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检索条件"机构=BioRobotics Institute and with the Department of Excellence in Robotics & AI"

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Smell Driven Navigation for Soft Robotic Arms: Artificial Nose and Control

Smell Driven Navigation for Soft Robotic Arms: Artificial No...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Francesco Piqué Francesco Stella Josie Hughes Egidio Falotico Cosimo Della Santina The BioRobotics Insitute Scuola Superiore Sant'Anna Pisa Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy EPFL CREATE Lab Lausanne Switzerland Department of Cognitive Robotics Delft University of Technology Delft The Netherlands the Institute of Robotics and Mechatronics German Aerospace Center (DLR) Wessling Germany

Elephants and other animals heavily rely on the sense of smell to operate. Soft robots would also benefit from an artificial sense of smell, which could be helpful in typical soft robotic tasks such as search and rescue, pipe inspection, and all the tasks involving unstructured environments. This work proposes an artificial nose on a soft robotic arm that ensures separate smell concentration readings. We propose designing the nose to generate a one-to-one matching between the sensors' inputs and the actuators. This design choice allows us to implement a simple control strategy tailored to reach a dynamically varying smell in the environment, which we validate on a two-segment tendon-driven soft robotic arm equipped with the proposed artificial nose. We also propose and validate in simulation a control strategy for reaching tasks in the case of a stationary smell.

关键词： Actuators Navigation Animals Olfactory Soft robotics Inspection Hardware

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Development of an Autonomous Fish-Inspired Robotic Platform for Aquaculture Inspection and Management

Development of an Autonomous Fish-Inspired Robotic Platform ...

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IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

作者： Gianluca Manduca Luca Padovani Edoardo Carosio Giorgio Graziani Cesare Stefanini Donato Romano The BioRobotics Institute & Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa Italy Department of Mechanical and Aerospace Engineering Sapienza University CNR-INM Marine Technology Research Institute Rome Italy Department of Mechanical and Aerospace Engineering Sapienza University Rome Italy

Aquaculture applications are increasingly utilizing precision techniques such as computer vision technologies to perform a variety of inspection tasks. This work presents the development of three activities essential for the creation of a biomimetic robotic platform with onboard intelligence and autonomous task execution capabilities. The proposed robot is inspired by carangiform movement and achieves various trajectories through a magnetic actuation system with a single motor for propulsion. Fluid dynamics studies can improve the performance of the proposed propulsion system, thus ensuring greater energy efficiency. Thanks to its modular and scalable structure, the platform can integrate different components such as a vision system. The investigated vision-based model shows promising results for deployment in marine environments and can be adapted to detect various marine species. This fish-inspired robot platform has potential applications in the sustainable inspection and management of aquaculture facilities.

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Whole-arm Grasping Strategy for Soft Arms to Capture Space Debris

Whole-arm Grasping Strategy for Soft Arms to Capture Space D...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Camilla Agabiti Etienne Ménager Egidio Falotico BioRobotics Institute Scuola Superiore Sant'Anna Pontedera Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy Centrale Lille UMR 9189 CRIStAL Université de Lille Inria CNRS Lille France

In this work, we present a whole-arm grasping strategy for soft arms whose task is to capture space debris. The non-cooperative nature of space debris and the characteristics of the space environment enforce high-level requirements for robotic arms, especially dexterity. Taking inspiration from the outstanding capabilities of the elephant trunk in grasping, we formulated a grasping strategy based upon the identification of contact points on the object to force the bending of the arm and induce the wrapping around the object, as the animal model does. This strategy is implemented by leveraging on coupled Finite Element simulations of a trunk-like soft arm and Reinforcement Learning tools to learn the grasping. The results show that the robot successfully learns the task by moving the proximal part closer to the object and using the distal one to wrap around the object. We show that the obtained policy is valid for diverse object sizes and positions. Our grasping strategy is the first example of bio-inspired whole-arm grasping for a soft arm in space. We believe that, in the near future, this strategy will enable new grasping capabilities in soft arms.

关键词： Deformation Space debris Grasping Reinforcement learning Bending Manipulators Finite element analysis

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A ROS 2 Response-Time Analysis Exploiting Starvation Freedom and Execution-Time Variance 42

A ROS 2 Response-Time Analysis Exploiting Starvation Freedom...

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Blaß, Tobias Casini, Daniel Bozhko, Sergey Brandenburg, Björn B. Germany TeCIP Institute Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa Italy Germany

ISBN: (纸本)9781665428026

Robots are commonly subject to real-time constraints. To ensure that such constraints are met, recent work has analyzed the response times of processing chains under ROS 2, a popular robotics framework. However, prior work supports only scalar worst-case execution time bounds and does not exploit that the ROS 2 scheduling mechanism is starvation-free. This paper proposes a novel response-time analysis for ROS 2 processing chains that accounts for both the high execution-time variance typically encountered in robotics workloads and the starvation freedom of the default ROS 2 callback scheduler. Experimental results from both synthetic callback graphs and a real ROS 2 workload empirically show the proposed analysis to be much more accurate (often by a factor of 2× or more). © 2021 IEEE

关键词： robotics

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Prototyping a sensorized tool wristband for objective skill assessment and feedback during training in minimally invasive surgery

Prototyping a sensorized tool wristband for objective skill ...

