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arXiv 2024年

作者： Mellet, Julien Berra, Andrea Seisa, Achilleas Santi Sankaranarayanan, Viswa Gamage, Udayanga G.W.K.N. Soto, Miguel Ángel Trujillo Heredia, Guillermo Nikolakopoulos, George Lippiello, Vincenzo Ruggiero, Fabio PRISMA Lab Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy CATEC Advanced Center for Aerospace Technologies Seville Spain Robotics and AI Team Department of Computer Electrical and Space Engineering LuleåUniversity of Technology Luleå Sweden Automation and Control Group Department of Electrical Engineering and Photonics Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400 g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop kinematic chain, employs two deported servomotors. Each joint is actuated with a single tendon for active motion control in compression of the arm at the end-effector. Its elasto-mechanical design reduces weight and provides flexibility, allowing passive-compliant interactions without impacting the motors’ integrity. Notably, the arm’s damping can be adjusted based on the proposed inner frictional bulges. Experimental applications showcase the aerial system performance in both free-flight and physical interaction. The presented work may open safer applications for Micro Aerial Vehicle (MAV) in real environments subject to perturbations during interaction. © 2024, CC BY.

关键词： End effectors

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View-Robust Neural Networks for Unseen Human Action Recognition in Videos

View-Robust Neural Networks for Unseen Human Action Recognit...

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

作者： Yu, Jiahui Ma, Tianyu Ju, Zhaojie Chen, Hang Xu, Yingke Zhejiang University Department of Biomedical Engineering Hangzhou China Binjiang Institute of Zhejiang University Innovation Center for Smart Medical Technologies & Devices Hangzhou China Shenyang Ligong University School of Automation and Electrical Engineering Shenyang China Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Portsmouth School of Computing Portsmouth United Kingdom

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Data-driven deep learning achieved excellent performance for human action recognition. However, unseen action recognition remains a challenge for most existing neural networks. Because the action categories, collection perspectives, and scenarios considered during data collection are limited. Compared with class-unseen action recognition, view-unseen action recognition in videos is under-explored. This paper proposes view-robust neural networks (VR-Net) to recognize unseen actions in videos. The VR-Net consists of a 3D pose estimation module, skeleton adaptive transformation neural networks, and classification modules. We first extract 3D skeleton models from the video sequence based on existing pose estimation methods. Next, we propose a skeleton representation transformation scheme and achieve it based on Convolutional Neural Networks (VR-CNN) and Graph Neural Networks (VR-GCN), resulting in the optimal skeleton representations. Futhermore, we explore an associate optimization scheme and a fused output method. We evaluate the proposed neural networks on three challenging benchmarks, i.e., NTU RGB-D dataset (NTU), Kinetics-400 dataset, and Human3.6M dataset (H3.6M). The experimental results show that view robust neural networks achieve the top performance compared to state-of-the-art RGB-based and skeleton-based works, such as 93.6% on the NTU (CV) and 94.6% on the Kinetics-400 dataset (Top-5). The proposed neural networks significantly improve the recognition performance for unseen action recognition, such as 86.8% on the H3.6M (View 2). © 2022 IEEE.

关键词： Convolutional neural networks

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SDF-Pack: Towards Compact Bin Packing with Signed-Distance-Field Minimization

arXiv

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arXiv 2023年

作者： Pan, Jia-Hui Hui, Ka-Hei Gao, Xiaojie Zhu, Shize Liu, Yun-Hui Heng, Pheng-Ann Fu, Chi-Wing The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The Hong Kong Centre for Logistics Robotics Hong Kong The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

Robotic bin packing is very challenging, especially when considering practical needs such as object variety and packing compactness. This paper presents SDF-Pack, a new approach based on signed distance field (SDF) to model the geometric condition of objects in a container and compute the object placement locations and packing orders for achieving a more compact bin packing. Our method adopts a truncated SDF representation to localize the computation, and based on it, we formulate the SDF-minimization heuristic to find optimized placements to compactly pack objects with the existing ones. To further improve space utilization, if the packing sequence is controllable, our method can suggest which object to be packed next. Experimental results on a large variety of everyday objects show that our method can consistently achieve higher packing compactness over 1,000 packing cases, enabling us to pack more objects into the container, compared with the existing heuristics under various packing settings. The code is publicly available at: https://***/kwpoon/SDF-Pack. Copyright © 2023, The Authors. All rights reserved.

