Article
Robotics
Kim-Ngoc-Khanh Nguyen, Yuta Kojio, Shintaro Noda, Fumihito Sugai, Kunio Kojima, Yohei Kakiuchi, Kei Okada, Masayuki Inaba
Summary: The study proposed a method to optimize joint trajectories considering time and robot orientation variables, using evolutionary search in a dynamic simulator to achieve fall recovery for biped robots. By combining fall recovery feature with fall detector and fall-damage-reduction motion, a biped robot platform was successfully developed.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Engineering, Mechanical
Linqi Ye, Xueqian Wang, Houde Liu, Bin Liang, Bo Yuan
Summary: This paper investigates how to walk faster for two simple 2D walking models. Open-loop analysis is conducted and the concept of acceleration factor is proposed. It is found that the acceleration factor has a fixed correlation with the velocity transition trend, independent of the step length. Based on this, walking controllers are designed and closed-loop simulations are performed to achieve faster walking speeds.
NONLINEAR DYNAMICS
(2023)
Article
Robotics
Francesco Roscia, Michele Focchi, Andrea Del Prete, Darwin G. Caldwell, Claudio Semini
Summary: In this letter, the authors propose an optimization-based reactive Landing Controller for torque-controlled quadruped robots in free-fall. The method utilizes an estimate of the Center of Mass horizontal velocity and a Variable Height Springy Inverted Pendulum model to continuously recompute the feet position for a successful landing in all directions.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Chuanyu Yang, Can Pu, Guiyang Xin, Jie Zhang, Zhibin Li
Summary: Falling is inevitable for legged robots in challenging real-world scenarios. We propose a deep reinforcement learning approach to learn generalized feedback-control policies for fall recovery that are robust to external disturbances. Our proposed pipeline is applicable to different robot models and can be implemented on real robots.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Automation & Control Systems
Arthicha Srisuchinnawong, Kitti Phongaksorn, Wasuthorn Ausrivong, Poramate Manoonpong
Summary: This work presents a novel adaptive bipedal robot and neural multimodal locomotion control for semiautonomous robotic out-pipe inspection. The robot can balance on curved surfaces, climb pipes with limited energy, overcome obstacles, and perform stable transitions between pipe segments. It achieves 100% successful locomotion on horizontal and vertical smooth pipes, with a speed of 10.24 cm/s and a cost of transport of 26.3 J/kgm, showing over 200% improvement in speed and energy efficiency compared to existing legged inspection robots.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Automation & Control Systems
Hao Sun, Junjie Yang, Yinghao Jia, Changhong Wang
Summary: This article proposes a free gait generation algorithm that only takes the robot state as input. By introducing the feasible impulse polytope, which takes into account both linear and angular momentum impulses acting on the body, a leg capability metric related to the effect of take-off and touch-down on the body motion is formulated. Gait sequence, take-off timing, and touch-down location can be automatically adjusted online based on a metric threshold.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Automation & Control Systems
Daojin Yao, Lin Yang, Xiaohui Xiao, MengChu Zhou
Summary: This article develops a gait planning method for underactuated bipedal robot to walk on uneven and compliant terrain by controlling and tracking the robot's CoM and desired velocity.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Robotics
Zhongyu Li, Jun Zeng, Shuxiao Chen, Koushil Sreenath
Summary: This paper presents an end-to-end autonomous navigation framework for bipedal robots to safely explore height-constrained environments. It leverages three layers of planners and a variable walking height controller to optimize trajectory plans and maintain stable periodic walking gaits. Experimental results with a bipedal robot Cassie demonstrate the reliability of the framework in avoiding obstacles and reaching the goal location in various cluttered environments.
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
(2023)
Article
Computer Science, Information Systems
Rohan P. Singh, Zhaoming Xie, Pierre Gergondet, Fumio Kanehiro
Summary: Recent advances in deep reinforcement learning combined with simulation training have provided a new approach for developing robust controllers for legged robots. However, applying these approaches to life-sized humanoid robots has been limited due to a large gap between simulation and reality. In this paper, the authors propose a method to overcome the sim2real gap issue by training in a simulated environment and utilizing torque feedback from the actuators on the real robot. The approach successfully achieves bipedal locomotion on a real HRP-5P humanoid robot.
Article
Automation & Control Systems
Marko Mihalec, Jingang Yi
Summary: This study proposes a motion and gait control design for bipedal robotic walkers to address the motion challenge on low-friction ground surfaces. By explicitly considering slipping dynamics and using an inverted pendulum model and a multi-link model, the controlled joint torques can be computed to achieve gait control. The experimental results demonstrate that considering foot slip improves performance and enables stable gait on low-friction ground surfaces.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Computer Science, Information Systems
Guillermo A. Castillo, Bowen Weng, Wei Zhang, Ayonga Hereid
Summary: This paper presents a novel reinforcement learning framework for designing cascade feedback control policies for 3D bipedal locomotion. The proposed solution decouples the locomotion problem into two modules, incorporating physical insights from walking dynamics and the Hybrid Zero Dynamics approach. The framework offers advantages such as lightweight network structure, sample efficiency, and reduced dependence on prior knowledge.
