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ZodiAq: An Isotropic Flagella-Inspired Soft Underwater Drone for Safe Marine Exploration

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arXiv 2025年

作者： Mathew, Anup Teejo Feliu-Talegon, Daniel Adamu, Yusuf Abdullahi Hmida, Ikhlas Ben Armanini, Costanza Stefanini, Cesare Seneviratne, Lakmal Renda, Federico Department of Mechanical Engineering Khalifa University Abu Dhabi United Arab Emirates New York University Abu Dhabi United Arab Emirates Department of Excellence in Robotics and AI The Biorobotics Institute Scuola Superiore Sant’Anna Pisa56127 Italy Khalifa University Abu Dhabi United Arab Emirates

The inherent challenges of robotic underwater exploration, such as hydrodynamic effects, the complexity of dynamic coupling, and the necessity for sensitive interaction with marine life, call for the adoption of soft robotic approaches in marine exploration. To address this, we present a novel prototype, ZodiAq, a soft underwater drone inspired by prokaryotic bacterial flagella. ZodiAq’s unique dodecahedral structure, equipped with 12 flagella-like arms, ensures design redundancy and compliance, ideal for navigating complex underwater terrains. The prototype features a central unit based on a Raspberry Pi, connected to a sensory system for inertial, depth, and vision detection, and an acoustic modem for communication. Combined with the implemented control law, it renders ZodiAq an intelligent system. This paper details the design and fabrication process of ZodiAq, highlighting design choices and prototype capabilities. Based on the strain-based modeling of Cosserat rods, we have developed a digital twin of the prototype within a simulation toolbox to ease analysis and control. To optimize its operation in dynamic aquatic conditions, a simplified model-based controller has been developed and implemented, facilitating intelligent and adaptive movement in the hydrodynamic environment. Extensive experimental demonstrations highlight the drone’s potential, showcasing its design redundancy, embodied intelligence, crawling gait, and practical applications in diverse underwater settings. This research contributes significantly to the field of underwater soft robotics, offering a promising new avenue for safe, efficient, and environmentally conscious underwater exploration. © 2025, CC BY.

关键词： Redundancy

EI-Drive: A Platform for Cooperative Perception With Realistic Communication Models

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IEEE Internet of Things Journal 2025年

作者： Zhou, Hanchu Xie, Edward Shao, Wei Gao, Dechen Dong, Michelle Zhang, Junshan University of California Davis AI Robotics and Edge Laboratory DavisCA95616 United States Electrical and Computer Engineering Department of Johns Hopkins University BaltimoreMD21218 United States Csiro data61 ClaytonVIC Australia Monta Vista High School CupertinoCA95014 United States

The growing interest in autonomous driving calls for realistic simulation platforms capable of accurately simulating cooperative perception process in realistic traffic scenarios. Existing studies for cooperative perception often have not accounted for transmission latency and errors in real-world environments. To address this gap, we introduce EI-Drive (Edge Intelligent Drive), an Edge-ai based autonomous driving simulation platform that integrates advanced cooperative perception with more realistic communication models. Built on the CARLA framework, EI-Drive features new modules for cooperative perception while taking into account transmission latency and errors, providing a more realistic platform for evaluating cooperative perception algorithms. In particular, the platform enables vehicles to fuse data from multiple sources, improving situational awareness and safety in complex environments. With its modular design, EI-Drive allows for detailed exploration of sensing, perception, planning, and control in various cooperative driving scenarios. Experiments using EI-Drive demonstrate significant improvements in vehicle safety and performance, particularly in scenarios with complex traffic flow and network conditions. All code and documents are accessible on our GitHub page: https://***/***/. © 2014 IEEE.

关键词： Digital elevation model

On-Device Neural Architecture Search

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On-Device Neural Architecture Search

IEEE International Conference on Consumer Electronics (ICCE)

作者： Andrea Mattia Garavagno Edoardo Ragusa Paolo Gastaldo Antonio Frisoli Claudio Loconsole Department of Electrical Electronic Telecommunication Engineering and Naval Architecture DITEN University of Genoa Genoa Italy Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa Italy Institute of Mechanical Intelligence Scuola Superiore Sant'Anna Pisa Italy Faculty of Technological and Innovation Sciences Universitas Mercatorum Rome Italy

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best tiny neural architecture for analyzing the real-time data acquired through sensors. This new adaptation capability can be particularly useful in the case of human-machine interfaces for which the neural network analyzing the biometrical data can be re-designed each time the user changes, after a guided data collection procedure, fighting the typical data variations between individuals on a new level. To implement the proposed approach a new NAS has been designed and then validated on the Italian Sign Language dataset (ISL), a collection of surface electromyography (sEMG) signals of the signs of the Italian alphabet, using several embedded systems. Moreover, further validation on the Case Western Reserve University dataset (CWRU), a benchmark for intelligent fault diagnosis, is presented to suggest another possible application of the proposed approach. When run on a Raspberry Pi 4, the proposed NAS performs beyond the state of the art proposing a tiny neural architecture having 0.63 times less RAM occupancy and 5.96 percentage points of more accuracy in the case of the ISL dataset; and 0.44 times less RAM occupancy and 0.2 percentage points of more accuracy in the case of the CWRU dataset.

