Article
Computer Science, Artificial Intelligence
Pablo Lopez-Osorio, Alberto Patino-Saucedo, Juan P. Dominguez-Morales, Horacio Rostro-Gonzalez, Fernando Perez-Pena
Summary: In recent years, locomotion mechanisms exhibited by vertebrate animals have served as an inspiration for enhancing the performance of robotic systems. This study aims to replicate the adaptability of locomotion seen in vertebrates through a sCPG model. The sCPG generates different rhythmic patterns driven by an external stimulus, allowing the locomotion of a robotic platform to be adapted to the terrain using any sensor as input.
Article
Computer Science, Artificial Intelligence
Mingliang Xu, Fuhai Chen, Lu Li, Chen Shen, Pei Lv, Bing Zhou, Rongrong Ji
Summary: This paper presents a biology-inspired framework for predicting facial aesthetics, which extracts discriminative and interpretable features using eye tracker technology and convolutional neural networks. Experimental results demonstrate the superiority of the proposed scheme in facial aesthetic prediction.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2021)
Article
Engineering, Manufacturing
Laszlo Monostori, Balazs Cs. Csaji, Peter Egri, Krisztian B. Kis, Jozsef Vancza, Jelena Ochs, Sven Jung, Niels Koenig, Simon Pieske, Stephan Wein, Robert Schmitt, Christian Brecher
Summary: Stem cell therapy has great potential in treating chronic and life-threatening diseases, and this potential can be maximized through efficient automation of stem cell production. However, working with living material poses challenges to automation that can be overcome with biologically inspired control algorithms.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2021)
Article
Green & Sustainable Science & Technology
Jiwen Guan, Yanzhao Su, Ling Su, C. B. Sivaparthipan, BalaAnand Muthu
Summary: The biologically inspired algorithm is crucial in industrial robot control, with the proposed Bio-inspired Intelligent Industrial Robot Control System (BIIRCS) utilizing Deep Learning methods to achieve high performance in tasks such as object recognition and action planning. The study demonstrates the potential of intelligent systems created using Deep Learning algorithms and industrial robotics, showing improved performance compared to existing approaches.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2021)
Article
Multidisciplinary Sciences
Yupeng Chen, Zhongpeng Zhu, Martin Steinhart, Stanislav N. Gorb
Summary: This article discusses the concept of bio-inspired wet adhesion, reviews associated challenges, and discusses future directions. It also proposes a model for wet adhesion management.
Article
Mechanics
Nirmal J. J. Nair, Andres Goza
Summary: A hybrid active-passive flow control strategy is proposed to improve aerodynamic performance by actively varying the hinge stiffness of a bio-inspired flap. This strategy could be implemented using variable-stiffness actuators and has shown significant lift improvements compared to the fixed-stiffness case. A reinforcement-learning-trained closed-loop feedback controller is used to vary the stiffness, and a physics-based penalty and a long-short-term training strategy are introduced for fast training of the hybrid controller. The lift improvements achieved are attributed to large-amplitude flap oscillations and their interaction with the flow as the stiffness varies over four orders of magnitude.
JOURNAL OF FLUID MECHANICS
(2023)
Article
Engineering, Manufacturing
Laszlo Monostori, Balazs Cs Csaji, Peter Egri, Krisztian B. Kis, Jozsef Vancza, Jelena Ochs, Sven Jung, Niels Koenig, Simon Pieske, Stephan Wein, Robert Schmitt, Christian Brecher
Summary: The potential for treating chronic and life-threatening diseases with stem cell therapies can be greatly realized through efficient automation of stem cell production. However, working with living materials poses significant challenges to automation. Recent developments in production platforms aim to improve reproducibility, quality, throughput, and cost-effectiveness of the process on a global scale.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Multidisciplinary
Alejandro G. Calvet, Mukul Dave, Jennifer A. Franck
Summary: An unsupervised machine learning strategy has been developed to cluster vortex wakes of bio-inspired propulsors into groups with similar performance metrics. Through simulations of 121 unique kinematics, it was found that the Strouhal number has the strongest influence on thrust coefficient, while the relative angle of attack has the most significant impact on propulsive efficiency. This automated clustering has the potential to identify complex vorticity patterns in wakes and modes of propulsion that may not be easily discerned using traditional classification methods.
