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SSRN 2023年

作者： Li, Shengjie Wang, Jin Zhang, Haiyun Feng, Yichang Lu, Guodong Zhai, Anbang State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China The Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China School of Robotics Ningbo University of Technology Ningbo315211 China

In the robot intelligent manufacturing scenario, working efficiency, energy consumption and motion stability are the important factors to evaluate the robotic working performance. In this study, the problem of collision-free comprehensive motion optimization for serial robots is discussed. Aiming at the Time, Energy consumption, and Impact (T-E-I) of the manipulator during movement, an incremental comprehensive motion optimization algorithm with adaptive fine-tuning strategy (iCMO-AFS) is proposed. Firstly, the objective function is constructed for the comprehensive performance optimization problem, and the constraints are converted into penalty functions to facilitate the solution. In particular, for the modeling problem of robot energy consumption, the static power regression model is constructed to improve the evaluation accuracy. Secondly, based on the optimization framework of adaptive sampling and accelerated gradient descent, the incremental trajectory optimization of the T-E-I performance is carried out. In addition, the above method is extended to the velocity optimization problem, which further improves the comprehensive motion performance of the robot. Thirdly, an adaptive fine-tuning strategy is proposed to modify the sampling method and collision model, which improves the solution performance. Simulation and experimental tests in several scenarios show that the proposed method can achieve satisfactory results in both planning efficiency and working performance. © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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Recalibrating the KITTI Dataset camera setup for improved odometry accuracy

arXiv

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arXiv 2021年

作者： Cvišić, Igor Marković, Ivan Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia

Over the last decade, one of the most relevant public datasets for evaluating odometry accuracy is the KITTI dataset. Beside the quality and rich sensor setup, its success is also due to the online evaluation tool, which enables researchers to benchmark and compare algorithms. The results are evaluated on the test subset solely, without any knowledge about the ground truth, yielding unbiased, overfit free and therefore relevant validation for robot localization based on cameras, 3D laser or combination of both. However, as any sensor setup, it requires prior calibration and rectified stereo images are provided, introducing dependence on the default calibration parameters. Given that, a natural question arises if a better set of calibration parameters can be found that would yield higher odometry accuracy. In this paper, we propose a new approach for one shot calibration of the KITTI dataset multiple camera setup. The approach yields better calibration parameters, both in the sense of lower calibration reprojection errors and lower visual odometry error. We conducted experiments where we show for three different odometry algorithms, namely SOFT2, ORB-SLAM2 and VISO2, that odometry accuracy is significantly improved with the proposed calibration parameters. Moreover, our odometry, SOFT2, in conjunction with the proposed calibration method achieved the highest accuracy on the official KITTI scoreboard with 0.53% translational and 0.0009 deg/m rotational error, outperforming even 3D laser-based methods. © 2021, CC BY.

关键词： Stereo image processing

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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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Distributions of Tension and Torsion in a Threaded Connection

SSRN

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SSRN 2023年

作者： Shi, Tengfei Liu, Yang Liu, Zhao Liu, Caishan State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China Exeter Small-Scale Robotics Lab Engineering Department University of Exeter North Park Road ExeterEX4 4QF United Kingdom

Threaded connection is a common structure in engineering applications, and its load distribution plays a critical role on the static and dynamic performances of the system. Although Sopwith (1948) studied the tension distribution along a thread helix, a comprehensive model that includes the tension-torsion coupling in threaded connections is still missing. In this paper, we model the threaded connection by considering the bolt and nut as two linearly elastic bodies with a tension-torsion coupling deformation. Deflection of the thread is calculated by the modified theory of the trapezoidal cantilever beam model. Based on the compatibility condition of bolt-nut deformation, we establish a new equilibrium equations for the threaded connection with the tension-torsion coupling deformation. Our theoretical analysis shows that the proposed model agrees with the Sopwith’s theory when the lead angle of the threaded connections is sufficiently small. For large lead angles, the torque generated by the axial load is nonnegligible. In addition, we perform a three-dimensional finite element analysis to demonstrate the validity of the proposed model. Finally, influence of geometrical and material parameters on load distribution is investigated. The finding of this work may provide a new insight into the anti-loosening design for threaded connections. © 2023, The Authors. All rights reserved.

关键词： Finite element method

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Demonstrating Autonomous 3D Path Planning on a Novel Scalable UGV-UAV Morphing Robot

Demonstrating Autonomous 3D Path Planning on a Novel Scalabl...

