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检索条件"任意字段=Conference on Next-Generation Robots and Systems"

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A Dynamic Simulation of a Compliant Worm Robot Amenable to Neural Control 12th

A Dynamic Simulation of a Compliant Worm Robot Amenable to...

引用

Proceedings of the 12th International conference on Biomimetic and Biohybrid systems, Living Machines 2023

作者： Riddle, Shane Jackson, Clayton Daltorio, Kathryn A. Quinn, Roger D. Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106 United States

ISBN: (纸本)9783031388569

This paper details the development and validation of a dynamic 3D compliant worm-like robot model controlled by a Synthetic Nervous System (SNS). The model was built and simulated in the physics engine Mujoco which is able to approximate soft bodied dynamics and generate contact, gravitational, frictional, and internal forces. These capabilities allow the model to realistically simulate the movements and dynamic behavior of a physical soft-bodied worm-robot. For validation, the results of this simulation were compared to data gathered from a physical worm robot and found to closely match key behaviors such as deformation propagation along the compliant structure and actuator efficiency losses in the middle segments. The SNS controller was previously developed for a simple 2D kinematic model and has been successfully implemented on this 3D model with little alteration. It uses coupled oscillators to generate coordinated actuator control signals and induce peristaltic locomotion. This model will be useful for analyzing dynamic effects during peristaltic locomotion like contact forces and slip as well as developing and improving control algorithms that avoid unwanted slip. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： robots

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RGB-D-based Human Detection and Segmentation for Mobile Robot Navigation in Industrial Environments 16

RGB-D-based Human Detection and Segmentation for Mobile Robo...

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16th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 16th International conference on Computer Vision Theory and Applications (VISAPP)

作者： Kedilioglu, Oguz Lieret, Markus Schottenhamml, Julia Wurfl, Tobias Blank, Andreas Maier, Andreas Franke, Joerg Friedrich Alexander Univ Erlangen Nurnberg FAU Inst Factory Automat & Prod Syst D-91058 Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Pattern Recognit Lab D-91058 Erlangen Germany

ISBN: (纸本)9789897584886

Automated guided vehicles (AGV) are nowadays a common option for the efficient and automated in-house transportation of various cargo and materials. By the additional application of unmanned aerial vehicles (UAV) in the delivery and intralogistics sector this flow of materials is expected to be extended by the third dimension within the next decade. To ensure a collision-free movement for those vehicles optical, ultrasonic or capacitive distance sensors are commonly employed. While such systems allow a collision-free navigation, they are not able to distinguish humans from static objects and therefore require the robot to move at a human-safe speed at any time. To overcome these limitations and allow an environment sensitive collision avoidance for UAVs and AGVs we provide a solution for the depth camera based real-time semantic segmentation of workers in industrial environments. The semantic segmentation is based on an adapted version of the deep convolutional neural network (CNN) architecture FuseNet. After explaining the underlying methodology we present an automated approach for the generation of weakly annotated training data and evaluate the performance of the trained model compared to other well-known approaches.

关键词： Image Segmentation Object Recognition Neural Networks Deep Learning Robotics Autonomous Mobile robots Flexible Automation Warehouse Automation

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Edge Computing Architectures for Enabling the Realisation of the next generation Robotic systems

Edge Computing Architectures for Enabling the Realisation of...

引用

Mediterranean conference on Control and Automation (MED)

作者： Achilleas Santi Seisa Gerasimos Damigos Sumeet Gajanan Satpute Anton Koval George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Team Lule&#x00E5 University of Technology Lule&#x00E5 Ericsson Reasearch Lule&#x00E5

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Edge Computing is a promising technology to provide new capabilities in technological fields that require instantaneous data processing. Researchers in areas such as machine and deep learning use extensively edge and cloud computing for their applications, mainly due to the significant computational and storage resources that they provide. Currently, Robotics is seeking to take advantage of these capabilities as well, and with the development of 5G networks, some existing limitations in the field can be overcome. In this context, it is important to know how to utilize the emerging edge architectures, what types of edge architectures and platforms exist today and which of them can and should be used based on each robotic application. In general, Edge platforms can be implemented and used differently, especially since there are several providers offering more or less the same set of services with some essential differences. Thus, this study addresses these discussions for those who work in the development of the next generation robotic systems and will help to understand the advantages and disadvantages of each edge computing architecture in order to choose wisely the right one for each application.

关键词： Deep learning Computers 5G mobile communication Computer architecture Data processing Task analysis robots

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Enhancing Autonomy with Blockchain and Multi-Access Edge Computing in Distributed Robotic systems 5

Enhancing Autonomy with Blockchain and Multi-Access Edge Com...