关键词： Containers

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GA+DDPG+HER: Genetic Algorithm-Based Function Optimizer in Deep Reinforcement Learning for Robotic Manipulation Tasks

GA+DDPG+HER: Genetic Algorithm-Based Function Optimizer in D...

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IEEE International Conference on Robotic Computing (IRC)

作者： Adarsh Sehga Nicholas Ward Hung Manh La Christos Papachristos Sushil Louis Advanced Robotics and Automation (ARA) Laboratory Department of Computer Science and Engineering University of Nevada Reno NV USA

ISBN: (纸本)9781665472616

Agents can base decisions made using reinforcement learning (RL) on a reward function. The selection of values for the learning algorithm parameters can, nevertheless, have a substantial impact on the overall learning process. In order to discover values for the learning parameters that are close to optimal, we extended our previously proposed genetic algorithm-based Deep Deterministic Policy Gradient and Hindsight Experience Replay approach (referred to as GA+DDPG+HER) in this study. On the robotic manipulation tasks of FetchReach, FetchSlide, FetchPush, FetchPick&Place, and DoorOpening, we applied the GA+DDPG+HER methodology. Our technique GA+DDPG+HER was also used in the AuboReach environment with a few adjustments. Our experimental analysis demonstrates that our method produces performance that is noticeably better and occurs faster than the original algorithm. We also offer proof that GA+DDPG+HER beat the current approaches. The final results support our assertion and offer sufficient proof that automating the parameter tuning procedure is crucial and does cut down learning time by as much as 57%.

关键词： Deep learning Reinforcement learning Task analysis Robots Tuning Genetic algorithms

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Machine learning-driven insights into phase prediction for high entropy alloys

Journal of Alloys and Metallurgical Systems

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Journal of Alloys and Metallurgical Systems 2024年 8卷

作者： Jain, Reliance Jain, Sandeep Dewangan, Sheetal Kumar Boriwal, Lokesh Kumar Samal, Sumanta Department of Robotics and Automation Prestige Institute of Engineering Management and Research Madhya Pradesh Indore452010 India Department of Advanced Materials Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Materials Science and Engineering Ajou University Suwon16499 Korea Republic of Department of Mechanical Engineering Prestige Institute of Engineering Management and Research Madhya Pradesh Indore452010 India Department of Metallurgical Engineering and Materials Science Indian Institute of Technology Indore Simrol Madhya Pradesh Indore453552 India

The unique properties of high-entropy alloys (HEAs) have attracted considerable attention, largely due to their dependence on the choice among three distinct phases: solid solution (SS), intermetallic compound (IM), or a blend of both (SS + IM). For this reason, precise phase prediction is key to identifying the optimal element combinations needed to develop HEAs with the required characteristics. Due to large compositional domain of HEAs is opportune to design new HEAs with desired output. A machine learning tool is exploited to discover and characterize high entropy alloys with satisfying targets. Herein, a method of designing substitutional high entropy alloys with optimization of input features and predict their phase formation, using different ML algorithms are proposed. The ML models such as multi layer precreptron MLP, Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), KNN, XGB nad SVM Classifier algorithm were used for the identifying the phase of HEAs. After assessing the accuracy and tuning of each model, an random forest classifier (accuracy = 0.914. precision = 0.916, ROC-AUC score = 0.97) model showed the best predictive capabilities for phase prediction. The new HEA was designed based on prediction and successfully validated with thermodynamic simulation. Data Availability: Data will be made available on request © 2024 The Authors

关键词： High-entropy alloys

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An Integrated Real-time UAV Trajectory Optimization and Potential Field Approach for Dynamic Collision Avoidance

