Article
Chemistry, Physical
Changliu He, Tingting Yang, Jiahao Fang, Xiaobo Pu, Kedong Shang, Guo Tian, Xulei Lu, Jianbing Wu, Weiqing Yang, Linmao Qian
Summary: In this study, a tensegrity-inspired design based on the biological musculoskeletal system is proposed to address the challenges faced by existing vibration energy harvesters and sensors in random and extreme vibration environments. The design incorporates a ten-segrity structure with a triboelectric nanogenerator, allowing for vibration transduction in a broadband frequency range (0-200 Hz) and excellent impact resistance under high g acceleration impacts (105 g level). The device remains functional after experiencing some structural damage and demonstrates more reliable performance than commercial sensors when subjected to severe hail impacts. The tensegrity structure design holds great potential for high-performance vibration monitoring in industrial settings.
Article
Mechanics
Claudio Boni, Gianni Royer-Carfagni
Summary: This paragraph discusses the characteristics of flexural tensegrity structures and the effects of nonlinear behavior on bending stiffness and structural damping. It also analyzes the vibration properties under harmonic excitation and the phenomena of parametric resonance.
INTERNATIONAL JOURNAL OF NON-LINEAR MECHANICS
(2021)
Article
Mathematics, Interdisciplinary Applications
Yipeng Ge, Zigang He, Shaofan Li, Liang Zhang, Litao Shi
Summary: Clustered tensegrity structures integrated with continuous cables are lightweight, foldable, and deployable, serving as flexible manipulators or soft robots. This study proposes a comprehensive data-driven computational approach to quantify the uncertainty and control the deformation of the structures. The approach utilizes machine learning methods and optimization techniques, demonstrating its validity and potential application in a clustered tensegrity beam subjected to actuation.
COMPUTATIONAL MECHANICS
(2023)
Article
Engineering, Mechanical
Yaqiong Tang, Tuanjie Li, Qing Lv, Xiaokai Wang
Summary: Tensegrity structure is attractive due to its advantages of self-equilibrium and lightweight, but its shape and dynamic behavior are susceptible to environmental change. This study develops a self-vibration-control tensegrity structure and validates its effectiveness through numerical simulations and experiments.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Levi H. Manring, John F. Schultze, Sandra J. Zimmerman, Brian P. Mann
Summary: This paper describes modifications to the Matrix Power Control Algorithm (MPCA) to improve convergence for Random Vibration Control (RVC) testing. Multiple-Input Multiple-Output (MIMO) implementations of MPCA were presented and validated through simulation and experiment. The results demonstrate that tuning control parameters and applying an optimized moving-average can enhance the performance and convergence of MPCA.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Interdisciplinary Applications
Kaan Yildiz, George A. Lesieutre
Summary: This paper addresses the sizing and prestress optimization of Class-2 tensegrity booms using a particle swarm optimization approach, discussing both single-objective and multi-objective optimization problems. The results demonstrate the potential of tensegrity structures for implementation as space structures and the robustness of the particle swarm optimization algorithm, even for multi-objective optimization problems.
ENGINEERING WITH COMPUTERS
(2022)
Article
Engineering, Multidisciplinary
Muhao Chen, Xiaolong Bai, Robert E. Skelton
Summary: This paper presents a minimal mass design approach for clustered tensegrity structures (CTS). The paper introduces connectivity and clustering definitions, derives the nonlinear statics equation of CTS, presents the equations for the force density and force vectors, formulates the mass and gravity of hollow bars and strings subject to buckling and yielding conditions, and proposes a nonlinear optimization algorithm to compute the minimal mass of any CTS. The paper also demonstrates the relationship between CTS and traditional tensegrity structure (TTS) and provides numerical examples to validate the CTS minimal mass design approach.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Mechanics
Sichen Yuan, Weidong Zhu
Summary: A new method, called the Cartesian spatial discretization method, is developed for nonlinear dynamic modeling and vibration analysis of tensegrity structures. This method successfully incorporates member internal displacements in dynamic modeling of a tensegrity structure by defining positions of structural members as a summation of internal terms and boundary-induced terms in a global Cartesian coordinate system. The results show that the proposed method is accurate in predicting dynamic responses of tensegrity structures, especially for vibration analysis in the high-frequency domain.
INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES
(2023)
Article
Engineering, Civil
Pei Liu, Shuqiang Huang, Mingming Song, Weiguo Yang
Summary: A Bayesian model updating method utilizing ambient vibration data to improve efficiency and avoid local optimums is proposed. The method employs Bayesian fast Fourier transform to extract modal parameters for weighting factors, and uses a subset simulation optimization algorithm to find global optimal solutions. Validation on numerical and real-world structures shows improved accuracy in parameter estimations, demonstrating the effectiveness of the proposed method.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2021)
Article
Computer Science, Interdisciplinary Applications
James K. Roth, Timothy J. McCarthy
Summary: Tensegrity structures offer new possibilities in structural engineering by efficiently distributing compressive loads through optimized prestressing. Over-tensioning can reduce overall resistance to compressive loading, while genetic algorithms can be used to optimize pretension loads for increased load capacity.
COMPUTERS & STRUCTURES
(2021)
Article
Mechanics
Haoran Zou, Lei Wu, Wenhao Li, Fei Han, Zichen Deng
Summary: In this study, a dynamics model based on rotating Bernoulli-Euler beam is developed to investigate the space deployment dynamics of tensegrity modules under thermal effects. The dynamic stiffness method is used to obtain accurate numerical solutions for the system's modal characteristics and dynamic response. Parametric analysis reveals the dynamic evolution of tensegrity module space deployment under thermal effects.
INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES
(2023)
Article
Mechanics
Jingyao Zhang, Makoto Ohsaki, Julian J. Rimoli, Kosuke Kogiso
Summary: Tensegrity structures are optimized for maximal energy absorption capability by controlling the force density of bars, adjusting the cross-sectional areas of cables and bars, and varying the level of prestresses. Buckling of bars is utilized to significantly increase energy absorption, with the structure's energy absorption scaling cubically when its sizes and prestress level are uniformly scaled.
COMPOSITE STRUCTURES
(2021)
Article
Multidisciplinary Sciences
Keren J. Kanarik, Wojciech T. Osowiecki, Yu (Joe) Lu, Dipongkar Talukder, Niklas Roschewsky, Sae Na Park, Mattan Kamon, David M. Fried, Richard A. Gottscho
Summary: One of the bottlenecks in semiconductor chip development is the increasing cost of developing chemical plasma processes. This study investigates how Bayesian optimization algorithms can decrease the cost of developing complex semiconductor chip processes. The results show that algorithms are more cost-efficient near the target tolerances, and a strategy combining human designers and algorithms can significantly reduce costs compared to relying on human designers alone.
Article
Engineering, Chemical
Nung Siong Lai, Yi Shen Tew, Xialin Zhong, Jun Yin, Jiali Li, Binhang Yan, Xiaonan Wang
Summary: This study proposes an innovative AI workflow that combines large-language models, Bayesian optimization, and active learning loop to expedite and enhance catalyst optimization. By effectively translating knowledge from literature into actionable parameters, the workflow simplifies catalyst development process and offers a fast, resource-efficient, and high-precision alternative to conventional methods.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Chemistry, Multidisciplinary
Rubaiyat Mohammad Khondaker, Stephen Gow, Samantha Kanza, Jeremy G. Frey, Mahesan Niranjan
Summary: The problems of chemical reaction optimization and reaction scope search can be solved by mathematical methods, such as Bayesian optimization. The Experimental Design for Bayesian optimization (EDBO) optimizer is a recent development in this area, which has shown promising results in various experiments and data sets.