Article
Robotics
JongHun Choe, Joon-Ha Kim, Seungwoo Hong, Jinoh Lee, Hae-Won Park
Summary: This letter proposes a reactive locomotion method for bipedal robots, which enhances robustness and external disturbance rejection performance by seamlessly rendering several walking strategies of the ankle, hip, and footstep adjustment. The method utilizes Nonlinear Model Predictive Control (NMPC) to predict the future states of the robot in response to the walking strategies. The proposed controller is validated in simulation and experimental tests on a bipedal robot platform, demonstrating its real-time effectiveness.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Engineering, Mechanical
Alexander G. Steele, Apploinaire Etoundi, Alexander J. Hunt
Summary: This article presents experimental test results for joints used in a biomimetic bipedal robot. By using MRI and CT scans, joints of similar size and function to the biological counterparts were designed and constructed. The range of motion and passive stiffness of these joints were tested, and the results showed a match between the physical knee prototype and previous simulations. The hybrid hard-soft joints were characterized, providing insights for improved control and application in prosthetic designs and robotics.
JOURNAL OF MECHANISMS AND ROBOTICS-TRANSACTIONS OF THE ASME
(2023)
Article
Robotics
Buyoun Cho, Min-Su Kim, Sung-Woo Kim, Seunghoon Shin, Yeseong Jeong, Jun-Ho Oh, Hae-Won Park
Summary: This study presents a design method and controller for a compact embedded hydraulic power unit for supply pressure regulation in bipedal robots. The design has undergone detailed analysis and testing, demonstrating durability at high pressure and performance in robot motion experiments.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Hosain Bagheri, Vidu Jayanetti, Hailey R. Burch, Clayton E. Brenner, Benjamin R. Bethke, Hamidreza Marvi
Summary: This study investigates the performance of the WhegRunner robotic platform in running on granular media at different saturation levels. It examines the effects of bipedal/quadrupedal gait, saturation level, stride length, and stride frequency on the robot's velocity and cost of transport. The research shows that the quadrupedal gait performs better overall and the parameters affecting the robot's stride and velocity vary on dry and wet sand.
JOURNAL OF FIELD ROBOTICS
(2023)
Article
Engineering, Industrial
Joao Pedro Pinho, Arturo Forner-Cordero
Summary: Using a commercial exoskeleton for different shoulder positions can reduce electromyographic activity in the Medial Deltoid and Anterior Deltoid muscles, while increasing co-contraction in the Anterior Deltoid/Triceps Brachii. Wearing the exoskeleton can prolong task completion time for task B and reduce perceived effort for tasks A and C, improving overall comfort.
APPLIED ERGONOMICS
(2022)
Review
Neurosciences
Guilherme Silva Umemura, Fabianne Furtado, Fabia Camile dos Santos, Bruno da Silva Brandao Goncalves, Arturo Forner-Cordero
Summary: This review examines the impact of sleep conditions on balance control and finds that acute and chronic sleep deprivation, as well as poor sleep quality, have negative effects on postural control. Additionally, time awake worsens balance control and is linked to chronotype and circadian rhythms.
FRONTIERS IN NEUROSCIENCE
(2022)
Review
Engineering, Biomedical
Lucas R. L. Cardoso, Vanesa Bochkezanian, Arturo Forner-Cordero, Alejandro Melendez-Calderon, Antonio P. L. Bo
Summary: Wearable devices based on soft robotics (SR) or functional electrical stimulation (FES) have shown promise in supporting hand function recovery in individuals with SCI. However, technical improvements in user intent detection, portability, calibration, and standardized functional outcome assessment are needed to overcome limitations preventing their translation into clinical practice.
