Dynamic simulation of a hydraulic exoskeleton robot based on virtual prototyping

Ding, Shuo; Ouyang, Xiaoping; Fan, Boqian; Yang, Huayong; Gong, Gwo‐Ching

doi:10.1109/aim.2016.7576979

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…Similar studies also include References [ 35 , 36 ]. Ding et al [ 37 ] developed a virtual prototype of a hydraulic exoskeleton robot in simulation software ADAMS (automatic dynamic analysis of mechanical systems) and analyzed its dynamic behaviors. The results were consistent with those obtained from real-world experiments.…”

Section: Background Reviewmentioning

confidence: 99%

AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training

Chen

Liu

et al. 2020

Sensors

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Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders’ situational awareness in disaster areas for urban search and rescue (SaR) operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots.

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Section: Background Reviewmentioning

confidence: 99%

AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training

Chen

Liu

et al. 2020

Sensors

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“…Furthermore, SolidWorks, a well‐known CAD software, was used to design 3D lower limb exoskeleton, that is, portable assistive devices (Ding & Ouyang, 2016). The simulation study of the 3D design of a lower limb exoskeleton was found cost‐effective and useful in improving the efficiency of assistive devices.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Spada (2018) evaluated the performance of the chairless exoskeleton in the simulated environment. Several experiments were carried out in a simulated environment (Bosch & van Eck, 2016; Deng & Wang, 2017; Ding & Ouyang, 2016; Fournier & Sm, 2017; Mir‐Nasiri & Jo, 2017; Shepherd & Rouse, 2017; Zhang & Liu, 2017). The performance of the lower limb exoskeleton's 3D design in a simulated environment was not evaluated or studied.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of 3D design lower limb exoskeleton on human musculoskeletal with various loads

Sohane

Agarwal

2021

Expert Systems

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The surface electromyography (SEMG) based exoskeleton presents a new opportunity for human augmentation and rehabilitation. Developing an efficient exoskeleton in real‐time is challenging as each individual's muscles and joint forces are unique. The aim of this research article is to analyze and evaluate the design of the lower limb exoskeleton during the squatting movement in a simulated environment to address problems concerning the development of a functional exoskeleton for an individual. An exoskeleton was designed in SolidWorks CAD software and imported into AnyBody Modelling Software (AMS). Thereafter, the performance of 3D designed exoskeleton was evaluated by placing various loads (0:5:25 kg) on both the shoulders of the human musculoskeletal. The results show the force in the knee muscles with the assistance of the exoskeleton were reduced significantly by 65.18–97.20% in the biceps femoris, 50.01–33.16% in the rectus femoris, 41.87–28.31% in the vastus lateralis, 42.25–28.78% in the vastus medialis, 7.28–22.91% in gluteus medius, and 22.54–13.13% in semitendinosus. The force in the knee joint was reduced by 44.04–31.43% as the load increases. Individual muscle force estimated from the SEMG signal and AMS during squatting was also compared for validation. The developed model helps in understanding the load effects on different muscles and provides useful information for the construction of an individual's optimized exoskeleton.

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Dynamic simulation of a hydraulic exoskeleton robot based on virtual prototyping

Cited by 2 publications

References 17 publications

AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training

AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training

Evaluation of 3D design lower limb exoskeleton on human musculoskeletal with various loads

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