Study on Soft-Tissue Injury in Robotics

Haddadin, Sami; Albu‐Schäffer, Alin; Haddadin, F.; Rosmann, Jurgen; Hirzinger, Gerd

doi:10.1109/mra.2011.942996

Cited by 33 publications

(16 citation statements)

References 20 publications

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“…Their treatment needs special care and presumably separate testing similar to our work in Haddadin et al (2011). However, the robot structure, the end-effector, and a Finding the exactly required granularity a i of the primitive parameters needs larger experimental investigations than we can provide here.…”

Section: Drop-testing Experimentsmentioning

confidence: 93%

On making robots understand safety: Embedding injury knowledge into control

Haddadin

Khoury

et al. 2012

The International Journal of Robotics Research

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162

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Enabling robots to safely interact with humans is an essential goal of robotics research. The developments achieved over recent years in mechanical design and control made it possible to have active cooperation between humans and robots in rather complex situations. For this, safe robot behavior even under worst-case situations is crucial and forms also a basis for higher-level decisional aspects. For quantifying what safe behavior really means, the definition of injury, as well as understanding its general dynamics, are essential. This insight can then be applied to design and control robots such that injury due to robot-human impacts is explicitly taken into account. In this paper we approach the problem from a medical injury analysis point of view in order to formulate the relation between robot mass, velocity, impact geometry and resulting injury qualified in medical terms. We transform these insights into processable representations and propose a motion supervisor that utilizes injury knowledge for generating safe robot motions. The algorithm takes into account the reflected inertia, velocity, and geometry at possible impact locations. The proposed framework forms a basis for generating truly safe velocity bounds that explicitly consider the dynamic properties of the manipulator and human injury.

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Section: Drop-testing Experimentsmentioning

confidence: 93%

On making robots understand safety: Embedding injury knowledge into control

Haddadin

Khoury

et al. 2012

The International Journal of Robotics Research

Self Cite

162

View full text Add to dashboard Cite

show abstract

“…The understanding of human injury has been treated in the fields of injury biomechanics and forensics for several decades and the respective studies served for the early work on human injury in robotics. In fact, various injury measures from biomechanics and forensics were applied to human injury analysis in robotics [2], [3], [4], [5], [1], [6], [7]. An overview on the most important existing injury classification metrics and biomechanical injury measures can be found in [8].…”

Section: Impact Scenariosmentioning

confidence: 99%

Physical Safety in Robotics

Haddadin

2015

Formal Modeling and Verification of Cyber-Physical Systems

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Abstract. Over the last decade, safe physical Human-Robot Interaction (pHRI) has been made possible due to significant advances in mechatronics, control, and planning. One result of these developments were fully integrated safer lightweight robots that are equipped with sophisticated interaction control capabilities. These new robots have even opened up novel and unforeseen application domains, in which human and robot are sought to work and interact with each other. For this, safe physical interaction is prime. This chapter gives a brief overview on two of its central aspects: human safety from an injury and standards standpoint, and control for physical interaction with focus on interaction control and collision handling.

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“…Previous research has used deviations from an expected torque, predicted using a model of the dynamics of the robot arm, to detect anomalous conditions (e.g., Dixon et al 2000;De Luca and Mattone 2004;Haddadin et al 2008Haddadin et al , 2011. Determining an accurate model of the arm dynamics can be challenging.…”

Section: Anomaly Detection Using Joint Torque Sensors and Arm Dynamicsmentioning

confidence: 99%

Improving robot manipulation with data-driven object-centric models of everyday forces

Jain

Kemp

2013

Auton Robot

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Based on a lifetime of experience, people anticipate the forces associated with performing a manipulation task. In contrast, most robots lack common sense about the forces involved in everyday manipulation tasks. In this paper, we present data-driven methods to inform robots about the forces that they are likely to encounter when performing specific tasks. In the context of door opening, we demonstrate that data-driven object-centric models can be used to haptically recognize specific doors, haptically recognize classes of door (e.g., refrigerator vs. kitchen cabinet), and haptically detect anomalous forces while opening a door, even when opening a specific door for the first time. We also demonstrate that two distinct robots can use forces captured from people opening doors to better detect anomalous forces. These results illustrate the potential for robots to use shared databases of forces to better manipulate the world and attain common sense about everyday forces.

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Study on Soft-Tissue Injury in Robotics

Cited by 33 publications

References 20 publications

On making robots understand safety: Embedding injury knowledge into control

On making robots understand safety: Embedding injury knowledge into control

Physical Safety in Robotics

Improving robot manipulation with data-driven object-centric models of everyday forces

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