JOURNAL OF NEUROENGINEERING AND REHABILITATION
(2022)
Article
Computer Science, Information Systems
Eric Cito Becman, Larissa Driemeier, Oron Levin, Stephan Swinnen, Arturo Forner-Cordero
Summary: This study investigates the impact of training and testing condition differences on the predictions of a convolutional neural network (CNN) for myoelectric simultaneous and proportional control (SPC). A dataset of electromyogram (EMG) signals and joint angular accelerations recorded during a star drawing task was utilized. CNNs were trained using specific combinations of motion amplitude and frequency and tested under different combinations. The predictive performance was evaluated using normalized root mean squared error (NRMSE), correlation, and linear regression slope. The results showed that the predictive performance declined differently depending on the increase or decrease of confounding factors. Correlation decreased as the factors decreased, while slope deteriorated when the factors increased. NRMSE worsened in both increasing and decreasing factor scenarios. The study suggests that differences in EMG signal-to-noise ratio (SNR) between training and testing may affect the noise robustness of the CNNs' learned internal features, leading to worse correlations. Additionally, the inability of the networks to predict accelerations outside the training range may contribute to slope deterioration. These findings provide opportunities for developing strategies to mitigate the negative impact of confounding factors on myoelectric SPC devices.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Engineering, Civil
Tiago G. Goto, Hossein R. Najafabadi, Mizael F. Falheiro, Rafael T. Moura, Larissa Driemeier, Ahmad Barari, Marcos S. G. Tsuzuki, Thiago C. Martins
Summary: Non-gradient-based topology optimization (NGTO) is a promising method that can avoid local minima and deal better with manufacturability constraints. In this study, a new NGTO algorithm using the simulated annealing algorithm and connectivity criteria was proposed. The algorithm penalizes checkerboard solutions and converges to optimized structures with comparable or enhanced compliance values. The proposed algorithm, being non-gradient-based and easy to apply, presents itself as a promising approach in the field of topology optimization research. The main contribution of the proposed method is the binary non-gradient topology optimization with checkerboard-free structure.
Article
Surgery
Cristina Pires Camargo, Arturo Forner-Cordero, Bruna Matsumoto Silva, Vinicius Melo de Souza, Higor Souza Cunha, Yasmin de Oliveira Feitosa, Guilherme Arellano Campello, Pedro Henrique Gianjoppe dos Santos, Carolina Logo Rego, Heloisa Carvalho, Rolf Gemperli
Summary: This study aimed to investigate the effect of photobiomodulation with two wavelengths on an acute radiodermatitis animal model. The results showed that LED (630 nm, 850 nm, and 630 nm + 850 nm) had a beneficial effect in the treatment of radiodermatitis, and the combination of 630 nm + 850 nm and 630 nm parameters demonstrated the best macroscopic and microscopic results.
PLASTIC AND RECONSTRUCTIVE SURGERY-GLOBAL OPEN
(2023)
Article
Engineering, Mechanical
Bruno Mussulini, Larissa Driemeier, Rafael Traldi Moura, Renato Teixeira Vargas, Henrique Ramos, Marcilio Alves
Summary: Crashworthiness is crucial and challenging in automotive design. Friction is an important parameter in finite element crash simulation, affecting both vehicle kinematics and occupant interaction. A test rig was designed to measure the friction coefficient of both hard and soft material contact pairs, allowing for the calculation of a new friction coefficient model that captures a wider range of tribological phenomena.
INTERNATIONAL JOURNAL OF IMPACT ENGINEERING
(2023)
Article
Engineering, Biomedical
Pedro Felipe Giarusso de Vazquez, Carlos Alberto Stefano Filho, Gabriel Chaves de Melo, Arturo Forner-Cordero, Gabriela Castellano
Summary: The aim of this study was to evaluate the reproducibility of EEG functional connectivity features used to discriminate between left and right-hand motor imagery tasks. The β band was found to produce the most stable and discriminative features, as well as the best classification features for most subjects. The Cz electrode showed the highest number of significantly discriminating features. The motif synchronization method produced the largest number of significant features and was the most stable and discriminative for most subjects.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Biomedical
Gabriel Chaves de Melo, Gabriela Castellano, Arturo Forner-Cordero
Summary: A Brain-Computer Interface (BCI) translates brain activities into computer commands through decoding brain signals, with electro-encephalography being the most widely adopted technique for signal recording. However, the high intra-subject variability of EEG signals poses a challenge for BCI development. This study aims to improve a pseudo-online movement detection system by using motor imagery EEG signals, proposing a strategy to minimize the effects of poor spatial resolution and active reference electrode by finding the best combinations of electrode pairs. The average accuracy across 15 subjects was 95%.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Proceedings Paper
Engineering, Biomedical
Pedro Parik-Americano, Joao Pedro Pinho, Fabia Camile dos Santos, Camila Taira, Guilherme Silva Umemura, Arturo Forner-Cordero
Summary: This study found that wearing a lower leg exoskeleton affects locomotion and posture, leading to changes in heart rate, gait temporal asymmetry, and M-L displacement of the center of pressure.
2022 9TH IEEE RAS/EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS (BIOROB 2022)
(2022)
Article
Engineering, Electrical & Electronic
Tarcisio Antonio Hess-Coelho, Milton Cortez, Rafael Traldi Moura, Arturo Forner-Cordero
Summary: This article presents the conceptual design and model calculation of an exoskeleton using modular modeling methodology, and demonstrates the consistency of the model and the estimation of actuator torques through simulations.