关键词： Performance evaluation Fault diagnosis Biometrics Sign language Costs Human-machine systems Random access memory Computer architecture Real-time systems Neural architecture search

Safe docking of a payload-carrying spacecraft using state constrained adaptive control

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Control engineering Practice 2025年 162卷

作者： Sankaranarayanan, Viswa Narayanan Banerjee, Avijit Satpute, Sumeet Nikolakopoulos, George Robotics and AI Team Department of Computer Science Electrical Engineering and Space Engineering Lulea University of Technology Lulea Sweden

In this article, we design an adaptive controller for the position and heading control for a payload-carrying spacecraft to perform docking with a target docking station. We address the problem by identifying the state constraints required to safely dock the spacecraft and imposing these constraints on an adaptive tracking controller. To make the controller adapt to different types of payloads, the adaptive controller is designed without any explicit a priori knowledge of the system dynamics or bound for the uncertainties. Furthermore, to accommodate a wide range of initial conditions, the constraints are chosen to be time-varying. Thus, unlike conventional controllers, the proposed controller enforces the safety of the spacecraft during docking by imposing state constraints while adapting to unknown drastic dynamic variations. The controller is validated in simulation for docking a 6 DoF spacecraft in the orbital space. Additionally, for technology readiness, we have performed the hardware validation of the controller using a payload-carrying planar floating robot and a prototype docking station. Compared to the state-of-the-art controllers, the proposed controller guarantees predefined time-varying state constraints while significantly improving the performance. The video of the experimental results is presented here: https://***/tJtJBibzHhI. © 2025 The Author(s)

关键词： Self adjusting control systems

ReBot: Scaling Robot Learning with Real-to-Sim-to-Real Robotic Video Synthesis

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arXiv 2025年

作者： Fang, Yu Yang, Yue Zhu, Xinghao Zheng, Kaiyuan Bertasius, Gedas Szafir, Daniel Ding, Mingyu Department of Computer Science University of North Carolina at Chapel Hill 201 S Columbia St Chapel HillNC27599 United States Robotics and AI Institute 145 Broadway CambridgeMA02142 United States Department of Electrical and Computer Engineering University of Washington 1410 NE Campus Parkway SeattleWA98195 United States

Vision-language-action (VLA) models present a promising paradigm by training policies directly on real robot datasets like Open X-Embodiment. However, the high cost of real-world data collection hinders further data scaling, thereby restricting the generalizability of VLAs. In this paper, we introduce ReBot, a novel real-to-sim-to-real approach for scaling real robot datasets and adapting VLA models to target domains, which is the last-mile deployment challenge in robot manipulation. Specifically, ReBot replays real-world robot trajectories in simulation to diversify manipulated objects (real-to-sim), and integrates the simulated movements with inpainted real-world background to synthesize physically realistic and temporally consistent robot videos (sim-to-real). Our approach has several advantages: 1) it enjoys the benefit of real data to minimize the sim-to-real gap;2) it leverages the scalability of simulation;and 3) it can generalize a pretrained VLA to a target domain with fully automated data pipelines. Extensive experiments in both simulation and real-world environments show that ReBot significantly enhances the performance and robustness of VLAs. For example, in SimplerEnv with the WidowX robot, ReBot improved the in-domain performance of Octo by 7.2% and OpenVLA by 21.8%, and out-of-domain generalization by 19.9% and 9.4%, respectively. For real-world evaluation with a Franka robot, ReBot increased the success rates of Octo by 17% and OpenVLA by 20%. More information can be found at our project page. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

A Graph-Based Reinforcement Learning Approach with Frontier Potential Based Reward for Safe Cluttered Environment Exploration

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arXiv 2025年

作者： Calzolari, Gabriele Sumathy, Vidya Kanellakis, Christoforos Nikolakopoulos, George Robotics and AI Group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

Autonomous exploration of cluttered environments requires efficient exploration strategies that guarantee safety against potential collisions with unknown random obstacles. This paper presents a novel approach combining a graph neural network-based exploration greedy policy with a safety shield to ensure safe navigation goal selection. The network is trained using reinforcement learning and the proximal policy optimization algorithm to maximize exploration efficiency while reducing the safety shield interventions. However, if the policy selects an infeasible action, the safety shield intervenes to choose the best feasible alternative, ensuring system consistency. Moreover, this paper proposes a reward function that includes a potential field based on the agent’s proximity to unexplored regions and the expected information gain from reaching them. Overall, the approach investigated in this paper merges the benefits of the adaptability of reinforcement learning-driven exploration policies and the guarantee ensured by explicit safety mechanisms. Extensive evaluations in simulated environments demonstrate that the approach enables efficient and safe exploration in cluttered environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