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International Symposium on Medical robotics (ISMR)

作者： A. Mariani M. Conti S. Gandah C. Galli de Paratesi A. Menciassi Department of Excellence in Robotics & AI of Scuola Superiore Sant'Anna The BioRobotics Institute Pisa Italy

ISBN: (纸本)9781665406239

Skill assessment is a key component of surgical practical training. Towards an objective, automatic and cost-effective skill evaluation, this work introduces a preliminary sensorized wristband as a training add-on for standard minimally invasive surgical tools. The prototype herein presented allows to classify the presence of the tool in the camera field of view, as well as to provide feedback accordingly. A usability study on 14 non-medical participants was carried out using the da Vinci Research Kit. Results demonstrated the classification accuracy of the method and the usefulness of the feedback to minimize the time spent with the tool out of the field of view. Embedding additional sensors and testing usability on surgical residents will pave the way towards the evolution of this proof of concept to an advanced prototype to use in a real training setting.

关键词： Training Minimally invasive surgery Prototypes Robot sensing systems Cameras Sensors Usability

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Learning a Controller for Soft Robotic Arms and Testing its Generalization to New Observations, Dynamics, and Tasks

Learning a Controller for Soft Robotic Arms and Testing its ...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Carlo Alessi Helmut Hauser Alessandro Lucantonio Egidio Falotico The BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy Department of Engineering Mathematics University of Bristol Bristol UK Department of Mechanical and Production Engineering Aarhus University Aarhus Denmark

Recently, learning-based controllers that leverage mechanical models of soft robots have shown promising results. This paper presents a closed-loop controller for dynamic trajectory tracking with a pneumatic soft robotic arm learned via Deep Reinforcement Learning using Proximal Policy Optimization. The control policy was trained in simulation leveraging a dynamic Cosserat rod model of the soft robot. The generalization capabilities of learned controllers are vital for successful deployment in the real world, especially when the encountered scenarios differ from the training environment. We assessed the generalization capabilities of the controller in silico for four tests. The first test involved the dynamic tracking of trajectories that differ significantly in shape and velocity profiles from the training data. Second, we evaluated the robustness of the controller to perpetual external end-point forces for dynamic tracking. For tracking tasks, it was also assessed the generalization to similar materials. Finally, we transferred the control policy without retraining to intercept a moving object with the end-effector. The learned control policy has shown good generalization capabilities in all four tests.

关键词： Deep learning Trajectory tracking Heuristic algorithms Dynamics Reinforcement learning Soft robotics End effectors

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The MONITOR Robot with UHF-RFID Rotating Antennas enhancing Indoor Tag Localization 8

The MONITOR Robot with UHF-RFID Rotating Antennas enhancing ...

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8th International Conference on Smart and Sustainable Technologies, SpliTech 2023

作者： Cecchi, Glauco Motroni, Andrea Buffi, Alice Nepa, Paolo D'Avella, Salvatore Tripicchio, Paolo Unetti, Matteo Col, Luca Del Salvatore, Alfredo University of Pisa Department of Information Engineering Pisa Italy University of Pisa Department of Energy Systems Territory and Construction Engineering Pisa Italy Institute Scuola Superiore Sant'Anna Department of Excellence in Robotics and AI Mechanical Intelligence Pisa Italy Partitalia Srl Milan Italy Sensorid Srl Campobasso Italy

ISBN: (纸本)9789532901283

The MONITOR Project is born to respond to the increasing demand for highly accurate and quick inventorying operations. For this purpose, an automatic Ultra-High-Frequency (UHF) Radio Frequency IDentification (RFID) system consisting of a mobile robot is conceived. Particularly, the robot is equipped with rotating arms allowing an antenna motion able to improve tag localization when Synthetic Aperture Radar (SAR) methods are applied. In this paper, the proof-of-concept of the mobile robot with rotating arms is firstly presented. Localization performance is shown in a realistic warehouse/store environment where RFID tags are attached to clothes arranged on dress racks. Also stray tags are considered in the indoor scenario, to make the environment more challenging. The experimental analysis demonstrates the system capability in improving localization performance thanks to rotating antennas. © 2023 University of Split, FESB.

