Tactile sensing and control of robotic manipulator integrating fiber Bragg grating strain-sensor

Tactile sensing is an instrumental modality of robotic manipulation, as it provides information that is not accessible via remote sensors such as cameras or lidars. Touch is particularly crucial in unstructured environments, where the robot’s internal representation of manipulated objects is uncertain. In this study we present the sensorization of an existing artificial hand, with the aim to achieve fine control of robotic limbs and perception of object’s physical properties. Tactile feedback is conveyed by means of a soft sensor integrated at the fingertip of a robotic hand. The sensor consists of an optical fiber, housing Fiber Bragg Gratings (FBGs) transducers, embedded into a soft polymeric material integrated on a rigid hand. Through several tasks involving grasps of different objects in various conditions, the ability of the system to acquire information is assessed. Results show that a classifier based on the sensor outputs of the robotic hand is capable of accurately detecting both size and rigidity of the operated objects (99.36 and 100% accuracy, respectively). Furthermore, the outputs provide evidence of the ability to grab fragile objects without breakage or slippage e and to perform dynamic manipulative tasks, that involve the adaptation of fingers position based on the grasped objects’ condition

Tactile Sensing and Control of Robotic Manipulator Integrating Fiber Bragg Grating Strain-Sensor

Tactile sensing is an instrumental modality of robotic manipulation, as it provides information that is not accessible via remote sensors such as cameras or lidars. Touch is particularly crucial in unstructured environments, where the robot's internal representation of manipulated objects is uncertain. In this study we present the sensorization of an existing artificial hand, with the aim to achieve fine control of robotic limbs and perception of object's physical properties. Tactile feedback is conveyed by means of a soft sensor integrated at the fingertip of a robotic hand. The sensor consists of an optical fiber, housing Fiber Bragg Gratings (FBGs) transducers, embedded into a soft polymeric material integrated on a rigid hand. Through several tasks involving grasps of different objects in various conditions, the ability of the system to acquire information is assessed. Results show that a classifier based on the sensor outputs of the robotic hand is capable of accurately detecting both size and rigidity of the operated objects (99.36 and 100% accuracy, respectively). Furthermore, the outputs provide evidence of the ability to grab fragile objects without breakage or slippage e and to perform dynamic manipulative tasks, that involve the adaptation of fingers position based on the grasped objects' condition

Tactile Sensing and Control of Robotic Manipulator Integrating Fiber Bragg Grating Strain-Sensor

Tactile sensing is an instrumental modality of robotic manipulation, as it provides information that is not accessible via remote sensors such as cameras or lidars. Touch is particularly crucial in unstructured environments, where the robot's internal representation of manipulated objects is uncertain. In this study we present the sensorization of an existing artificial hand, with the aim to achieve fine control of robotic limbs and perception of object's physical properties. Tactile feedback is conveyed by means of a soft sensor integrated at the fingertip of a robotic hand. The sensor consists of an optical fiber, housing Fiber Bragg Gratings (FBGs) transducers, embedded into a soft polymeric material integrated on a rigid hand. Through several tasks involving grasps of different objects in various conditions, the ability of the system to acquire information is assessed. Results show that a classifier based on the sensor outputs of the robotic hand is capable of accurately detecting both size and rigidity of the operated objects (99.36 and 100% accuracy, respectively). Furthermore, the outputs provide evidence of the ability to grab fragile objects without breakage or slippage e and to perform dynamic manipulative tasks, that involve the adaptation of fingers position based on the grasped objects' condition

Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework

Recently, data-driven models such as deep neural networks have shown to be promising tools for modelling and state inference in soft robots. However, voluminous amounts of data are necessary for deep models to perform effectively, which requires exhaustive and quality data collection, particularly of state labels. Consequently, obtaining labelled state data for soft robotic systems is challenged for various reasons, including difficulty in the sensorization of soft robots and the inconvenience of collecting data in unstructured environments. To address this challenge, in this paper, we propose a semi-supervised sequential variational Bayes (DSVB) framework for transfer learning and state inference in soft robots with missing state labels on certain robot configurations. Considering that soft robots may exhibit distinct dynamics under different robot configurations, a feature space transfer strategy is also incorporated to promote the adaptation of latent features across multiple configurations. Unlike existing transfer learning approaches, our proposed DSVB employs a recurrent neural network to model the nonlinear dynamics and temporal coherence in soft robot data. The proposed framework is validated on multiple setup configurations of a pneumatic-based soft robot finger. Experimental results on four transfer scenarios demonstrate that DSVB performs effective transfer learning and accurate state inference amidst missing state labels. The data and code are available at https://github.com/shageenderan/DSVB.Comment: Accepted at the International Conference on Robotics and Automation (ICRA) 202