BIOINSPIRATION & BIOMIMETICS
(2021)
Article
Biochemical Research Methods
Tao Zeng, Bernard Andes Hess, Fan Zhang, Ruibo Wu
Summary: A bio-inspired strategy named TeroGen is developed to mimic the two key biosynthetic stages of terpenoid natural products using physically based simulations and deep learning models. It can predict and estimate the synthetic accessibility and chemical interpretation of tens of thousands of sesterterpenoids, thereby expanding the chemical space of terpenoids.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Computer Science, Information Systems
Thomas Chaffre, Paulo E. Santos, Gilles Le Chenadec, Estelle Chauveau, Karl Sammut, Benoit Clement
Summary: This study proposes a novel Biologically-Inspired Experience Replay method (BIER) to overcome the data inefficiency and performance degradation issues of deep reinforcement learning in Unmanned Underwater Vehicle (UUV) manoeuvring tasks. The BIER method is evaluated in simulated scenarios, and the results show that it achieves optimal performance faster than standard Experience Replay methods as complexity increases.
Article
Neurosciences
Yuxuan Zhao, Yi Zeng
Summary: With the advancement of artificial intelligence and robotics, robots are becoming a part of human daily life. Current human-robot interaction technologies lack flexibility in adapting to user habits. A brain-inspired intention prediction model based on reinforcement learning allows robots to perform actions according to the user's intention, enhancing user satisfaction.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Automation & Control Systems
Yuan Yuan, Hailong Ning, Xiaoqiang Lu
Summary: Visual attention prediction is a significant issue in computer vision, and most existing methods are based on deep learning. This article introduces a novel method that combines low-level contrast and high-level semantic features via bio-inspired representation learning to generate the visual attention map. The proposed method consists of feature extraction, bio-inspired representation learning, and visual attention map generation.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Engineering, Mechanical
Sam Noble, V. S. Sooraj
Summary: The present article discusses the development of humanoid robots with a human-like range of motion for the spine and torso. The construction of the humanoid spine and ribs is presented, considering the spine as a hyper redundant mechanism with attached ribs in the thoracic region. Biomechanical aspects of the spine are thoroughly discussed to demonstrate the joints and motion characteristics. A three-segment hyper redundant mechanism is used to imitate the human mimetic spine, considering flexion-extension and lateral bending motions.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
(2023)
Article
Robotics
Hideyuki Ichiwara, Hiroshi Ito, Kenjiro Yamamoto, Hiroki Mori, Tetsuya Ogata
Summary: The researchers developed a modality attention motion generation model based on multi-modality prediction. This model provides interpretability regarding modality usage and demonstrates robustness against disturbances. It effectively integrates multiple modalities through a hierarchical model and gating mechanisms, and has been validated in the task of inserting furniture components.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Hengde Zhu, Wei Wang, Irek Ulidowski, Qinghua Zhou, Shuihua Wang, Huafeng Chen, Yudong Zhang
Summary: This paper proposes an evolutionary synthesis mechanism to automatically evolve DenseNet for medical image classification, inspired by biological evolution. The mechanism generates sparser offspring in each generation and addresses the limitation of ensemble learning. The proposed model outperforms state-of-the-art methods on medical image datasets.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Automation & Control Systems
Christian Ott, Bernd Henze, Georg Hettich, Tim Niklas Seyde, Maximo A. Roa, Vittorio Lippi, Thomas Mergner
IEEE ROBOTICS & AUTOMATION MAGAZINE
(2016)
Article
Computer Science, Artificial Intelligence
Vittorio Lippi, Thomas Mergner
FRONTIERS IN NEUROROBOTICS
(2017)
Article
Computer Science, Artificial Intelligence
Alexei V. Alexandrov, Vittorio Lippi, Thomas Mergner, Alexander A. Frolov, Georg Hettich, Dusan Husek
FRONTIERS IN NEUROROBOTICS
(2017)
Article
Computer Science, Artificial Intelligence
Thomas Mergner, Vittorio Lippi
FRONTIERS IN NEUROROBOTICS
(2018)
Article
Neurosciences
V Lippi, L. Asslaender, E. Akcay, T. Mergner
NEUROSCIENCE LETTERS
(2020)
Proceedings Paper
Engineering, Electrical & Electronic
Vittorio Lippi, Alessandro Filippeschi, Cristian Camardella
Summary: The EXOSMOOTH project focuses on benchmarking the performance of lower limb exoskeletons and aims to investigate the effectiveness of a novel control strategy for smooth assistance and the role of the actuation at the ankle joint in assisted walking.
