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Design of an Active Flexible Spine for Wall Climbing Robot Using Pneumatic Soft Actuators

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Journal of Bionic Engineering 2023年第2期20卷 530-542页

作者： Guangming Chen Tao Lin gabriel lodewijks Aihong Ji Lab of Locomotion Bioinspiration and Intelligent Robots College of Mechanical and Electrical EngineeringNanjing University of Aeronautics and Astronautics29 Yudao StreetNanjing210016China School of Engineering College of EngineeringScience&EnvironmentUniversity of NewcastleCallaghan CampusUniversity DriveCallaghanNSW2308Australia

Wall climbing robots can be used to undertake missions in many unstructured ***,current wall climbing robots have mobility difficulties such as in the turning or *** of the main reasons for the limitations is the poor flexibility of the *** robotic technology can actively enable structure deformation and stiffness varations,which provides a solution for the design of active flexible *** research utilizes pneumatic soft actuators to design a flexible spine with the abilities of actively bending and twisting by each *** bending and torsion moment equilibriums,respectively,from air pressure to material deformations,the bending and twisting models for a single actuator with respect to different pressure are *** theoretical models are verified by finite-element method simulations and experimental *** addition,the bending and twisiting motions of single joint and whole spine are analytically *** results show that the bionic spine can perform desired deformations in accordance with the applied pressure on specified *** variations of the stiffness are also numerically ***,the effectiveness of the bionic flexible spine for actively producing sequenced motions as biological spine is experimentally *** work demonstrated that the peneumatic spine is potential to improve the spine flexibility of wall climbing robot.

关键词： Active spine Bionic design Soft robot Gecko locomotion Finite-element modeling

Modeling particle sedimentation in viscous fluids with a coupled immersed boundary method and discrete element method

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Particuology 2017年第2期15卷 191-199页

作者： Sayed M. Derakhshani Dingena L. Schott gabriel lodewijks Faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Delft Netherlands Department of Mechanical Engineering Faculty of Engineering University of Isfahan Isfahan Iran

Numerical techniques have increasingly been used to model fluid–particle two-phase flows. Coupling the immersed boundary method （IBM） and discrete element method （DEM） is one promising approach for modeling particulate flows. In this study, IBM was coupled with DEM to improve the reliability and accuracy of IBM for determining the positions of particles during the sedimentation process within viscous fluids. The required ratio of the particle diameter to the grid size （D/dx） was determined by comparing the simulation results with the analytical solution and experimental data. A dynamic mesh refinement model was utilised in the IBM model to refine the computational fluid dynamics grid near the particles. In addition, an optimum coupling interval between the IBM and DEM models was determined based on the experimental results of a single particle sedimentation within silicon oil at a Reynolds number of 1.5. The experimental results and the analytical solution were then utilised to validate the IBM–DEM model at Reynolds numbers of 4.1, 11.6, and 31.9. Finally, the validated model was utilised to investigate the sedimentation process for more than one particle by modeling the drafting-kissing-tumbling process and the Boycott phenomenon. Benchmark tests showed that the IBM–DEM technique preserves the advantages of DEM for tracking a group of particles, while the IBM provides a reliable and accurate approach for modeling the particle–fluid interaction.

关键词： Immersed boundary method Discrete element method Particle sedimentation Boycott phenomenon

Scheduling Storage Process of Shuttle-Based Storage and Retrieval Systems Based on Reinforcement Learning

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Complex System Modeling and Simulation 2021年第2期1卷 131-144页

作者： Lei Luo Ning Zhao gabriel lodewijks School of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China School of Aviation University of New South WalesSydney 2052Australia.

