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Design and Control of Modular Soft-Rigid Hybrid Manipulators with Self-Contact

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arXiv 2024年

作者： Patterson, Zach J. Sologuren, Emily Della Santina, Cosimo Rus, Daniela Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States Department of Cognitive Robotics Delft University of Technology Delft Netherlands Wessling Germany

Soft robotics focuses on designing robots with highly deformable materials, allowing them to adapt and operate safely and reliably in unstructured and variable environments. While soft robots offer increased compliance over rigid body robots, their payloads are limited, and they consume significant energy when operating against gravity in terrestrial environments. To address the carrying capacity limitation, we introduce a novel class of soft-rigid hybrid robot manipulators (SRH) that incorporates both soft continuum modules and rigid joints in a serial configuration. The SRH manipulators can seamlessly transition between being compliant and delicate to rigid and strong, achieving this through dynamic shape modulation and employing self-contact among rigid components to effectively form solid structures. We discuss the design and fabrication of SRH robots, and present a class of novel control algorithms for SRH systems. We propose a configuration space PD+ shape controller and a Cartesian impedance controller, both of which are provably stable, endowing the soft robot with the necessary low-level capabilities. We validate the controllers on SRH hardware and demonstrate the robot performing several tasks. Our results highlight the potential for the soft-rigid hybrid paradigm to produce robots that are both physically safe and effective at task performance. © 2024, CC BY.

关键词： Machine design

GV-Bench: Benchmarking Local Feature Matching for Geometric Verification of Long-term Loop Closure Detection

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arXiv 2024年

作者： Yu, Jingwen Ye, Hanjing Jiao, Jianhao Tan, Ping Zhang, Hong The Hong Kong University of Science and Technology Hong Kong Hong Kong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Computer Science University College London London United Kingdom

关键词： Geometry

Modeling and Control of Intrinsically Elasticity Coupled Soft-Rigid Robots

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arXiv 2023年

作者： Patterson, Zach J. Della Santina, Cosimo Rus, Daniela Computer Science and Artificial Intelligence Laboratory MIT United States Department of Cognitive Robotics Delft University of Technology Netherlands Germany

While much work has been done recently in the realm of model-based control of soft robots and soft-rigid hybrids, most works examine robots that have an inherently serial structure. While these systems have been prevalent in the literature, there is an increasing trend toward designing soft-rigid hybrids with intrinsically coupled elasticity between various degrees of freedom. In this work, we seek to address the issues of modeling and controlling such structures, particularly when underactuated. We introduce several simple models for elastic coupling, typical of those seen in these systems. We then propose a controller that compensates for the elasticity, and we prove its stability with Lyapunov methods without relying on the elastic dominance assumption. This controller is applicable to the general class of underactuated soft robots. After evaluating the controller in simulated cases, we then develop a simple hardware platform to evaluate both the models and the controller. Finally, using the hardware, we demonstrate a novel use case for underactuated, elastically coupled systems in "sensorless" force control. Copyright © 2023, The Authors. All rights reserved.

关键词： Controllers

Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

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Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

Cooperative vs. Teleoperation Control of the Steady Hand Eye Robot with Adaptive Sclera Force Control: A Comparative Study

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Cooperative vs. Teleoperation Control of the Steady Hand Eye...

IEEE International Conference on robotics and Automation (ICRA)

作者： Mojtaba Esfandiari Ji Woong Kim Botao Zhao Golchehr Amirkhani Muhammad Hadi Peter Gehlbach Russell H. Taylor Iulian Iordachita Department of Mechanical Engineering Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Johns Hopkins Hospital Wilmer Eye Institute Baltimore MD USA Department of Computer Science Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

A surgeon’s physiological hand tremor can significantly impact the outcome of delicate and precise retinal surgery, such as retinal vein cannulation (RVC) and epiretinal membrane peeling. Robot-assisted eye surgery technology provides ophthalmologists with advanced capabilities such as hand tremor cancellation, hand motion scaling, and safety constraints that enable them to perform these otherwise challenging and high-risk surgeries with high precision and safety. Steady-Hand Eye Robot (SHER) with cooperative control mode can filter out surgeon’s hand tremor, yet another important safety feature, that is, minimizing the contact force between the surgical instrument and sclera surface for avoiding tissue damage cannot be met in this control mode. Also, other capabilities, such as hand motion scaling and haptic feedback, require a teleoperation control framework. In this work, for the first time, we implemented a teleoperation control mode incorporated with an adaptive sclera force control algorithm using a PHANTOM Omni haptic device and a force-sensing surgical instrument equipped with Fiber Bragg Grating (FBG) sensors attached to the SHER 2.1 end-effector. This adaptive sclera force control algorithm allows the robot to dynamically minimize the toolsclera contact force. Moreover, for the first time, we compared the performance of the proposed adaptive teleoperation mode with the cooperative mode by conducting a vessel-following experiment inside an eye phantom under a microscope.

关键词： Tissue damage Heuristic algorithms Veins Force Surgery Phantoms Retina

Embodied-AI Wheelchair Framework with Hands-free Interface and Manipulation

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Embodied-AI Wheelchair Framework with Hands-free Interface a...