PROCEEDINGS OF THE 2022 17TH ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION (HRI '22)
(2022)
Proceedings Paper
Automation & Control Systems
Vittorio Lippi, Giacomo Ceccarelli
ICINCO: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL 1
(2019)
Proceedings Paper
Automation & Control Systems
Vittorio Lippi, Raphael Deimel
ICINCO: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL 1
(2019)
Proceedings Paper
Engineering, Biomedical
Vittorio Lippi, Fabio Molinari, Thomas Seel
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2019)
Proceedings Paper
Automation & Control Systems
Teresa Zielinska, Zhingiang Gao, Magdalena Zurawska, Qinling Zheng, Thomas Mergner, Vittorio Lippi
2017 11TH INTERNATIONAL WORKSHOP ON ROBOT MOTION AND CONTROL (ROMOCO)
(2017)
Proceedings Paper
Neurosciences
T. Mergner, V. Lippi
CONVERGING CLINICAL AND ENGINEERING RESEARCH ON NEUROREHABILITATION II, VOLS 1 AND 2
(2017)
Proceedings Paper
Engineering, Mechanical
Vittorio Lippi, Thomas Mergner, Maksymilian Szumowski, Magdalena Sylwia Zurawska, Teresa Zielinska
ROMANSY 21 - ROBOT DESIGN, DYNAMICS AND CONTROL
(2016)
Proceedings Paper
Automation & Control Systems
M. Zebenay, V. Lippi, T. Mergener
ICIMCO 2015 PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL. 2
(2015)
Proceedings Paper
Neurosciences
Georg Hettich, Vittorio Lippi, Thomas Mergner
NEUROTECHNOLOGY, ELECTRONICS, AND INFORMATICS: REVISED SELECTED PAPERS FROM NEUROTECHNIX 2013
(2015)
Article
Automation & Control Systems
Runwei Guan, Shanliang Yao, Lulu Liu, Xiaohui Zhu, Ka Lok Man, Yong Yue, Jeremy Smith, Eng Gee, Yutao Yue
Summary: With the development of Unmanned Surface Vehicles (USVs), the perception of inland waterways has become significant. Traditional RGB cameras cannot work effectively in adverse weather and at night, which has led to the emergence of 4D millimeter-wave radar as a new perception sensor. However, the radar suffers from water-surface clutter and irregular shape of point cloud. To address these issues, this paper proposes a high-performance panoptic perception model called Mask-VRDet, which fuses features of vision and radar using graph neural network.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Adrien Le Reun, Kevin Subrin, Anthony Dubois, Sebastien Garnier
Summary: This study aims to evaluate the quality and health of aerospace parts using a high-dimensional robotic cell. By utilizing X-ray Computed Tomography devices, the interior of the parts can be reconstructed and anomalies can be detected. A methodology is proposed to assess both the raw process capability and the improved process capability, with three strategies developed to improve the robot behavior model and calibration.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Weiming Ba, Jung-Che Chang, Jing Liu, Xi Wang, Xin Dong, Dragos Axinte
Summary: This paper proposes a hybrid scheme for kinematic control of continuum robots, which avoids errors through tension supervision and accurate piecewise linear approximation. The effectiveness of the controller is verified on different continuum robotic systems.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Gabriele Abbate, Alessandro Giusti, Viktor Schmuck, Oya Celiktutan, Antonio Paolillo
Summary: In this study, a learning-based approach is proposed to predict the probability of human users interacting with a robot before the interaction begins. By considering the pose and motion of the user, the approach labels the robot's encounters with humans in a self-supervised manner. The method is validated and deployed in various scenarios, achieving high accuracy in predicting user intentions to interact with the robot.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Tiago Cortinhal, Eren Erdal Aksoy
Summary: This work presents a new depth-and semantics-aware conditional generative model, named TITAN-Next, for cross-domain image-to-image translation between LiDAR and camera sensors. The model is able to translate raw LiDAR point clouds to RGB-D camera images by solely relying on semantic scene segments, and it has practical applications in fields like autonomous vehicles.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Marios Krestenitis, Emmanuel K. Raptis, Athanasios Ch. Kapoutsis, Konstantinos Ioannidis, Elias B. Kosmatopoulos, Stefanos Vrochidis