Soft Morphological Computation

Soft Robotics is a relatively new area of research, where progress in material science has powered the next generation of robots, exhibiting biological-like properties such as soft/elastic tissues, compliance, resilience and more besides. One of the issues when employing soft robotics technologies is the soft nature of the interactions arising between the robot and its environment. These interactions are complex, and the their dynamics are non-linear and hard to capture with known models. In this thesis we argue that complex soft interactions can actually be beneficial to the robot, and give rise to rich stimuli which can be used for the resolution of robot tasks. We further argue that the usefulness of these interactions depends on statistical regularities, or structure, that appear in the stimuli. To this end, robots should appropriately employ their morphology and their actions, to influence the system-environment interactions such that structure can arise in the stimuli. In this thesis we show that learning processes can be used to perform such a task. Following this rationale, this thesis proposes and supports the theory of Soft Morphological Computation (SoMComp), by which a soft robot should appropriately condition, or ‘affect’, the soft interactions to improve the quality of the physical stimuli arising from it. SoMComp is composed of four main principles, i.e.: Soft Proprioception, Soft Sensing, Soft Morphology and Soft Actuation. Each of these principles is explored in the context of haptic object recognition or object handling in soft robots. Finally, this thesis provides an overview of this research and its future directions.AHDB CP17

Ambient intelligence and affective computing: a contribute to energetic sustainability

Tese de Doutoramento em Informática.economy and the citizen behaviours are putting stress on resources at increasing scales. Society demands sustainable solutions for these problems. However, these solutions need to compromise restrictions enforced by either society, physics and resources. This leads to the traditional dimensions of sustainability: economic, environmental and social, which need to be addressed as a whole in order to find sustainable configurations. Although not as old as sustainability itself, computational sustainability provides methods to specify and intervene in sustainability problems. The most used approaches to computational sustainability systems target constraint conditions, computer simulation and machine learning to solve sustainability problems. Computer science can leverage computational sustainability to acquire relevant information from environment and users, plan and predict approaches to problems and act upon physical systems. This thesis presents an archetype platform, the People Help Energy Savings and Sustainability (PHESS), which results from experiments upon computational sustainability problems with the aid of action-research methodology. It is aimed at intelligent environments such as smart cites and ambient assisted living, and makes use of ubiquitous technologies, such as the Internet of Things (IoT) and pervasive computing. More than just measuring and reporting tool, the archetype aims to promote behavioural change and continuous improvement through techniques taken from fields such as intelligent environments, gamification and affective computing which help improve sustainability scenarios. This archetype enabled the implementation of case studies where the platform was used to assess energy consumption to manage and monitor user environments, user comfort and urban transportation to demonstrate the adaptability of the archetype to different kinds of scenarios.A sociedade depara-se, muitas vezes, com problemas de sustentabilidade. É um facto que a evolução económica e os comportamentos dos cidadãos estão a colocar pressão sobre os recursos naturais numa escala cada vez maior. A sociedade exige soluções sustentáveis para estes problemas. No entanto, estas soluções devem harmonizar restrições impostas pela sociedade, a física e os recursos. Estes fatores conduzem às dimensões tradicionais da sustentabilidade: económica, ambiental e social, que precisam ser tratadas como um todo, com o intuito de encontrar configurações sustentáveis. Embora não tão antiga quanto a própria sustentabilidade, a sustentabilidade computacional fornece métodos para especificar e intervir nos problemas de sustentabilidade. As abordagens mais usadas para sistemas computacionais de sustentabilidade abordam restrição de condições, simulação por computador e aprendizagem máquina para resolver problemas de sustentabilidade. A ciência da computação pode melhorar o desempenho da sustentabilidade computacional através da criação de informação relevante a partir do ambiente e seus utilizadores, planear e prever abordagens para os problemas e agir sobre sistemas físicos. Esta tese de doutoramento apresenta um arquétipo, o Pessoas Ajudam na Economia de Energia e na Sustentabilidade (PHESS People Help Energy Savings and Sustainability), que é o resultado de experiências sobre problemas de sustentabilidade computacional com o aUXIlio da metodologia de action-research. É destinada a ambientes inteligentes, como por exemplo cidades inteligentes e ambientes de vida assistida e faz uso de tecnologias ubíquas, tais como a Internet das Coisas (IoT - Internet of Things) e computação pervasiva. Mais do que apenas medir e elaborar relatórios, o arquétipo tem como objetivo promover a mudança de comportamentos e a melhoria contínua através de técnicas de ramos como ambientes inteligentes, gamification e computação afetiva que ajudam a melhorar cenários de sustentabilidade. Este arquétipo possibilitou a implementação de diversos casos de estudo onde a plataforma foi usada para gerir e monitorizar ambientes e utilizadores, o conforto dos utilizadores e transportes urbanos, para demonstrar a capacidade de adaptação do arquétipo a diferentes cenários reais

