Safe Physical Human-Robot Interaction: Measurements, Analysis and New Insights

Haddadin, Sami; Albu-Schäffer, Alin; Hirzinger, Gerd

doi:10.1007/978-3-642-14743-2_33

Cited by 77 publications

(65 citation statements)

References 14 publications

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“…hematoma). 20,21 In Eq. (8), F is the normal impact force applied to the lower arm; x 0 and x 1 denote the displacement between the robot end-effector and the lower arm bone tissue at impact start time t 0 and end time t 1 .…”

Section: Impact Energy Densitymentioning

confidence: 99%

A tool for the evaluation of human lower arm injury: approach, experimental validation and application to safe robotics

Povse¹,

Haddadin

Belder

et al. 2015

Robotica

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SUMMARYThis paper treats the systematic injury analysis of lower arm robot-human impacts. For this purpose, a passive mechanical lower arm (PMLA) was developed that mimics the human impact response and is suitable for systematic impact testing and prediction of mild contusions and lacerations. A mathematical model of the passive human lower arm is adopted to the control of the PMLA. Its biofidelity is verified by a number of comparative impact experiments with the PMLA and a human volunteer. The respective dynamic impact responses show very good consistency and support the fact that the developed device may serve as a human substitute in safety analysis for the described conditions. The collision tests were performed with two different robots: the DLR Lightweight Robot III (LWR-III) and the EPSON PS3L industrial robot. The data acquired in the PMLA impact experiments were used to encapsulate the results in a robot independent safety curve, taking into account robot's reflected inertia, velocity and impact geometry. Safety curves define the velocity boundaries on robot motions based on the instantaneous manipulator dynamics and possible human injury due to unforeseen impacts.

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Section: Impact Energy Densitymentioning

confidence: 99%

A tool for the evaluation of human lower arm injury: approach, experimental validation and application to safe robotics

Povse¹,

Haddadin

Belder

et al. 2015

Robotica

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show abstract

“…For that reason a disturbance observer [8] does not only detect contacts but also classifies them according to their "severity level". This is defined according to the evaluated severity levels for humans during worst-case robot-human impacts (see Haddadin et al [9]). Figure 5 displays the flow chart of the entire implementation, which can be divided into three sub loops: exploration, bin-picking and human-robot interaction.…”

Section: Compliant Control and Disturbance Observermentioning

confidence: 99%

Cooperative bin-picking with Time-of-Flight camera and impedance controlled DLR lightweight robot III

Fuchs

Haddadin

Keller

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Because bin-picking effectively mirrors great challenges in robotics, it has been a relevant robotic showpiece application for several decades. This paper approaches the binpicking issue by applying the latest state-of-the-art hardware components, namely an impedance controlled light-weight robot and a Time-of-Flight camera. Lightweight robots have gained new capabilities in both sensing and actuation without suffering a decrease in speed and payload. Time-of-Flight cameras are superior to common proximity sensors by providing depth and intensity images in video frame rate independent of textures. Furthermore, the bin-picking process presented here incorporates an environment model and allows for physical human-robot interaction. The existing imprecision in Time-ofFlight camera measurements is compensated by the compliant behavior of the robot. A generic state machine monitors the entire bin-picking process. This paper describes the computer vision algorithms in combination with the sophisticated control schemes of the robot and demonstrates a reliable and robust solution to the chosen problem.

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“…Similarly, it is becoming increasingly clear that successfully overcoming complex technological and scientific challenges makes part of the story only [2], in that the impact of such advances in society, from the consideration of daily political events of relevance to the dominating believes, culture and popular trends, must be simultaneously treated. In that sense, multidisciplinary research, encompassing technology and science and the corresponding interpretation from the point of view of the broader fields of humanities, such as arts, religion and philosophy, is therefore not a volatile trend but an unavoidable current necessity.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2017

IRATJ

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Here we describe an architecture and model to simulate a sub-set of human interactions and transitions in human emotions occurring via these interactions. The architecture is modular and highly scalable and draws from analogies in human physiology and modern neuroscience while exploiting common human physical phenomena to quantify persistency in long and short term memory and how these concepts affect emotion, emotional swings and reactions. The model is phenomenological in that it emulates phenomena rather than mechanisms. We further allow for enough flexibility to emulate a broad set of mechanisms and behavior so that, while our examples are not universal, there is room to explore a wide range of phenomena. We advance that the implementation and use of this architecture can be potentially exploited in psychology, psychiatry, sexuality and the study of emotional transitions and swings due to physical interactions. multidisciplinary point of view, our objective now is to detail the technical details of the current human-computer interactions project henceforth referred to as the Samantha Project (SP), and present the terminology and modular architecture of the project below. We further advance that cloud computing and online implementations have been purposefully avoided and a low cost dedicated algorithms have been sought instead. We would also like to note that the etymology of the name Samantha suggests that it comes from Aramaic and that its inherent meaning might be "the one that listens" [14]. Since we deal with a system that will interact with its surroundings and with itself via several interfaces, we find this name to be suggestive and appropriate to the project. For simplicity, we will unfold the Samantha Project model focusing on physical interactions (PI), a single emotional family (EF), i.e. here the degree of sexual implication or perceived sexuality implication in a given context is the choice of emotional family, a single genome, i.e. here the Physiological Genome (PG) that will control the relationship between emotional states, physical interactions and reactions, and the Call For Attention (CFA) algorithm-a full explanation of these terms is given below as the description of the model and architecture unfolds. CitationLet us assume that N sensors are present in the humanoid system as shown in Figure 1 where an illustration of human anatomy is shown and locations where sensors are placed are also shown in red. Physical interactions can thus be quantified in N locations by quantitatively measuring the pressure exerted from 0 to 1 (normalized coefficients); 0 (~10 kPa or less in the experiments) being no pressure measured and 1 (~100 kPa in the experiments) being maximum pressure measured. We further define a sensor measuring 0 as inactive and those giving measurements from 0, excluding 0, to 1 as active. Furthermore, from now on we define physical interactions (PI) between the humanoid-system and humans as "physical touch", external physical interactions, or external active physical inter...

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Safe Physical Human-Robot Interaction: Measurements, Analysis and New Insights

Cited by 77 publications

References 14 publications

A tool for the evaluation of human lower arm injury: approach, experimental validation and application to safe robotics

A tool for the evaluation of human lower arm injury: approach, experimental validation and application to safe robotics

Cooperative bin-picking with Time-of-Flight camera and impedance controlled DLR lightweight robot III

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