关键词： Synthetic aperture radar

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Calibration of a biophysical model to replicate evoked compound action potentials in the swine vagus nerve 46

Calibration of a biophysical model to replicate evoked compo...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Verardo, Claudio Zinno, Ciro Romeni, Simone Agnesi, Filippo Bernini, Fabio Dushpanova, Anar Casieri, Valentina Lionetti, Vincenzo Micera, Silvestro The BioRobotics Institute Department of Excellence in Robotics & Ai Scuola Superiore Sant'Anna Pisa Italy Bertarelli Foundation Neuro-X Institute Epfl Genève Switzerland BioMedLab Interdisciplinary Research Center 'Health Science' Scuola Superiore Sant'Anna Pisa Italy Unit of Translational Critical Care Medicine Laboratory of Basic and Applied Medical Sciences Interdisciplinary Research Center 'Health Science' Scuola Superiore Sant'Anna Pisa Italy Al-Farabi Kazakh National University Almaty Kazakhstan Fondazione Toscana 'G. Monasterio' Pisa Italy

ISBN: (纸本)9798350371499

Vagus nerve stimulation (VNS) is a promising application of bioelectronic medicine to treat many pathologies, ranging from epilepsy and depression to cardiovascular diseases. Conventional VNS is not optimized taking into account the topographic organization of the vagus nerve, resulting in suboptimal stimulation protocols, which can lead to severe adverse effects. The development of in vivo methods to determine topographic organization would allow more selective stimulation protocols and is thus pivotal in the development of future therapies. Here, we show that it may be possible to reverse-engineer vagus nerve topographic organization starting from experimental evoked compound action potentials (ECAPs). The parameters of a biophysical model of ECAP generation are varied until the optimal matching between synthetic and experimental ECAPs is determined. In the present work, we managed to match an experimental ECAP obtained from swine VNS, and to replicate the shape of an unseen experimental ECAP through the optimized parameters. This work paves the way to the automatic optimization of selective VNS protocols, opening to new important therapeutic opportunities. © 2024 IEEE.

关键词： Neurons

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Identifying Synthetic Voices’ Qualities for Conversational Agents 2nd

Identifying Synthetic Voices’ Qualities for Conversational ...

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2nd International Conference on Applied Intelligence and Informatics, aiI 2022

作者： Cuciniello, Marialucia Amorese, Terry Cordasco, Gennaro Marrone, Stefano Marulli, Fiammetta Cavallo, Filippo Gordeeva, Olga Carrión, Zoraida Callejas Esposito, Anna Department of Psychology Università degli Studi della Campania "Luigi Vanvitelli" and IIASS Caserta and Vietri Sul Mare Italy Università degli Studi della Campania "Luigi Vanvitelli" Caserta Italy Department of Industrial Engineering BioRobotics Institute SS Sant’Anna and Department of Excellence in Robotics & AI Florence University Florence Italy Acapela Group Mons Belgium Universidad de Granada Granada Spain

ISBN: (纸本)9783031248009

The present study aims to explore user’ acceptance and perceptions toward different quality levels of synthetical voices. To achieve this, four voices have been exploited considering two main factors: the quality of the voices (low vs high) and their gender (male and female). 186 volunteers were recruited and subsequently allocated into four groups of different ages respectively, adolescents, young adults, middle-aged and seniors. After having randomly listened to each voice, participants were asked to fill the Virtual Agent Voice Acceptance Questionnaire (VAVAQ). Outcomes show that the two higher quality voices of Antonio and Giulia were more appreciated than the low-quality voices of Edoardo and Clara by the whole sample in terms of pragmatic, hedonic and attractiveness qualities attributed to the voices. Concerning preferences towards differently aged voices, it clearly appeared that they varied according to participants age’ ranges examined. Furthermore, in terms of suitability to perform different tasks, participants considered Antonio and Giulia equally adapt for healthcare and front office jobs. Antonio was also judged to be significantly more qualified to accomplish protection and security tasks, while Edoardo was classified as the absolute least skilled in conducting household chores. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Continual Policy Distillation of Reinforcement Learning-based Controllers for Soft Robotic In-Hand Manipulation

Continual Policy Distillation of Reinforcement Learning-base...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Lanpei Li Enrico Donatot Vincenzo Lornonaco Egidio Falotico Department of Computer Science University of Pisa Pisa Italy Institute of Information Science and Technologies (ISTI) National Research Council (CNR) Pisa Italy The BioRobotics Institute Sant’ Anna School of Advanced Studies Pontedera (PI) Italy Departement of Excellence in Robotics & AI Sant’ Anna School of Advanced Studies Pisa Italy

Dexterous manipulation, often facilitated by multi-fingered robotic hands, holds solid impact for real-world ap-plications. Soft robotic hands, due to their compliant nature, offer flexibility and adaptability during object grasping and manipulation. Yet, benefits come with challenges, particularly in the control development for finger coordination. Reinforce-ment Learning (RL) can be employed to train object-specific in-hand manipulation policies, but limiting adaptability and generalizability. We introduce a Continual Policy Distillation (CPD) framework to acquire a versatile controller for in-hand manipulation, to rotate different objects in shape and size within a four-fingered soft gripper. The framework leverages Policy Distillation (PD) to transfer knowledge from expert policies to a continually evolving student policy network. Exemplar-based rehearsal methods are then integrated to mitigate catastrophic forgetting and enhance generalization. The performance of the CPD framework over various replay strategies demonstrates its effectiveness in consolidating knowledge from multiple experts and achieving versatile and adaptive behaviours for in-hand manipulation tasks.

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