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

作者： Eric Sihite Filip Slezak Ioannis Mandralis Adarsh Salagame Milad Ramezani Arash Kalantari Alireza Ramezani Morteza Gharib The Department of Aerospace Engineering California Institute of Technology Pasadena USA Department of Electrical and Computer Engineering The Silicon Synapse Labs Northeastern University Boston USA The Robotics and Autonomous Systems DATA61 CSIRO Brisbane QLD Australia The Jet Propulsion Laboratory Pasadena USA

Some animals exhibit multi-modal locomotion capability to traverse a wide range of terrains and environments, such as amphibians that can swim and walk or birds that can fly and walk. This capability is extremely beneficial for expanding the animal's habitat range and they can choose the most energy efficient mode of locomotion in a given environment. The robotic biomimicry of this multi-modal locomotion capability can be very challenging but offer the same advantages. However, the expanded range of locomotion also increases the complexity of performing localization and path planning. In this work, we present our morphing multi-modal robot, which is capable of ground and aerial locomotion, and the implementation of readily available SLAM and path planning solutions to navigate a complex indoor environment.

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Learn to Coordinate: a Whole-Body Learning from Demonstration Framework for Differential Drive Mobile Manipulators

Learn to Coordinate: a Whole-Body Learning from Demonstratio...

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IEEE International Conference on systems, Man and Cybernetics

作者： Yuqiang Yang Darong Huang Chen Chen Chao Zeng Yanong He Chenguang Yang Automation Science and Engineering South China University of Technology Guangzhou China Application Innovate Laboratory Huawei Technologies Co. Ltd Shenzhen China Technical Aspects of Multimodal Systems Universitat Hamburg Hamburg Germany Bristol Robotics Laboratory University of the West of England Bristol UK

This paper proposes a whole-body learning from demonstration (LfD) framework that enables differential drive mobile manipulators to learn coordination working and disturbance rejection. First, an efficient kinesthetic teaching method is devised based on the weighted least-norm (WLN) inverse kinematics solution and an admittance controller, which facilitates human users to guide the mobile manipulator to perform tasks. Second, we propose a whole-body LfD framework through Gaussian Process, which endows the mobile manipulator's skill learning process with features of large-scale convergence, coordination working and disturbance rejection, after just a few human demonstrations. The proposed learning framework also allows for human-in-the-loop correction when the whole-body is conducting a task. Finally, the effectiveness of the proposed framework is verified via two simulations and a pick-and-place experiment. Supplementary video for this paper is available in github † † https://***/yuqiang-yang/SMC2023-Video.

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Recalibrating the KITTI Dataset Camera Setup for Improved Odometry Accuracy

Recalibrating the KITTI Dataset Camera Setup for Improved Od...

引用

European Conference on Mobile Robots (ECMR)

作者： Igor Cvišić Ivan Marković Ivan Petrović Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Croatia

Over the last decade, one of the most relevant public datasets for evaluating odometry accuracy is the KITTI dataset. Beside the quality and rich sensor setup, its success is also due to the online evaluation tool, which enables researchers to bench-mark and compare algorithms. The results are evaluated on the test subset solely, without any knowledge about the ground truth, yielding unbiased, overfit free and therefore relevant validation for robot localization based on cameras, 3D laser or combination of both. However, as any sensor setup, it requires prior calibration and rectified stereo images are provided, introducing dependence on the default calibration parameters. Given that, a natural question arises if a better set of calibration parameters can be found that would yield higher odometry accuracy. In this paper, we propose a new approach for one shot calibration of the KITTI dataset multiple camera setup. The approach yields better calibration parameters, both in the sense of lower calibration reprojection errors and lower visual odometry error. We conducted experiments where we show for three different odometry algorithms, namely SOFT2, ORB-SLAM2 and VISO2, that odometry accuracy is significantly improved with the pro-posed calibration parameters. Moreover, our odometry, SOFT2, in conjunction with the proposed calibration method achieved the highest accuracy on the official KITTI scoreboard with 0.53% translational and 0.0009 deg/m rotational error, outperforming even 3D laser-based methods.

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Development and Initial Evaluation of a Multi-Purpose Spraying Robot Prototype

Development and Initial Evaluation of a Multi-Purpose Sprayi...

