Pressure-Driven Body Compliance Using Robot Skin

Guadarrama-Olvera, Julio Rogelio; Dean-León, Emmanuel; Bergner, Florian; Cheng, Gordon

doi:10.1109/lra.2019.2928214

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…The effectiveness of the EDS in comparison to its clock-driven counterpart in various experimental evaluations and applications [38][39][40]83,[87][88][89]93], including control [39,[87][88][89]93], can be assessed by analyzing the indicators: 1) the network traffic between the deployed skin system and the information handling system; and 2) the CPU load of the perception module in the information handling system. While deeper analysis and evaluations with many additional indicators (e.g., performance of control) have been performed previously, this work focuses solely on the indicators network traffic and CPU load to provide a comprehensive overview of all results in the context of validating the presented design and realization principles.…”

Section: Resultsmentioning

confidence: 99%

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Design and Realization of an Efficient Large-Area Event-Driven E-Skin

Bergner

Dean-León

Cheng

2020

Sensors

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The sense of touch enables us to safely interact and control our contacts with our surroundings. Many technical systems and applications could profit from a similar type of sense. Yet, despite the emergence of e-skin systems covering more extensive areas, large-area realizations of e-skin effectively boosting applications are still rare. Recent advancements have improved the deployability and robustness of e-skin systems laying the basis for their scalability. However, the upscaling of e-skin systems introduces yet another challenge—the challenge of handling a large amount of heterogeneous tactile information with complex spatial relations between sensing points. We targeted this challenge and proposed an event-driven approach for large-area skin systems. While our previous works focused on the implementation and the experimental validation of the approach, this work now provides the consolidated foundations for realizing, designing, and understanding large-area event-driven e-skin systems for effective applications. This work homogenizes the different perspectives on event-driven systems and assesses the applicability of existing event-driven implementations in large-area skin systems. Additionally, we provide novel guidelines for tuning the novelty-threshold of event generators. Overall, this work develops a systematic approach towards realizing a flexible event-driven information handling system on standard computer systems for large-scale e-skin with detailed descriptions on the effective design of event generators and decoders. All designs and guidelines are validated by outlining their impacts on our implementations, and by consolidating various experimental results. The resulting system design for e-skin systems is scalable, efficient, flexible, and capable of handling large amounts of information without customized hardware. The system provides the feasibility of complex large-area tactile applications, for instance in robotics.

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Section: Resultsmentioning

confidence: 99%

“…Following these examinations, we successfully realized an event decoder for our event-driven LASS. This event decoder has been extensively utilized in applications that take advantage of our event-driven e-skin and combine it with state-of-the-art control algorithms [83,[86][87][88][89].…”

Section: Realizing Event Decodersmentioning

confidence: 99%

Design and Realization of an Efficient Large-Area Event-Driven E-Skin

Bergner

Dean-León

Cheng

2020

Sensors

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“…Although vision and touch ( proprioceptive force rather than tact ) emerge nowadays as the most popular senses on collaborative robots, the advent of cheap, precise, and easy to integrate tactile, distance, and audio sensors present great opportunities for the future. Typically, we believe that robot skins (e.g., on arms and hands, Guadarrama-Olvera et al, 2019 ; Navarro et al, 2020 ) will simplify interaction, boosting the opportunities for human-robot collaboration. It is imperative that researchers develop the appropriate tools for this.…”

Section: Discussionmentioning

confidence: 99%

Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities

Cherubini

Navarro-Alarcon

2021

Front. Neurorobot.

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The objective of this paper is to present a systematic review of existing sensor-based control methodologies for applications that involve direct interaction between humans and robots, in the form of either physical collaboration or safe coexistence. To this end, we first introduce the basic formulation of the sensor-servo problem, and then, present its most common approaches: vision-based, touch-based, audio-based, and distance-based control. Afterwards, we discuss and formalize the methods that integrate heterogeneous sensors at the control level. The surveyed body of literature is classified according to various factors such as: sensor type, sensor integration method, and application domain. Finally, we discuss open problems, potential applications, and future research directions.

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“…One of the detrimental consequences of the high (static) friction in tendon-driven hands is that it becomes hard to detect a contact -be it an intentional contact with an object that should be grasped, or an unintentional collision with an obstacle. Tactile sensors [5], [6] or proprioceptive sensors [7] can be used to detect such contacts, but they require further hardware integration and modification of the hand. A further contribution of this paper is to demonstrate (in the experimental section in Sect.…”

Section: Introductionmentioning

confidence: 99%

Friction Estimation for Tendon-Driven Robotic Hands

Lange

Pfanne

Steinmetz

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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In tendon-driven robotic hands, tendons are usually routed along several pulleys. The resulting friction is often substantial, and must therefore be modelled and estimated, for instance for accurate control and contact detection. Common approaches for friction estimation consider special dedicated setups, where the parameters of a static or dynamic friction model at a single contact point are determined. In this paper, we rather combine such individual friction models into an overall friction model for the entire finger. Furthermore, we propose a method for estimating the parameters of this overall model in situ, i.e. from trajectories executed on the assembled hand, avoiding the need for dedicated setups. An important component of the proposed model is a varying bias for treating friction at low velocities, allowing a simpler static friction model to be used. We demonstrate that our approach enables contacts to be detected more accurately on the DLR David hand, without additional sensors.

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Pressure-Driven Body Compliance Using Robot Skin

Cited by 10 publications

References 26 publications

Design and Realization of an Efficient Large-Area Event-Driven E-Skin

Design and Realization of an Efficient Large-Area Event-Driven E-Skin

Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities

Friction Estimation for Tendon-Driven Robotic Hands

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