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arXiv 2025年

作者： Akinwande, Samuel I. Sidrane, Chelsea Kochenderfer, Mykel J. Barrett, Clark Department of Aeronautical and Astronautical Engineering Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Division of Robotics Perception and Learning Intelligent Systems Department School of Electrical Engineering & Computer Science KTH Royal Institute of Technology Stockholm Sweden

As dynamical systems equipped with neural network controllers (neural feedback systems) become increasingly prevalent, it is critical to develop methods to ensure their safe operation. Verifying safety requires extending control theoretic analysis methods to these systems. Although existing techniques can efficiently handle linear neural feedback systems, relatively few scalable methods address the nonlinear case. We propose a novel algorithm for forward reachability analysis of nonlinear neural feedback systems. The approach leverages the structure of the nonlinear transition functions of the systems to compute tight polyhedral enclosures (i.e., abstractions). These enclosures, combined with the neural controller, are then encoded as a mixed-integer linear program (MILP). Optimizing this MILP yields a sound over-approximation of the forward-reachable set. We evaluate our algorithm on representative benchmarks and demonstrate an order of magnitude improvement over the current state of the art. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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Crop protection and disease detection using artificial intelligence and computer vision: a comprehensive review

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Multimedia Tools and Applications 2025年第7期84卷 3723-3743页

作者： Shah, Kanish Sushra, Rajat Shah, Manan Shah, Dhairya Shah, Haard Raval, Megh Prajapati, Mitul Software Engineer Walmart SunnyvaleCA United States Atmiya Vidya Mandir Gujarat Surat India Department of Chemical Engineering School of Energy Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Robotics ECE Concentration Northeastern University BostonMA United States Department of Computer Science Concentration in AI Hofstra University HempsteadNY United States Department of Chemical Engineering S. S Agrawal Institute of Engineering & Technology Gujarat Navsari India R &D Engineer Span Inspection Systems Pvt. Ltd Gujarat Gandhinagar India

The technological advancements in the field of agriculture have increased to a great extent in recent years, and many techniques have evolved from other techniques. Some methods are improved or upgraded from the previous versions by implementing a new model or using better hardware devices. This has been helpful for the farmers in increasing crop productivity, and the life expectancy of crops has also increased as the diseases inside or outside the crops can be detected much earlier, and learning at an early stage helps prevent other crops. In this paper, we have presented a study where many varieties of fruits and vegetables have been taken to determine which method was used for a particular crop. By analyzing the various works carried out by the authors, it was inferred that most of the works revolved around image processing and hyperspectral imaging. Due to this, we had also included most of the papers, particularly as the models and hardware components used were much better than other works. Then, a comparative study was done where different fruits and vegetables highlighted the two main areas: the method used and the accuracy obtained. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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Dual-loop joint torque control based on lower limb exoskeleton robots 18

Dual-loop joint torque control based on lower limb exoskelet...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zhang, Meitong Zhang, Chi Yi, Feng Shu, Xindong Yuan, Ting Li, Zheng Hebei University of Science and Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences School of Electrical Engineering Ningbo315201 China Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering Ningbo315201 China University of Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo315201 China Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Science Faculty of Electrical Engineering and Computer Science Ningbo315201 China Hebei University of Science and Technology School of Electrical Engineering Shijiazhuang050000 China

ISBN: (纸本)9798350312201

In the framework of impedance control, the torque trajectory of the joint is unpredictable due to nonlinear factors during the movement of the exoskeleton. To precisely predict command torque trajectory and compensate for the disturbances, we propose a dual-loop joint torque control system including predictive feedforward and a disturbance observer. A predictive feedforward module is adopted as the control outer loop based on the feedback control, and a general compensation module is used to compensate for the prediction error. The inner loop adopts a disturbance observer to estimate and compensate for low-frequency external disturbances. The simulation, which utilizes the actual torque of hip and knee joints as a reference, is conducted to prove the effectiveness of the proposed method. The comparison results demonstrate that the proposed control method can realize high bandwidth for exoskeleton joint control. © 2023 IEEE.

关键词： Torque control

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Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition

arXiv

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arXiv 2024年

作者： Wang, Zihan Song, Siyang Luo, Cheng Deng, Songhe Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China University of Leicester United Kingdom Monash University Australia

Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hierarchical inter-dependencies among AUs, this paper proposes to comprehensively model multi-scale AU-related dynamic and hierarchical spatio-temporal relationship among AUs for their occurrences recognition. Specifically, we first propose a novel multi-scale temporal differencing network with an adaptive weighting block to explicitly capture facial dynamics across frames at different spatial scales, which specifically considers the heterogeneity of range and magnitude in different AUs’ activation. Then, a two-stage strategy is introduced to hierarchically model the relationship among AUs based on their spatial distribution (i.e., local and cross-region AU relationship modelling). Experimental results achieved on BP4D and DISFA show that our approach is the new state-of-the-art in the field of AU occurrence recognition. Our code is publicly available at https://***/CVI-SZU/MDHR. © 2024, CC BY.

