A High-Fidelity Simulation Framework for Grasping Stability Analysis in Human Casualty Manipulation

Recently, there has been a growing interest in rescue robots due to their vital role in addressing emergency scenarios and providing crucial assistance in challenging or hazardous situations where human intervention is problematic. However, very few of these robots are capable of actively engaging with humans and undertaking physical manipulation tasks. This limitation is largely attributed to the absence of tools that can realistically simulate physical interactions, especially the contact mechanisms between a robotic gripper and a human body. In this study, we aim to address key limitations in current developments towards robotic casualty manipulation. Firstly, we present an integrative simulation framework for casualty manipulation. We adapt a finite element method (FEM) tool into the grasping and manipulation scenario, and the developed framework can provide accurate biomechanical reactions resulting from manipulation. Secondly, we conduct a detailed assessment of grasping stability during casualty grasping and manipulation simulations. To validate the necessity and superior performance of the proposed high-fidelity simulation framework, we conducted a qualitative and quantitative comparison of grasping stability analyses between the proposed framework and the state-of-the-art multi-body physics simulations. Through these efforts, we have taken the first step towards a feasible solution for robotic casualty manipulation.

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Publication Details

Journal
IEEE Transactions on Medical Robotics and Bionics
Published
2025-01-09
DOI
https://doi.org/10.1109/tmrb.2025.3527687
Citations
3
Primary Topic
Muscle activation and electromyography studies
Type
article
Field-Weighted Citation Impact
2.02

Funders

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article

A High-Fidelity Simulation Framework for Grasping Stability Analysis in Human Casualty Manipulation

Chad Spurlock, Rajarshi Roy, Qianwen Zhao, Long Wang et al.
3 citations
IEEE Transactions on Medical Robotics and Bionics
Muscle activation and electromyography studies
2.02
article

A High-Fidelity Simulation Framework for Grasping Stability Analysis in Human Casualty Manipulation

Chad Spurlock, Rajarshi Roy, Qianwen Zhao, Long Wang, Kevin Lister
article en
3 citations

Abstract

Recently, there has been a growing interest in rescue robots due to their vital role in addressing emergency scenarios and providing crucial assistance in challenging or hazardous situations where human intervention is problematic. However, very few of these robots are capable of actively engaging with humans and undertaking physical manipulation tasks. This limitation is largely attributed to the absence of tools that can realistically simulate physical interactions, especially the contact mechanisms between a robotic gripper and a human body. In this study, we aim to address key limitations in current developments towards robotic casualty manipulation. Firstly, we present an integrative simulation framework for casualty manipulation. We adapt a finite element method (FEM) tool into the grasping and manipulation scenario, and the developed framework can provide accurate biomechanical reactions resulting from manipulation. Secondly, we conduct a detailed assessment of grasping stability during casualty grasping and manipulation simulations. To validate the necessity and superior performance of the proposed high-fidelity simulation framework, we conducted a qualitative and quantitative comparison of grasping stability analyses between the proposed framework and the state-of-the-art multi-body physics simulations. Through these efforts, we have taken the first step towards a feasible solution for robotic casualty manipulation.

IEEE Transactions on Medical Robotics and BionicsVol. 7(1)
Stevens Institute of Technology (US), Corvid Technologies (United States) (US)
Defense Health Agency
Openalex Percentile: Top 16%
Muscle activation and electromyography studies
2.02
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A High-Fidelity Simulation Framework for Grasping Stability Analysis in Human Casualty Manipulation — Chad Spurlock, Rajarshi Roy, et al. · IEEE Transactions on Medical Robotics and Bionics (2025) | TGRS Research Map | TGRS