An Integrated Real-time UAV Trajectory Optimization and Pote...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： D. M. K. K. Venkateswara Rao Hamed Habibi Jose Luis Sanchez-Lopez Holger Voos Automation and Robotics Research Group Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg Department of Engineering Faculty of Science Technology and Medicine (FSTM) University of Luxembourg

This paper presents an integrated approach that combines trajectory optimization and Artificial Potential Field (APF) method for real-time optimal Unmanned Aerial Vehicle (UAV) trajectory planning and dynamic collision avoidance. A minimum-time trajectory optimization problem is formulated with initial and final positions as boundary conditions and collision avoidance as constraints. It is transcribed into a nonlinear programming problem using Chebyshev pseudospectral method. The state and control histories are approximated by using Lagrange polynomials and the collocation points are used to satisfy constraints. A novel sigmoid-type collision avoidance constraint is proposed to overcome the drawbacks of Lagrange polynomial approximation in pseudospectral methods that only guarantees inequality constraint satisfaction only at nodal points. Automatic differentiation of cost function and constraints is used to quickly determine their gradient and Jacobian, respectively. An APF method is used to update the optimal control inputs for guaranteeing collision avoidance. The trajectory optimization and APF method are implemented in a closed-loop fashion continuously, but in parallel at moderate and high frequencies, respectively. The initial guess for the optimization is provided based on the previous solution. The proposed approach is tested and validated through indoor *** video link: https://***/swSspfvYjJs

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Risk-Aware Non-Myopic Motion Planner for Large-Scale Robotic Swarm Using CVaR Constraints

Risk-Aware Non-Myopic Motion Planner for Large-Scale Robotic...

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

作者： Xuru Yang Yunze Hu Han Gao Kang Ding Zhaoyang Li Pingping Zhu Ying Sun Chang Liu Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China Department of Automation Tsinghua University Beijing China Department of Computer Sciences and Electrical Engineering (CSEE) Marshall University Huntington WV USA School of Electrical Engineering and Computer Science The Pennsylvania State University State College PA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Swarm robotics has garnered significant attention due to its ability to accomplish elaborate and synchronized tasks. Existing methodologies for motion planning of swarm robotic systems mainly encounter difficulties in scalability and safety guarantee. To address these limitations, we propose a Risk-aware swarm mOtion planner using conditional ValuE-at-Risk (ROVER) that systematically navigates large-scale swarms through cluttered environments while ensuring safety. ROVER formulates a finite-time model predictive control (FTMPC) problem predicated upon the macroscopic state of the robot swarm represented by a Gaussian Mixture Model (GMM) and integrates conditional value-at-risk (CVaR) to ensure collision avoidance. The key component of ROVER is imposing a CVaR constraint on the distribution of the Signed Distance Function between the swarm GMM and obstacles in the FTMPC to enforce collision avoidance. Utilizing the analytical expression of CVaR of a GMM derived in this work, we develop a computationally efficient solution to solve the non-linear constrained FTMPC through sequential linear programming. Simulations and comparisons with representative benchmark approaches demonstrate the effectiveness of ROVER in flexibility, scalability, and safety guarantee.

关键词： Navigation Scalability Swarm robotics Benchmark testing Safety Planning Trajectory Synchronization Collision avoidance Robots

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Towards Exact Interaction Force Control for Underactuated Quadrupedal Systems with Orthogonal Projection and Quadratic Programming

arXiv

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arXiv 2022年

作者： Wang, Shengzhi Chu, Xiangyu Au, K.W. Samuel The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong The Multiscale Medical Robotics Center

Projected Inverse Dynamics Control (PIDC) is commonly used in robots subject to contact, especially in quadrupedal systems. Many methods based on such dynamics have been developed for quadrupedal locomotion tasks, and only a few works studied simple interactions between the robot and environment, such as pressing an E-stop button. To facilitate the interaction requiring exact force control for safety, we propose a novel interaction force control scheme for underactuated quadrupedal systems relying on projection techniques and Quadratic Programming (QP). This algorithm allows the robot to apply a desired interaction force to the environment without using force sensors while satisfying physical constraints and inducing minimal base motion. Unlike previous projection-based methods, the QP design uses two selection matrices in its hierarchical structure, facilitating the decoupling between force and motion control. The proposed algorithm is verified with a quadrupedal robot in a high-fidelity simulator. Compared to the QP designs without the strategy of using two selection matrices and the PIDC method for contact force control, our method provided more accurate contact force tracking performance with minimal base movement, paving the way to approach the exact interaction force control for underactuated quadrupedal systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Quadratic programming