关键词： Face recognition

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A Review of Multi-Robot Collaborative Simultaneous Localization and Mapping

A Review of Multi-Robot Collaborative Simultaneous Localizat...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Xianzhe Zhao Shiliang Shao Ting Wang Chuxi Fang Jin Zhang Hai Zhao School of Computer Science And Engineering Northeastern University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

Collaborative Simultaneous Localization and Mapping (C-SLAM), commonly known as multi-robot SLAM, is a prefatory technology that allows multiple robots to efficiently traverse large and intricate areas. This is possible due to the merging of maps and the recognition of shared spaces. Despite its many benefits, multi-robot systems encounter various challenges, such as communication barriers, co-localization issues, and joint optimization problems. This paper reviews the sensors and back-end optimization methods used for SLAM. Then, it summarizes existing collaborative SLAM frameworks and some novel solutions. Finally, it discusses trends and promising research directions for collaborative SLAM.

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MICP-L: Mesh-based ICP for Robot Localization Using Hardware-Accelerated Ray Casting

MICP-L: Mesh-based ICP for Robot Localization Using Hardware...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Alexander Mock Thomas Wiemann Sebastian Pütz Joachim Hertzberg Knowledge-Based Systems Group Institute of Computer Science Os-nabrück University Osnabrück Germany Robotics in Computer Science Fulda University of Applied Sciences Fulda Germany Nature Robots GmbH Osnabruck Germany Plan-Based Robot Control DFKI Os-nabrück Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Triangle mesh maps are a versatile 3D environment representation for robots to navigate in challenging indoor and outdoor environments exhibiting tunnels, hills and varying slopes. To make use of these mesh maps, methods are needed to accurately localize robots in such maps to perform essential tasks like path planning and navigation. We present Mesh ICP Localization (MICP-L), a novel and computationally efficient method for registering one or more range sensors to a triangle mesh map to continuously localize a robot in 6D, even in GPS-denied environments. We accelerate the computation of ray casting correspondences (RCC) between range sensors and mesh maps by supporting different parallel computing devices like multicore CPUs, GPUs and the latest NVIDIA RTX hardware. By additionally transforming the covariance computation into a reduction operation, we can optimize the initial guessed poses in parallel on CPUs or GPUs, making our implementation applicable in real-time on many architectures. We demonstrate the robustness of our localization approach with datasets from agricultural, aerial, and automotive domains.

关键词： Location awareness Casting Three-dimensional displays Navigation computer architecture Robot sensing systems Sensors Software reliability State estimation Robots

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Optimal Constrained Task Planning as Mixed Integer Programming

arXiv

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arXiv 2022年

作者： Adu-Bredu, Alphonsus Devraj, Nikhil Jenkins, Odest Chadwicke The Robotics Institute Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States

For robots to successfully execute tasks assigned to them, they must be capable of planning the right sequence of actions. These actions must be both optimal with respect to a specified objective and satisfy whatever constraints exist in their world. We propose an approach for robot task planning that is capable of planning the optimal sequence of grounded actions to accomplish a task given a specific objective function while satisfying all specified numerical constraints. Our approach accomplishes this by encoding the entire task planning problem as a single mixed integer convex program, which it then solves using an off-the-shelf Mixed Integer Programming solver. We evaluate our approach on several mobile manipulation tasks in both simulation and on a physical humanoid robot. Our approach is able to consistently produce optimal plans while accounting for all specified numerical constraints in the mobile manipulation tasks. Open-source implementations of the components of our approach as well as videos of robots executing planned grounded actions in both simulation and the physical world can be found at this url: https://***/gtpmip © 2022, CC BY.