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Leaman, Jesse F. Yang, Zongming Elglaly, Yasmine N. La, Hung Li, Bing Automotive Engineering Department GreenvilleSC United States Gryphon Foundation for People with Disadvantages Western Washington University Computer Science Department United States University of Nevada Advanced Robotics and Automation Laboratory Reno United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Assistive robots can be found in hospitals and rehabilitation clinics, where they help patients maintain a positive disposition. Our proposed robotic mobility solution combines state of the art hardware and software to provide a safer, more independent, and more productive lifestyle for people with some of the most severe disabilities. New hardware includes, a retractable roof, manipulator arm, a hard backpack, a number of sensors that collect environmental data and processors that generate 3D maps for a hands-free human-machine *** proposed new system receives input from the user via head tracking or voice command, and displays information through augmented reality into the user's field of view. The software algorithm will use a novel cycle of self-learning artificial intelligence that achieves autonomous navigation while avoiding collisions with stationary and dynamic objects. The prototype will be assembled and tested over the next three years and a publicly available version could be ready two years thereafter. © 2022 IEEE.

关键词： Wheelchairs

Dynamic Subgoal Based Path Formation and Task Allocation: A NeuroFleets Approach to Scalable Swarm robotics

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Dynamic Subgoal Based Path Formation and Task Allocation: A ...

IEEE International Conference on Systems, Man and Cybernetics

作者： Robinroy Peter Lavanya Ratnabala Eugene Yugarajah Andrew Charles Dzmitry Tsetserukou NeuroFleets (PVT) LTD Jaffna Sri Lanka Intelligent Space Robotics Laboratory Skolkovo Institute of Science and Technology Moscow Russia Department of Computer Science University of Jaffna Jaffna Sri Lanka

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This paper addresses the challenges of exploration and navigation in unknown environments from the perspective of evolutionary swarm robotics. A key focus is on path formation, which is essential for enabling cooperative swarm robots to navigate effectively. We designed the task allocation and path formation process based on a finite state machine, ensuring systematic decision-making and efficient state transitions. The approach is decentralized, allowing each robot to make decisions independently based on local information, which enhances scalability and robustness. We present a novel subgoal-based path formation method that establishes paths between locations by leveraging visually connected subgoals. Simulation experiments conducted in the Argos simulator show that this method successfully forms paths in the majority of trials. However, inter-collision (traffic) among numerous robots during path formation can negatively impact performance. To address this issue, we propose a task allocation strategy that uses local communication protocols and light signal-based communication to manage robot deployment. This strategy assesses the distance between points and determines the optimal number of robots needed for the path formation task, thereby reducing unnecessary exploration and traffic congestion. The performance of both the subgoal-based path formation method and the task allocation strategy is evaluated by comparing the path length, time, and resource usage against the A * algorithm. Simulation results demonstrate the effectiveness of our approach, highlighting its scalability, robustness, and fault tolerance.

关键词： Systematics Protocols Navigation Scalability Simulation Heuristic algorithms Swarm robotics Robustness Resource management Traffic congestion

A computer Vision Approach to Plant Growth Monitoring in an Embedded CubeSat Module

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A Computer Vision Approach to Plant Growth Monitoring in an ...

IEEE Conference on Aerospace

作者： Alexis Lopez Olaoluwayimika Olugbenle Michael C.F. Bazzocchi Department of Mechanical and Aerospace Engineering Astronautics and Robotics Laboratory (ASTRO Lab) Clarkson University Potsdam NY USA Department of Electrical and Computer Engineering Astronautics and Robotics Laboratory (ASTRO Lab) Clarkson University Potsdam NY USA Department of Mechanical and Aerospace Engineering Astronautics and Robotics Laboratory (ASTRO Lab) Clarkson University Potsdam NY Department of Earth and Space Science and Engineering York University Toronto Canada

The investigation and development of space-based food production systems are essential to improve the reliability and availability of fresh sustenance for astronauts. With their compact size and low-cost production, CubeSats can provide a unique platform for plant-based in-space experiments. Additionally, the combination of CubeSats and computer vision can allow for monitoring the health of plants during their growth cycles. This paper investigates the electronics and data handling of a crop growth module, otherwise referred to as an environmental monitoring and control subsystem (EMCS), as well as the integration of computer vision techniques for plant growth and development. Using the Otsu thresholding and holistically-nested edge detection algorithms, image segmentation and edge detection were performed, respectively. A support vector machine (SVM) was also employed to classify foliage and provide feedback on the plant’s health. The results from the system show that the computer vision approach can accurately predict the health of the plants based on color and texture. This study builds a foundation for future plant health monitoring research in deep space environments.

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HeRo 2.0: A Low-Cost Robot for Swarm robotics Research

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arXiv 2022年

作者： Rezeck, Paulo Azpúrua, Héctor Corrêa, Maurício F.S. Chaimowicz, Luiz Graduate Program in Computer Science Computer Vision and Robotics Laboratory Department of Computer Science Universidade Federal de Minas Gerais Brazil Instituto Tecnológico Vale MG Ouro Preto Brazil Vision and Robotics Laboratory Department of Computer Science Universidade Federal de Minas Gerais Brazil Computer Vision and Robotics Laboratory Department of Computer Science Universidade Federal de Minas Gerais Brazil

The current state of electronic component miniaturization coupled with the increasing efficiency in hardware and software allow the development of smaller and compact robotic systems. The convenience of using these small, simple, yet capable robots has gathered the research community's attention towards practical applications of swarm robotics. This paper presents the design of a novel platform for swarm robotics applications that is low cost, easy to assemble using off-the-shelf components, and deeply integrated with the most used robotic framework available today: ROS (Robot Operating System). The robotic platform is entirely open, composed of a 3D printed body and open-source software. We describe its architecture, present its main features, and evaluate its functionalities executing experiments using a couple of robots. Results demonstrate that the proposed mobile robot is very effective given its small size and reduced cost, being suitable for swarm robotics research and education. © 2022, CC BY.

关键词： Swarm intelligence