Methods and Sensors for Slip Detection in Robotics: A Survey

The perception of slip is one of the distinctive abilities of human tactile sensing. The sense of touch allows recognizing a wide set of properties of a grasped object, such as shape, weight and dimension. Based on such properties, the applied force can be accordingly regulated avoiding slip of the grasped object. Despite the great importance of tactile sensing for humans, mechatronic hands (robotic manipulators, prosthetic hands etc.) are rarely endowed with tactile feedback. The necessity to grasp objects relying on robust slip prevention algorithms is not yet corresponded in existing artificial manipulators, which are relegated to structured environments then. Numerous approaches regarding the problem of slip detection and correction have been developed especially in the last decade, resorting to a number of sensor typologies. However, no impact on the industrial market has been achieved. This paper reviews the sensors and methods so far proposed for slip prevention in artificial tactile perception, starting from more classical techniques until the latest solutions tested on robotic systems. The strengths and weaknesses of each described technique are discussed, also in relation to the sensing technologies employed. The result is a summary exploring the whole state of art and providing a perspective towards the future research directions in the sector

Sense and Respond

Over the past century, the manufacturing industry has undergone a number of paradigm shifts: from the Ford assembly line (1900s) and its focus on efficiency to the Toyota production system (1960s) and its focus on effectiveness and JIDOKA; from flexible manufacturing (1980s) to reconfigurable manufacturing (1990s) (both following the trend of mass customization); and from agent-based manufacturing (2000s) to cloud manufacturing (2010s) (both deploying the value stream complexity into the material and information flow, respectively). The next natural evolutionary step is to provide value by creating industrial cyber-physical assets with human-like intelligence. This will only be possible by further integrating strategic smart sensor technology into the manufacturing cyber-physical value creating processes in which industrial equipment is monitored and controlled for analyzing compression, temperature, moisture, vibrations, and performance. For instance, in the new wave of the ‘Industrial Internet of Things’ (IIoT), smart sensors will enable the development of new applications by interconnecting software, machines, and humans throughout the manufacturing process, thus enabling suppliers and manufacturers to rapidly respond to changing standards. This reprint of “Sense and Respond” aims to cover recent developments in the field of industrial applications, especially smart sensor technologies that increase the productivity, quality, reliability, and safety of industrial cyber-physical value-creating processes

A novel type of compliant and underactuated robotic hand for dexterous grasping

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The usefulness and versatility of a robotic end-effector depends on the diversity of grasps it can accomplish and also on the complexity of the control methods required to achieve them. We believe that soft hands are able to provide diverse and robust grasping with low control complexity. They possess many mechanical degrees of freedom and are able to implement complex deformations. At the same time, due to the inherent compliance of soft materials, only very few of these mechanical degrees have to be controlled explicitly. Soft hands therefore may combine the best of both worlds. In this paper, we present RBO Hand 2, a highly compliant, underactuated, robust, and dexterous anthropomorphic hand. The hand is inexpensive to manufacture and the morphology can easily be adapted to specific applications. To enable efficient hand design, we derive and evaluate computational models for the mechanical properties of the hand's basic building blocks, called PneuFlex actuators. The versatility of RBO Hand 2 is evaluated by implementing the comprehensive Feix taxonomy of human grasps. The manipulator's capabilities and limits are demonstrated using the Kapandji test and grasping experiments with a variety of objects of varying weight. Furthermore, we demonstrate that the effective dimensionality of grasp postures exceeds the dimensionality of the actuation signals, illustrating that complex grasping behavior can be achieved with relatively simple control