关键词： Anthropomorphic robots

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Cryo-CACTI: Cryogenic-Aware CACTI for Cache Modeling down to 10K in Advanced 7nm FinFETs

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IEEE Transactions on computers 2025年

作者： Ravipati, Divya Praneetha van Santen, Victor M. Parihar, Shivendra Singh Chauhan, Yogesh Singh Panda, Preeti Ranjan Amrouch, Hussam Indian Institute of Technology Delhi Department of Computer Science and Engineering New Delhi India Technical University of Munich TUM School of Computation Information and Technology Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Germany Kanpur Nanolab Department of Electrical Engineering Uttar Pradesh India

Cryogenic circuits are currently employed in fields such as quantum computing, particle detectors, magnetic resonance imaging, and space applications. While cryogenic circuits are being researched, there is limited work on designing cryogenic caches at temperatures below 77K. Moreover, there is no tool to estimate the delay, power, and area of cryogenic caches at advanced technology nodes. Our research focuses on the development of cryogenic caches tailored for the 7nm technology node, operating at 10K. However, a key challenge is the lack of cryogenic measurement data, especially in recent technologies. Consequently, through conducting our own FinFET transistor measurements, we calibrate cryogenic transistor models at 10K. With the 7nm cryogenic transistor data, we model Cryo-CACTI for cryogenic caches (due to cache’s vital role in improving performance and their considerable share in area and power of the processor). Using Cryo-CACTI, our evaluation reveals considerable improvements in the energy efficiency (up to 99%) of cryogenic caches of larger sizes compared to the caches at room temperature (300K). Additionally, we explore alternative cache configurations at circuit-level to optimize cryogenic operation. Furthermore, we use Cryo-CACTI to explore the performance/energy consumption of cryogenic caches while simulating workloads such as SPEC CPU2017 and machine learning via neural networks. © 1968-2012 IEEE.

关键词： Combinatorial circuits

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Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities

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computers, Materials & Continua 2021年第5期67卷 2449-2462页

作者： Nabeela Awan Salman Khan Mohammad Khalid Imam Rahmani Muhammad Tahir Nur Alam MD Ryan Alturki Ihsan Ullah Department of Computer Science Government Girls Degree College MardanKPK23200Pakistan Department of Electrical Energy System UETPeshawar25000Pakistan Department of Computer Science College of Computing and InformaticsSaudi Electronic UniversityKSA Department of Smart Computing Kyungdong UniversityGlobal CampusGosung24764South Korea Department of Information Sciences College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Robotics Engineering Daegu Gyeongbuk Institute of Science and TechnologySouth Korea

Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green energy *** smart cities,the IoT devices are used for linking power,price,energy,and demand information for smart homes and home energy management(HEM)in the smart *** complex smart gridconnected systems,power scheduling and secure dispatch of information are the main research *** challenges can be resolved through various machine learning techniques and data *** this paper,we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement,for the smart *** proposed collaborative execute-before-after dependencybased requirement algorithm works in two phases,analysis and assessment of the requirements of end-users and power distribution *** the rst phases,a xed load is adjusted over a period of 24 h,and in the second phase,a randomly produced population load for 90 days is evaluated using particle swarm *** simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction,peak to average ratio,and power variance mean ratio than particle swarm optimization and inclined block rate.

关键词： PSO IBR machine learning IoT smart cities CDBR

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SocNavGym: A Reinforcement Learning Gym for Social Navigation

SocNavGym: A Reinforcement Learning Gym for Social Navigatio...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Aditya Kapoor Sushant Swamy Pilar Bachiller Luis J. Manso Tata Consultancy Services Research & Innovation India Birla Institute of Technology and Science Goa India Computer and Communication Technology Department Universidad de Extremadura Spain Computer Science Department Autonomous Robotics and Perception Laboratory Aston University UK

It is essential for autonomous robots to be socially compliant while navigating in human-populated environments. Machine Learning and, especially, Deep Reinforcement Learning have recently gained considerable traction in the field of Social Navigation. This can be partially attributed to the resulting policies not being bound by human limitations in terms of code complexity or the number of variables that are handled. Unfortunately, the lack of safety guarantees and the large data requirements by DRL algorithms make learning in the real world unfeasible. To bridge this gap, simulation environments are frequently used. We propose SocNavGym, an advanced simulation environment for social navigation that can generate a wide variety of social navigation scenarios and facilitates the development of intelligent social agents. SocNavGym is lightweight, fast, easy to use, and can be effortlessly configured to generate different types of social navigation scenarios. It can also be configured to work with different hand-crafted and data-driven social reward signals and to yield a variety of evaluation metrics to benchmark agents’ performance. Further, we also provide a case study where a Dueling-DQN agent is trained to learn social-navigation policies using SocNavGym. The results provide evidence that SocNavGym can be used to train an agent from scratch to navigate in simple as well as complex social scenarios. Our experiments also show that the agents trained using the data-driven reward function display more advanced social compliance in comparison to the heuristic-based reward function.

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