Summary: This paper addresses the issue of informative path planning for a UAV used in precision agriculture. By using a non-uniform scanning approach, the time spent in areas with minimal value is reduced, while maintaining high precision in information-dense regions. A novel active sensing and deep learning-based coverage path planning approach is proposed, which adjusts the UAV's speed based on the quantity and confidence level of identified plant classes.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Shota Kokubu, Pablo E. Tortos Vinocour, Wenwei Yu
Summary: In this study, a new modular soft actuator was proposed to improve the support performance of soft rehabilitation gloves (SRGs). Objective evaluations and clinical tests were conducted to demonstrate the effectiveness and functionality of the proposed actuator and SRG.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Jinliang Zhu, Yuanxi Sun, Jie Xiong, Yiyang Liu, Jia Zheng, Long Bai
Summary: This paper proposes an active prosthetic knee joint with a variable stiffness parallel elastic actuation mechanism. Numerical verifications and practical experiments demonstrate that the mechanism can reduce torque and power, thus reducing energy consumption and improving the endurance of the prosthetic knee joint.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Yong You, Jingtao Wu, Yunlong Meng, Dongye Sun, Datong Qin
Summary: A new power-cycling variable transmission (PCVT) is proposed and applied to construction vehicles to improve transmission efficiency. A shift correction strategy is developed based on identifying the changes in construction vehicles' mass and gradient. Simulation results show that the proposed method can correct shift points, improve operation efficiency, and ensure a safer operation process.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Shaorui Liu, Wei Tian, Jianxin Shen, Bo Li, Pengcheng Li
Summary: This paper proposes a two-objective optimization technique for multi-robot systems, addressing the issue of balancing productivity and machining performance in high-quality machining tasks.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Pengchao Ding, Faben Zhu, Hongbiao Zhu, Gongcheng Wang, Hua Bai, Han Wang, Dongmei Wu, Zhijiang Du, Weidong Wang
Summary: We propose an autonomous approaching scheme for mobile robot traversing obstacle stairwells, which overcomes the restricted field of vision caused by obstacles. The scheme includes stair localization, structural parameter estimation, and optimization of the approaching process.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Pedro Azevedo, Vitor Santos
Summary: Accurate detection and tracking of vulnerable road users and traffic objects are vital tasks for autonomous driving and driving assistance systems. This paper proposes a solution for object detection and tracking in an autonomous driving scenario, comparing different object detectors and exploring the deployment on edge devices. The effectiveness of DeepStream technology and different object trackers is assessed using the KITTI tracking dataset.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Benjamin Beiter, Divya Srinivasan, Alexander Leonessa
Summary: Powered exoskeletons can significantly reduce physical workload and have great potential impact on future labor practices. To truly assist users in achieving task goals, a shared autonomy control framework is proposed to separate the control objectives of the human and exoskeleton. Positive Power control is introduced for the human-based controller, while 'acceptance' is used as a measure of matching the exoskeleton's control objective to the human's. Both control objectives are implemented in an optimization-based Whole-Body-Control structure. The results verify the effectiveness of the control framework and its potential for improving cooperative control for powered exoskeletons.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)