JOURNAL OF CHEMINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J. Bentley, Samuel Bernard, Guillaume Beslon, David M. Bryson, Nick Cheney, Patryk Chrabaszcz, Antoine Cully, Stephane Doncieux, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frenoy, Christian Gagn, Leni Le Goff, Laura M. Grabowski, Babak Hodjat, Frank Hutter, Laurent Keller, Carole Knibbe, Peter Krcah, Richard E. Lenski, Hod Lipson, Robert MacCurdy, Carlos Maestre, Risto Miikkulainen, Sara Mitri, David E. Moriarty, Jean-Baptiste Mouret, Anh Nguyen, Charles Ofria, Marc Parizeau, David Parsons, Robert T. Pennock, William F. Punch, Thomas S. Ray, Marc Schoenauer, Eric Shulte, Karl Sims, Kenneth O. Stanley, Francois Taddei, Danesh Tarapore, Simon Thibault, Richard Watson, Westley Weimer, Jason Yosinski
Article
Robotics
Konstantinos Chatzilygeroudis, Vassilis Vassiliades, Freek Stulp, Sylvain Calinon, Jean-Baptiste Mouret
IEEE TRANSACTIONS ON ROBOTICS
(2020)
Article
Robotics
Lorenzo Vianello, Jean-Baptiste Mouret, Eloise Dalin, Alexis Aubry, Serena Ivaldi
Summary: This study introduces a method to predict human postures in a collaborative scenario with a robot trajectory using probabilistic terms. It considers individual differences and preferences in movement execution, and evaluates the impact from an ergonomics standpoint.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Editorial Material
Robotics
Keyan Ghazi-Zahedi, John Rieffel, Syn Schmitt, Helmut Hauser
FRONTIERS IN ROBOTICS AND AI
(2021)
Article
Robotics
Waldez Gomes, Pauline Maurice, Eloise Dalin, Jean-Baptiste Mouret, Serena Ivaldi
Summary: Work-related musculoskeletal disorders are a major health issue caused by poor postures. Optimizing for a single ergonomics score may lead to decreased scores in other areas. To address this, we propose a multi-objective optimization approach that can simultaneously optimize multiple scores to find better body postures.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Timothee Anne, Eloise Dalin, Ivan Bergonzani, Serena Ivaldi, Jean-Baptiste Mouret
Summary: This letter introduces a method called D-Reflex, which uses a neural network to choose the contact position of a humanoid robot when leaning on a wall, in order to achieve a stable posture. The experimental results show that D-Reflex can effectively prevent falls and can be applied to real robots.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Jacques Zhong, Vincent Weistroffer, Jean-Baptiste Mouret, Francis Colas, Pauline Maurice
Summary: This letter presents a framework for efficiently evaluating the suitability of a workstation over a large population of workers using physics-based simulation. The framework simulates different morphologies and adapts behaviors through optimization algorithms, aiming to help ergonomists improve workstation designs.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Proceedings Paper
Automation & Control Systems
Evelyn D'Elia, Jean-Baptiste Mouret, Jens Kober, Serena Ivaldi
Summary: This study proposes a learning method that combines multi-objective optimization algorithm and Bayesian optimization algorithm for designing controllers for complex robots. The method outputs a diverse Pareto set of well-functioning controller weights and gains by using various training trajectories, which also perform well on real robots.
2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
(2022)
Proceedings Paper
Automation & Control Systems
Anji Ma, Yoann Fleytoux, Jean-Baptiste Mouret, Serena Ivaldi
Summary: In this paper, VP-GO, a light stochastic action-conditioned visual prediction model, is proposed for robotic grasping of unknown soft objects. By decomposing semantic actions into elementary movements, compatibility with existing models and datasets is ensured. A new open dataset called PandaGrasp is also provided for visual prediction of object grasping.
2022 INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM 2022)
(2022)
Proceedings Paper
Automation & Control Systems
Glenn Maguire, Nicholas Ketz, Praveen K. Pilly, Jean-Baptiste Mouret
Summary: Reinforcement learning agents often struggle to perform well in novel situations, but this paper presents an online, data-driven emergency response method called A-EMS that aims to provide autonomous agents the ability to react to unknown scenarios. By selecting actions that minimize the rate of increase of reconstruction error, the proposed approach sequentially designs a customized response to unforeseen situations and achieves this optimization in an online, data-efficient manner using a modified Bayesian optimization procedure.
TOWARDS AUTONOMOUS ROBOTIC SYSTEMS, TAROS 2022
(2022)
Article
Robotics
Pierre Laclau, Vladislav Tempez, Franck Ruffier, Enrico Natalizio, Jean-Baptiste Mouret
Summary: The paper introduces a distributed algorithm called U-Chain for coordinating a chain of flying robots between an exploration drone and an operator. The algorithm uses measurement of signal quality and estimates of ground speed based on an optic flow sensor, leveraging distributed policy and Kalman filter for reliable signal quality estimates. Evaluation was done formally and in simulation with real miniature quadrotors and a base station.
FRONTIERS IN ROBOTICS AND AI
(2021)
Proceedings Paper
Automation & Control Systems
Rituraj Kaushik, Timothee Anne, Jean-Baptiste Mouret
2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Niels Justesen, Miguel Gonzalez-Duque, Daniel Cabarcas, Jean-Baptiste Mouret, Sebastian Risi
2020 IEEE CONFERENCE ON GAMES (IEEE COG 2020)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Jean-Baptiste Mouret, Glenn Maguire
GECCO'20: PROCEEDINGS OF THE 2020 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret
GECCO'20: PROCEEDINGS OF THE 2020 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE
(2020)