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5th International conference on Fog and Mobile Edge Computing (FMEC)

作者： Queralta, Jorge Pena Li Qingqing Zou, Zhuo Westerlund, Tomi Univ Turku Fac Sciece & Engn Turku Intelligent Embedded & Robot Syst Lab Turku Finland Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781728172163

This conceptual paper discusses how different aspects involving the autonomous operation of robots and vehicles will change when they have access to next-generation mobile networks. 5G and beyond connectivity is bringing together a myriad of technologies and industries under its umbrella. High-bandwidth, low-latency edge computing services through network slicing have the potential to support novel application scenarios in different domains including robotics, autonomous vehicles, and the Internet of Things. In particular, multi-tenant applications at the edge of the network will boost the development of autonomous robots and vehicles offering computational resources and intelligence through reliable offloading services. The integration of more distributed network architectures with distributed robotic systems can increase the degree of intelligence and level of autonomy of connected units. We argue that the last piece to put together a services framework with third-party integration will be next-generation low-latency blockchain networks. Blockchains will enable a transparent and secure way of providing services and managing resources at the Multi-Access Edge Computing (MEC) layer. We overview the state-of-the-art in MEC slicing, distributed robotic systems and blockchain technology to define a framework for services the MEC layer that will enhance the autonomous operations of connected robots and vehicles.

关键词： Edge Computing Robotics 5G Computational Offloading Multi-Access Edge Computing Autonomous robots Network Slicing Mapping and localization Edge AI

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Enhancing Robotic Grasp Failure Prediction Using A Pre-hoc Explainability Framework*

Enhancing Robotic Grasp Failure Prediction Using A Pre-hoc E...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Cagla Acun Ali Ashary Dan O. Popa Olfa Nasraoui Dept. of Computer Science & Engineering Knowledge Discovery & Web Mining Lab Next Generation Systems Group

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Enhancing the explainability of machine learning (ML) models is crucial for bridging their use gap for practical applications of robotic autonomy. The common standard for adding explainability has been to use a post-hoc explanation approach. However, post-hoc approaches have recently attracted criticism for their lack of transparency, specifically because the post-hoc methods learn a surrogate model after the predictive model has already been learned, which means that the explanations are not authentically explaining the model’s behavior. This study aims to add explainability to the task of robot grasping failure prediction using Factorization Machines as the predictive model and using a novel pre-hoc explainability framework to learn an explainable Factorization Machine model. Unlike post-hoc methods, pre-hoc explainability starts with learning the explainable model before training the black-box model and then provides guidance while learning the latter to make predictions that are faithful to the explanations through a regularization mechanism. Through a detailed case study, we explore the trade-off between prediction accuracy and explanation fidelity and show that our framework is able to make predictions that are more accurate than an explainable white-box model while simultaneously learning a model whose pre-hoc explanations achieve a high level of fidelity relative to the predictions. Results show that our framework can predict the robustness of the grasp with 83% accuracy while explaining that increased effort exerted in Joint 2 of Finger 3 contributes tremendously to producing grasp failure, which is in contrast to increased efforts exerted at Joint 2 of Fingers 1 and 2 and at joint 1 of Finger 3, that all lead to reducing grasp failure.

关键词： Training Knowledge engineering Accuracy Fingers Grasping Predictive models Robustness robots Standards Optimization

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End-to-End Learning of Social Behaviors for Humanoid robots

End-to-End Learning of Social Behaviors for Humanoid Robots

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IEEE International conference on systems, Man, and Cybernetics (SMC)

作者： Ko, Woo-Ri Lee, Jaeyeon Jang, Minsu Kim, Jaehong Elect & Telecommun Res Inst Artificial Intelligence Res Lab Daejeon South Korea

ISBN: (纸本)9781728185262

Social robots should understand the user's nonverbal behavior and respond appropriately. Machine learning is one way of implementing the social intelligence. It provides the ability to automatically learn and improve from experience instead of explicitly telling the robot what to do. This paper proposes an end-to-end machine learning method to learn social behaviors for humanoid robots. We adapt sequence-to-sequence architecture consisting of two long short-term memory (LSTM) units. One is an LSTM encoder for encoding the previous sequence of human poses, and the other is an LSTM decoder for generating the next sequence of robot poses. The weights of the LSTMs are trained using human-human interaction data such as greeting and handshaking. The trained model is implemented in a humanoid robot, Pepper, to show its feasibility. Experimental results show that the robot can generate gestures appropriate to the situation and recognize subtle differences in user behavior. In addition, when a user's behavior changes, the transition to another behavior occurs naturally.

关键词： Social robot social behavior interaction behavior gesture generation deep learning

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A Tip Mount for Transporting Sensors and Tools using Soft Growing robots

A Tip Mount for Transporting Sensors and Tools using Soft Gr...

