Skinning a Robot: Design Methodologies for Large-Scale Robot Skin

Providing a robot with large-scale tactile sensing capabilities requires the use of design tools bridging the gap between user requirements and technical solutions. Given a set of functional requirements (e.g., minimum spatial sensitivity or minimum detectable force), two prerequisites must be considered: (i) the capability of the chosen tactile technology to satisfy these requirements from a technical standpoint; (ii) the ability of the customisation process to find a trade-off among different design parameters, such as (in case of robot skins based on the capacitive principle) dielectric thickness, diameter of sensing points, or weight. The contribution of this paper is two-fold: (i) the description of the possibilities offered by a design toolbox for large-scale robot skin based on Finite Element Analysis and optimisation principles, which provides a designer with insights and alternative choices to obtain a given tactile performance according to the scenario at hand; (ii) a discussion about the intrinsic limitations in simulating robot skin

Functional mimicry of Ruffini receptors with fibre Bragg gratings and deep neural networks enables a bio-inspired large-area tactile-sensitive skin

Collaborative robots are expected to physically interact with humans in daily living and the workplace, including industrial and healthcare settings. A key related enabling technology is tactile sensing, which currently requires addressing the outstanding scientific challenge to simultaneously detect contact location and intensity by means of soft conformable artificial skins adapting over large areas to the complex curved geometries of robot embodiments. In this work, the development of a large-area sensitive soft skin with a curved geometry is presented, allowing for robot total-body coverage through modular patches. The biomimetic skin consists of a soft polymeric matrix, resembling a human forearm, embedded with photonic fibre Bragg grating transducers, which partially mimics Ruffini mechanoreceptor functionality with diffuse, overlapping receptive fields. A convolutional neural network deep learning algorithm and a multigrid neuron integration process were implemented to decode the fibre Bragg grating sensor outputs for inference of contact force magnitude and localization through the skin surface. Results of 35 mN (interquartile range 56 mN) and 3.2 mm (interquartile range 2.3 mm) median errors were achieved for force and localization predictions, respectively. Demonstrations with an anthropomorphic arm pave the way towards artificial intelligence based integrated skins enabling safe human–robot cooperation via machine intelligence

EM-skin:an artificial robotic skin using magnetic inductance tomography

Physical sensing by touch is essential for building intelligent artificial systems in robotic manipulation and human-robotic interaction. Inductive skins are being investigated as part of a major effort to develop the most robust and reliable touch sensors, primarily based on traditional inductive proximity sensing. Magnetic induction tomography (MIT) is an imaging system considered for medical diagnostics and industrial process monitoring. This article presents a novel electromagnetic-based skin (EM-skin) using the MIT imaging system. This is done by processing the mutual inductance data from a planar array sensing a skin-like medium, including an elastomeric medium that interfaces the MIT sensors with plates of metallic or magnetic touch elements. This article demonstrates EM-based multi-touch, dynamical touch, and quantitative touch pressure sensing. MIT data are captured at 10 frames/s, so allowing for dynamical touch analysis. The EM-skin sensing area of 900 mm demonstrates a large area of sensing skin. The results show the successful reconstruction of dynamical sensing, where two applied cyclic touch points, with different frequencies are discriminately detected. Quantitative force sensing shows the detection of a minimum of 120 mN force, which translates to 0.38 kP of applied pressure in the described system. Further force calibration is carried out demonstrating the quantitative nature of the proposed EM skin. These results will open the way to a new generation of distributed and reliable soft skins that are versatile due to material design and processing.</p

EM-skin:an artificial robotic skin using magnetic inductance tomography

Physical sensing by touch is essential for building intelligent artificial systems in robotic manipulation and human-robotic interaction. Inductive skins are being investigated as part of a major effort to develop the most robust and reliable touch sensors, primarily based on traditional inductive proximity sensing. Magnetic induction tomography (MIT) is an imaging system considered for medical diagnostics and industrial process monitoring. This article presents a novel electromagnetic-based skin (EM-skin) using the MIT imaging system. This is done by processing the mutual inductance data from a planar array sensing a skin-like medium, including an elastomeric medium that interfaces the MIT sensors with plates of metallic or magnetic touch elements. This article demonstrates EM-based multi-touch, dynamical touch, and quantitative touch pressure sensing. MIT data are captured at 10 frames/s, so allowing for dynamical touch analysis. The EM-skin sensing area of 900 mm demonstrates a large area of sensing skin. The results show the successful reconstruction of dynamical sensing, where two applied cyclic touch points, with different frequencies are discriminately detected. Quantitative force sensing shows the detection of a minimum of 120 mN force, which translates to 0.38 kP of applied pressure in the described system. Further force calibration is carried out demonstrating the quantitative nature of the proposed EM skin. These results will open the way to a new generation of distributed and reliable soft skins that are versatile due to material design and processing.</p

A Human-Robot Interaction Perspective on Assistive and Rehabilitation Robotics

Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing, and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human–robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions

On the Use of Large Area Tactile Feedback for Contact Data Processing and Robot Control

The progress in microelectronics and embedded systems has recently enabled the realization of devices for robots functionally similar to the human skin, providing large area tactile feedback over the whole robot body. The availability of such kind of systems, commonly referred to as extit{robot skins}, makes possible to measure the contact pressure distribution applied on the robot body over an arbitrary area. Large area tactile systems open new scenarios on contact processing, both for control and cognitive level processing, enabling the interpretation of physical contacts. The contents proposed in this thesis address these topics by proposing techniques exploiting large area tactile feedback for: (i) contact data processing and classification; (ii) robot control

A Human–Robot Interaction Perspective on Assistive and Rehabilitation Robotics

abstract: Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing, and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human–robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions.View the article as published at http://journal.frontiersin.org/article/10.3389/fnbot.2017.00024/ful

Embodied active tactile perception

Tactile perception plays an important role in an agent safely interacting with the environment while acquiring information about it. Bio-inspired robotics opens up possibilities for a new paradigm leveraging the morphology of the body, which filters the tactile information in physical interactions and enables investigations of new designs for embodied active tactile perception. The subjects of morphology embodied active perception and motor embodied active perception is defined and discussed in this chapter. In the scope of morphology embodied active perception, sensor optimization and sensor adaptation are further defined to describe the change of sensor morphology in the design phase and the interacting phase, respectively. More specifically, the concept of online and offline sensor adjustment is presented. Sensor optimization is solely considered in the offline process for optimization and evolution design of the sensor structure and characteristics. Sensor adaptation and motor embodied active perception are considered in the online process to actively shape the sensing process with the morphology change of the sensors themselves and the action of the body where the sensors are placed, respectively. "Design as a whole" is proposed as an inverse problem to address the sensing tasks. The design of new tactile sensors should not focus on the sensor per se but should also include design parameters for sensor optimization, sensor adaptation, and motor actions