The Shuttle-Based Storage and Retrieval System(SBS/RS)has been widely studied because it is currently the most efficient automated warehousing *** of the related existing studies are focused on the prediction and improvement of the efficiency of such a system at the design ***,the control of existing SBS/RSs has been rarely *** existing SBS/RSs,some empirical rules,such as storing loads column by column,are used to control or schedule the storage *** question is whether or not the control of the storage process in an existing system can be improved further by using a different *** storage process is controlled to minimize the makespan of storing a series of loads into *** storage rules are easy to control,but they do not reach the minimum *** this study,the performance of a control system that uses reinforcement learning to schedule the storage process of an SBS/RS with fixed configurations is ***,a reinforcement learning algorithm called the actor-critic algorithm is *** algorithm is made up of two neural networks and is effective in making decisions and updating *** can also reduce the makespan relative to the existing empirical rules used to improve system *** results show that in an SBS/RS comprising six columns and six tiers and featuring a storage capacity of 72 loads,the actor-critic algorithm can reduce the makespan by 6.67%relative to the column-by-column storage *** proposed algorithm also reduces the makespan by more than 30%when the number of loads being stored is in the range of 7–45,which is equal to 9.7%–62.5%of the systems’storage capacity.

关键词： Shuttle-Based Storage and Retrieval System(SBS/RS) reinforcement learning scheduling

Trajectory Predictions with Details in a Robotic Twin-Crane System

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Complex System Modeling and Simulation 2022年第1期2卷 1-17页

作者： Ning Zhao gabriel lodewijks Zhuorui Fu Yu Sun Yue Sun School of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China School of Aviation University of New South WalesSydney 2052Australia

Nowadays,more automated or robotic twin-crane systems(RTCSs)are employed in ports and factories to improve material handling *** a twin-crane system,cranes must travel with a minimum safety distance between them to prevent *** crane trajectory prediction is critical to interference handling and crane *** trajectory predictions lack accuracy because many details are *** enhance accuracy and lessen the trajectory prediction time,a trajectory prediction approach with details(crane acceleration/deceleration,different crane velocities when loading/unloading,and trolley movement)is proposed in this *** on different details and their combinations are conducted on a container terminal case *** to the simulation results,the accuracy of the trajectory prediction can be improved by 20%.The proposed trajectory prediction approach is helpful for building a digital twin of RTCSs and enhancing crane scheduling.

关键词： twin-crane systems trajectory prediction acceleration velocity interference

Data-driven Methods to Predict the Burst Strength of Corroded Line Pipelines Subjected to Internal Pressure

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Journal of Marine Science and Application 2022年第2期21卷 115-132页

作者： Jie Cai Xiaoli Jiang Yazhou Yang gabriel lodewijks Minchang Wang Department of Technology and Innovation University of Southern DenmarkCampusvej 55-5230 OdenseDenmark Department of Maritime and Transport Technology Delft University of Technology2628 CD Delftthe Netherlands College of Advanced Interdisciplinary Studies National University of Defence TechnologyChangsha410073P.R.China School of Engineering University of NewcastleNSW2308Australia Hangzhou Silan Microelectronics Co.Ltd. HangzhouZhejiang310008P.R.China.

A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines,especially those served for a long ***-element method and empirical formulas are thereby used for the strength prediction of such pipes with ***,it is time-consuming for finite-element method and there is a limited application range by using empirical *** order to improve the prediction of strength,this paper investigates the burst pressure of line pipelines with a single corrosion defect subjected to internal pressure based on data-driven *** supervised ML(machine learning)algorithms,including the ANN(artificial neural network),the SVM(support vector machine)and the LR(linear regression),are deployed to train models based on experimental *** analysis is first conducted to determine proper pipe features for *** tuning to control the learning process is then performed to fit the best strength models for corroded *** all the proposed data-driven models,the ANN model with three neural layers has the highest training accuracy,but also presents the largest *** SVM model provides both high training accuracy and high validation *** LR model has the best performance in terms of generalization *** models can be served as surrogate models by transfer learning with new coming data in future research,facilitating a sustainable and intelligent decision-making of corroded pipelines.

关键词： Pipelines Corrosion Burst strength Internal pressure Data-driven method Machine learning