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IEEE/RSJ International conference on Intelligent robots and systems (IROS)

作者： Jeong, Sang-Goo Coad, Margaret M. Blumenschein, Laura H. Luo, Ming Mehmood, Usman Kim, Ji Hun Okamura, Allison M. Ryu, Jee-Hwan Stanford Univ Dept Mech Engn Stanford CA 94305 USA Korea Univ Technol & Educ Sch Mech Engn Cheonan Si South Korea Korea Adv Inst Sci & Technol Dept Civil & Environm Engn Daejeon South Korea

ISBN: (纸本)9781728162126

Pneumatically operated soft growing robots that extend via tip eversion are well-suited for navigation in confined spaces. Adding the ability to interact with the environment using sensors and tools attached to the robot tip would greatly enhance the usefulness of these robots for exploration in the field. However, because the material at the tip of the robot body continually changes as the robot grows and retracts, it is challenging to keep sensors and tools attached to the robot tip during actuation and environment interaction. In this paper, we analyze previous designs for mounting to the tip of soft growing robots, and we present a novel device that successfully remains attached to the robot tip while providing a mounting point for sensors and tools. Our tip mount incorporates and builds on our previous work on a device to retract the robot without undesired buckling of its body. Using our tip mount, we demonstrate two new soft growing robot capabilities: (1) pulling on the environment while retracting, and (2) retrieving and delivering objects. Finally, we discuss the limitations of our design and opportunities for improvement in future soft growing robot tip mounts.

关键词： Machine design

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Robotic Active Tactile Sensing Inspired by Serotonergic Modulation Using Active Inference 12th

Robotic Active Tactile Sensing Inspired by Serotonergic Mod...

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Proceedings of the 12th International conference on Biomimetic and Biohybrid systems, Living Machines 2023

作者： Novicky, Filip Offergeld, Joshua Janssen, Simon Lanillos, Pablo Donders Institute for Brain Cognition and Behavior Department of Neurophysics Radboud University Nijmegen Netherlands Donders Institute for Brain Cognition and Behavior Department of Artificial Intelligence Radboud University Nijmegen Netherlands Madrid Spain

ISBN: (纸本)9783031388569

When faced with uncertainty in the world, biological agents actively sense the environment to acquire the most informative input to fulfil their tasks. Actions are performed to adjust bodily sensors to maximize the collected information, which is usually known as active sensing. For instance, rodents continuously adjust the speed and amplitude of whisking to better identify objects and body location in space, which ultimately regulates navigation. Whilst, the internal mechanism that drives active sensing in humans is still under research, recent evidence points towards neuromodulators, such as serotonin, that influence whether the habitual behaviour is preferred over sensor adjustments to trigger exploration. Here, we present an active tactile-sensing model for a robot inspired by the serotonergic function viewed from the uncertainty minimization perspective. To mechanistically explain this neuromodulatory function, we associated it with precision parameters regulating habitual behaviour and tactile encoding based on previous findings. We qualitatively evaluated the model using an experiment inspired by the gap-crossing paradigm but tailored to a humanoid with tactile sensing. Behavioural switch timing results show the strong dependencies between active sensing and precision regulation. Ultimately, this work discusses how the neural microcircuitry regulates active sensing, hence opening future research of such neuromodulatory processes translated to robotics active sensing and perception. © 2023, The Author(s).

关键词： robots

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Hybrid Multi-Robot System for Drilling and Blasting Automation 16

Hybrid Multi-Robot System for Drilling and Blasting Automati...

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16th IEEE International conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Dac Dang Khoa Nguyen Lai, Yujun Sutjipto, Sheila Paul, Gavin Univ Technol Sydney UTS Fac Engn & Informat Technol Ctr Autonomous Syst CAS Sydney NSW Australia

ISBN: (纸本)9781728177090

Multi-robot systems possess the potential of becoming the next generation of robots in the mining industry due to their robustness and scalability. However, they present challenges for the system to efficiently allocate tasks to each robot and allow them to navigate toward their targets safely. This paper introduces a hybrid approach method for a multi-robot system, alongside with a case study in drilling and blasting automation. A Centralized Control Unit delegates tasks and information among the robots in the system, each equipped with a decentralized motion planner that supports cooperative inter-robot collision avoidance. The proposed system inherits the advantage of a centralized multi-robot system in providing a time-wise optimal solution;while also possessing the computational benefit and scalability of a decentralized system. Simulations were conducted to validate the proposed method and discuss insights into the efficacy and performance of the proposed method.

关键词： Multipurpose robots

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SAM IoT 2020 - Proceedings of the 1st Eclipse Research International conference on Security, Artificial Intelligence and Modeling for the next generation Internet of Things

SAM IoT 2020 - Proceedings of the 1st Eclipse Research Inter...

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1st Eclipse Research International conference on Security, Artificial Intelligence and Modeling for the next generation Internet of Things, SAM IoT 2020

The proceedings contain 11 papers. The topics discussed include: risk assessment in IoT case study: collaborative robots system;integrated solution for industrial IoT data security - the CHARIOT solution;guidelines for privacy and security in IoT;semantic models for network intrusion detection;smart building energy and comfort management based on sensor activity recognition and prediction;prescriptive system for reconfigurable manufacturing systems considering variable demand and production rates;a cross-platform communication mechanism for ROS-based cyber-physical system;REPLICA: a solution for next generation IoT and digital twin based fault diagnosis and predictive maintenance;DINASORE: a dynamic intelligent reconfiguration tool for cyber-physical production systems;bringing clouds down to earth: modeling arrowhead deployments via eclipse Vorto;and model based methodology and framework for design and management of next-gen IoT systems.

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