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International Conference on Advanced robotics (ICAR)

作者： Christos Chrysoulakis John Fasoulas Michael Sfakiotakis Control Systems and Robotics Laboratory School of Engineering Hellenic Mediterranean University Heraklion Crete Greece

ISBN: (纸本)9781665436854

We present a versatile mobile robotic sprayer system, primarily aimed for pesticide application in greenhouses and for disinfection of indoor spaces. The prototype is built around a skid-steered wheeled platform driven by a pair of DC motors, and is equipped with an electronically controlled sprayer unit that is mounted on a pan/tilt robot arm for directing the spray. The robot can be operated either manually or in AGV mode, utilizing a magnetic guide sensor to track a predefined path, along with a depth camera and ultrasonic sensors for obstacle detection. The robot's control architecture employs a central Single Board Computer running ROS, with peripheral functions allocated to three 8-bit microcontrollers. Closed loop flow regulation schemes have been developed to provide precision variable application rate of the sprayed agents, combined with motion control strategies of the pan/tilt unit to implement rectangular coverage spraying. The robot's capabilities are demonstrated through a series of tests, involving laboratory experiments as well as application scenarios trials.

关键词： Robot control Robot vision systems Spraying Prototypes Robot sensing systems Regulation Mobile robots

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Organic Heterojunction Enabled Near 1% H2o2 Photoproduction with Ultrafast Charge Separation

SSRN

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SSRN 2024年

作者： Liu, Jingjing Hu, Qiushi Liu, Shang Lin, Meng Zhang, Xiaosa Xie, Ying Chen, Xihan Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems SUSTech Energy Institute for Carbon Neutrality Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Key Laboratory for Rare Earth Chemistry and Application of Liaoning Province College of Science Shenyang University of Chemical Technology Liaoning Shenyang China

Hydrogen peroxide (H2O2) finds wide application in disinfection, chemical production, and bleaching owing to its sustainability, stability, and ease of storage compared to hydrogen. However, the current industrial engineering practices for producing photocatalysts used in H2O2 generation face challenges such as low yields with organic catalysts and high costs associated with inorganic catalysts. In light of these challenges, we investigate a viable method utilizing a simple condensation reflux technique to synthesize pure organic heterojunction photocatalysts featuring a π-π stacked structure derived from graphitized carbon nitride (melem) and meso-tetrakis (4-carboxyphenyl) porphyrin. This approach enables efficient H2O2 production from water, seawater, and oxygen without the necessity of sacrificial agents. In this study, the synthesized catalyst demonstrates an impressive H2O2 production rate of 106 mmol/h.g.L under seawater conditions and 77.67 mmol/h.g.L under pure water conditions in ambient air. Following a reaction time of 4.5 hours, the H2O2 concentration reaches 0.88 wt% with a volume of 5.0 mL. Transient absorption spectroscopy validates the formation of a heterojunction between melem and porphyrin. Notably, this heterogeneous catalyst efficiently facilitates water oxidation and oxygen reduction while suppressing both H2O2 decomposition and subsequent photodecomposition reactions, thereby addressing the challenges associated with organic catalyst yield. © 2024, The Authors. All rights reserved.

关键词： Absorption spectroscopy

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Attitude observers for three-vehicle heterogeneous formations based on the Lagrange-d’Alembert principle

Attitude observers for three-vehicle heterogeneous formation...

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European Control Conference (ECC)

作者： Pedro Cruz Pedro Batista Amit Sanyal Institute for Systems and Robotics Laboratory for Robotics and Engineering Systems at Instituto Superior Técnico Universidade de Lisboa Portugal Syracuse University Syracuse NY United States

This paper considers the problem of attitude estimation in three-vehicle heterogeneous formations with no line of sight between two of the vehicles. Each vehicle measures different inertial reference vectors and directions to other vehicles. The relative direction between the two vehicles with no line of sight cannot be measured. Moreover, rate gyros measure the angular velocity of each vehicle. An attitude observer is designed based on the Lagrange-d’Alembert principle of variational mechanics, considering only kinematic models. This design is driven by the angular velocity measurement and a reconstructed attitude computed from the direction measurements. The attitude reconstruction follows a deterministic algorithm, which has a unique solution under appropriate assumptions. The attitude observer is locally exponentially stable and the estimation error is shown to converge to zero for almost all initial conditions. The discrete-time form of the observer is obtained for practical implementation. Lastly, numerical simulations validate the stability and convergence characteristics of the observer.

关键词： Europe Kinematics Observers Position measurement Numerical simulation Angular velocity Stability analysis

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