Vectors Hip

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Hydrogen Evolution Properties: Cr Doping and V Co-Doping Effect of Zns Nanoparticles

SSRN

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SSRN 2023年

作者： Poornaprakash, Bathalavaram Puneetha, Peddathimula Sangaraju, Sambasivam Yeon, Young Joo Lee, Dong-Yeon Ramu, S. Kim, Y.L. Department of Electronic Engineering Gangneung-Wonju National Univeristy Gangneung25457 Korea Republic of Department of Robotics and Intelligent Machine Engineering College of Mechanical and IT Engineering Yeungnam University Gyeongsan38541 Korea Republic of National Water and Energy Center United Arab Emirates University Al Ain15551 United Arab Emirates Department of Biochemical Engineering Gangneung-Wonju National Univeristy Gangneung25457 Korea Republic of

In this study, the effects of Cr doping and V co-doping on the hydrogen (H2) evolution properties of ZnS nanoparticles (NPs) were evaluated. An economical and facile hydrothermal method was used to synthesize ZnS, Zn0.99Cr0.01S, Zn0.98V0.01Cr0.01S, and Zn0.97V0.02Cr0.01S NPs. All the samples exhibited a cubic structure with effective Cr doping and V co-doping in the parent ZnS lattice. The valence states of Cr and V in the host matrix were 3+ and 4+, respectively, as confirmed by X-ray photoelectron spectroscopy (XPS). The variable bandgap of ZnS was engineered by Cr doping and V co-doping. Therefore, blue emission with a diminished fluorescence efficiency was achieved for the ZnS system by doping and co-doping. The synthesized NPs were evaluated for H2 evolution by water splitting under artificial solar illumination. The Zn0.97V0.02Cr0.01S system showed the highest H2 evolution potential (12098 µmol h−1g−1) in 5 h. Thus, V co-doping enriched the H2 production capacity of the ZnS:Cr system. © 2023, The Authors. All rights reserved.

关键词： Zinc sulfide

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