Dual humanoid robot collaborative control systems possess better flexibility and adaptability in complex environments due to their similar structures to humans. This paper adopts a distributed model predictive controller based on the leader-follower approach to address the collaborative transportation control issue of dual humanoid robots. In the dual-robot collaborative control system, network latency issues may arise due to unstable network conditions, affecting the consistency of dual-robot collaboration. To solve this issue, a communication protocol was constructed through socket communication for dual-robot collaborative consistency, thereby resolving the problem of consistency in dual humanoid robot collaboration. Additionally, due to the complex structure of humanoid robots, there are deficiencies in position tracking accuracy during movement. To address the poor accuracy in position tracking, this paper proposes a distributed model predictive control that considers historical cumulative error, thus enhancing the position tracking accuracy of dual-robot collaborative control.
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http://dx.doi.org/10.3390/biomimetics9060332 | DOI Listing |
Biomimetics (Basel)
December 2024
School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China.
Humanoid robots are becoming a global research focus. Due to the limitations of bipedal walking technology, mobile humanoid robots equipped with a wheeled chassis and dual arms have emerged as the most suitable configuration for performing complex tasks in factory or home environments. To address the high redundancy issue arising from the wheeled chassis and dual-arm design of mobile humanoid robots, this study proposes a whole-body coordinated motion control algorithm based on arm potential energy optimization.
View Article and Find Full Text PDFBiomimetics (Basel)
October 2024
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Future humanoid robots will be widely deployed in our daily lives. Motion planning and control in an unstructured, confined, and human-centered environment utilizing dexterity and a cooperative ability of dual-arm robots is still an open issue. We propose a globally guided dual-arm reactive motion controller (GGDRC) that combines the strengths of global planning and reactive methods.
View Article and Find Full Text PDFFront Med (Lausanne)
October 2024
Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, United States.
Background: Modern digital anthropometry applications utilize smartphone cameras to rapidly construct three-dimensional humanoid avatars, quantify relevant anthropometric variables, and estimate body composition.
Methods: In the present study, 131 participants ([73 M, 58 F] age 33.7 ± 16.
Adv Sci (Weinh)
December 2024
State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China.
Tactile sensors with capability of multiaxial force perception play a vital role in robotics and human-machine interfaces. Flexible optical waveguide sensors have been an emerging paradigm in tactile sensing due to their high sensitivity, fast response, and antielectromagnetic interference. Herein, a flexible multiaxial force sensor enabled by U-shaped optical micro/nanofibers (MNFs) is reported.
View Article and Find Full Text PDFInt J Obes (Lond)
December 2024
Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA.
Objective: To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient body composition analysis and metabolic disease risk stratification in clinical settings.
Methods: Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations.
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