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On SORA for High-Risk UAV Operations under New EU Regulations: Perspectives for Automated Approach

On SORA for High-Risk UAV Operations under New EU Regulation...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Hamed Habibi D. M. K. K. Venkateswara Rao Jose Luis Sanchez-Lopez Holger Voos Automation and Robotics Research Group Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg Department of Engineering Faculty of Science Technology and Medicine (FSTM) University of Luxembourg

In this paper, we investigate requirements to prepare an application for Specific Operations Risk Assessment (SORA), regulated by European Union Aviation Safety Agency (EASA) to obtain flight authorization for Unmanned Aerial Vehicles (UAVs) operations and propose some perspectives to automate the approach based on our successful application. Preparation of SORA requires expert knowledge as it contains technicalities. Also, the whole process is an iterative and timeconsuming one. It is even more challenging for higher-risk operations, such as those in urban environments, near airports, and multi- and customized models for research activities. SORA process limits the potential socio-economic impacts of innovative UAV capabilities. Therefore, in this paper, we present a SORA example, review the steps and highlight challenges. Accordingly, we propose an alternative workflow, considering the same steps, while addressing the challenges and pitfalls, to shorten the whole process. Furthermore, we present a comprehensive list of preliminary technical procedures, including the pre/during/post-flight checklists, design and installation appraisal, flight logbook, operational manual, training manual, and General Data Protection Regulation (GDPR), which are not explicitly instructed in SORA manual. Moreover, we propose the initial idea to create an automated SORA workflow to facilitate obtaining authorization, which is significantly helpful for operators, especially the scientific community, to conduct experimental operations.

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Automated Non-invasive Analysis of Motile Sperms Using Cross-scale Guidance Network

Automated Non-invasive Analysis of Motile Sperms Using Cross...

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IEEE International Conference on robotics and automation (ICRA)

作者： Wei Dai Zixuan Wu Jiaqi Wang Rui Liu Min Wang Tianyi Wu Junxian Zhou Zhuoran Zhang Jun Liu Centre for Robotics and Automation City University of Hong Kong Hong Kong China Department of Mechanical Engineering City University of Hong Kong Hong Kong China School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) Shenzhen China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Unbiased measurement of sperm morphometric and motility parameters is essential for assessing fertility potential and guiding visual feedback for microrobotic manipulation. Automated analysis of multiple sperms and selection of an optimal sperm is crucial for in vitro fertilisation treatment such as robotic intracytoplasmic sperm injection. However, conventional image processing methods have limitations in analysing small sperm objects under microscopic imaging. The emergence of convolutional neural networks (CNNs) has offered promising advancements in microscopic image analysis. However, previous CNN methods have struggled to accurately segment tiny objects, requiring staining or fluorescence techniques to enhance visual contrast between sperm and culture medium, leading to clinical impracticality. To address these limitations, we introduce a novel segmentation network named the cross-scale guidance (CSG) network for accurate and efficient segmentation of minute sperm objects. The CSG network employs innovative modules, including collateral multi-scale convolution, cross-scale feature map guide, and multi-scale feature fusion, to preserve essential sperm details despite their small size. Experimental results indicate that the CSG network surpassed the state-of-the-art models designed for small object segmentation, achieving a significant increase up to 18.62% higher mean intersection over union (mIoU). Additionally, the CSG network excelled in sperm morphometric analysis, achieving errors below 20%. Moreover, sperm motility parameters were further derived from the segmentation results for comprehensive sperm fertility analysis.

关键词： Visualization Image segmentation Accuracy Image analysis Microscopy Morphology Object segmentation

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