Design and Evaluation of a UGV-Based Robotic Platform for Precision Soil Moisture Remote Sensing

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arXiv 2025年

作者： Tsimpidi, Ilektra Tevetzidis, Ilias Sumathy, Vidya Nikolakopoulos, George Robotics and AI Team Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

This extended abstract presents the design and evaluation of AgriOne, an automated unmanned ground vehicle (UGV) platform for high precision sensing of soil moisture in large agricultural fields. The developed robotic system is equipped with a volumetric water content (VWC) sensor mounted on a robotic manipulator and utilizes a surface-aware data collection framework to ensure accurate measurements in heterogeneous terrains. The framework identifies and removes invalid data points where the sensor fails to penetrate the soil, ensuring data reliability. Multiple field experiments were conducted to validate the platform’s performance, while the obtained results demonstrate the efficacy of the AgriOne robot in real-time data acquisition, reducing the need for permanent sensors and labor-intensive methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Intelligent robots

Pushing the Boundaries of ai Chips: From Monolithic 3D CMOS to Cryogenic Computing

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Pushing the Boundaries of AI Chips: From Monolithic 3D CMOS ...

Design, Automation and Test in Europe Conference and Exhibition

作者： Mahdi Benkhelifa Shivendra S. Parihar Anirban Kar Girish Pahwa Yogesh S. Chauhan Hussam Amrouch Chair of AI Processor Design Technical University of Munich TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Munich Germany Department of Electrical Engineering and Computer Sciences University of California Berkeley USA National Yang Ming Chiao Tung University International College of Semiconductor Technology Taiwan Indian Institute of Technology Kanpur India

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

As CMOS scaling approaches its fundamental limits, the explosive rise of ai and LLMs has unveiled profound bottlenecks in computing architectures. This paper presents two groundbreaking paradigms poised to reshape the landscape of high-performance computing and meet the surging demands of ai-driven workloads. The first paradigm is 3D monolithic integration, a revolutionary approach that achieves unprecedented logic density through Complementary FETs (CFETs), where pMOS and nMOS transistors are vertically stacked, and a dramatic expansion of on-chip memory capacity by integrating memory layers atop logic transistors. The second paradigm leverages the transformative potential of operating chips at cryogenic temperatures where transistors exhibit enhanced performance, and parasitic resistances are substantially minimized. These advancements hold the promise of redefining computing efficiency and performance for the ai era.

关键词： Semiconductor device modeling Three-dimensional displays High performance computing Random access memory Cryogenics Silicon System-on-chip Logic Three-dimensional integrated circuits Optimization

Low-Frequency Ultrasound Triggers Drug Release from Perfluorocarbon Droplets at Low Intensities

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SSRN

SSRN 2025年

作者： Sirolli, Sofia Boroujeni, Faraz Amini Guachi-Guachi, Lorena Prentice, Paul Ricotti, Leonardo Cafarelli, Andrea The BioRobotics Institute Scuola Superiore Sant’Anna Piazza Martiri della Libertà 33 Pisa56127 Italy Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Piazza Martiri della Libertà 33 Pisa56127 Italy James Watt School of Engineering University of Glasgow G12 8QQ United Kingdom

Perfluorocarbon droplets represent a promising platform for ultrasound-triggered drug delivery. Their liquid core can vaporize upon the application of a pressure wave such as ultrasound, resulting in the controlled and targeted release of their drug cargo. These carriers are highly tunable, exhibit a sharp on-off behavior and have longer lifetimes compared to ultrasound-responsive microbubbles. However, despite increasing efforts in the field, achieving an acceptable balance between stability and a safe activation threshold remains challenging. Indeed, stable droplet fomulations are usually activated at very high ultrasound pressures, exceeding 1 MPa, raising safety concerns. In this study, we investigated the response of stable surfactant-shelled perfluoropentane droplets to low-intensity pulsed ultrasound. We demonstrated that a significant drug release can be triggered at a pressure as low as 100 kPa using a very low frequency (i.e., 38 kHz). Therefore, we focused on characterizing the behavior of this droplet formulation under 38 kHz ultrasound stimulation, optimizing a protocol able to achieve efficient drug release while adhering to safety guidelines, thus facilitating a future in vivo translation. We systematically examined how drug delivery efficiency varies with different stimulation parameters, including pressure, pulse repetition frequency and duty cycle. Additionally, high-speed camera imaging and ultrasound imaging were performed to further elucidate the response mechanisms of perfluorocarbon droplets to low-frequency ultrasound. © 2025, The Authors. All rights reserved.

关键词： High speed cameras