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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Pereira, Jorge Mendes, Jerome Junior, Jorge S. S. Viegas, Carlos Paulo, Joao Ruivo University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Pólo Ii CoimbraPT-3030-290 Portugal University of Coimbra Association for the Development of Industrial Aerodynamics CoimbraPT-3030-289 Portugal

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Every year, wildfires cause significant losses and destruction around the globe. In order to attempt to reduce their damages, resources have been put into developing fire propagation prediction systems. In a real wildfire event, these systems provide the authorities with information about the fire propagation in the near future, thus allowing them to make better decisions. Wildfire spread prediction systems are based on fire propagation models, from which the most used and accepted model is the Rothermel model. However, given the complexity of the wildfire phenomena and the uncertainty of some of its input parameter values, the Rothermel model can produce misleading results of fire propagation. This paper uses 3 metaheuristic algorithms, genetic algorithm (GA), differential evolution (DE) and simulated annealing (SA), for calibration of input parameters from the Rothermel model. These algorithms were validated using 37 datasets containing data from controlled experimental fires. Results have shown that these algorithms provide a precise wildfire spread prediction accounting for the uncertainties in the model's selected parameters. © 2022 IEEE.

关键词： Forecasting

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IMPS: Informative Map Point Selection for Visual-Inertial SLAM

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 5595-5605页

作者： Changxiang Liu Hongshan Yu Qiang Fu Xieyuanli Chen Naveed Akhtar Zhi-Hong Mao College of Electrical and Information Engineering School of Robotics Quanzhou Institute of Industrial Design and Machine Intelligence Innovation Hunan University Changsha China National University of Defense Technology Changsha China School of Computing and Information Systems University of Melbourne Melbourne VIC Australia Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA USA

Visual-inertial SLAM (VINS) stands at the forefront of advancements in computer vision, robotics, and autonomous driving, revolutionizing the way we perceive and navigate the world. The typical approach in optimization-based methods for VINS is to estimate camera poses by minimizing the reprojection errors of all corresponding map points. However, not all map points are suitable for accurate pose estimation. Some map points introduce noise and can compromise the accuracy of the estimation. Current methods primarily focus on removing 2D noise points, which proves to be ineffective since triangulation can increase the uncertainty of map points. To address this problem, we propose a new method for Informative Map Point Selection (IMPS) in VINS systems. IMPS identifies the most informative map points by utilizing mutual and geometric information as the optimization target. We integrate IMPS into a VINS system and evaluate its performance using publicly available EuRoC, TUM and M2DGR datasets, as well as our own data. Experimental results demonstrate that our method surpasses existing VINS methods and achieves state-of-the-art pose estimation performance. Importantly, IMPS functions as an independent module with strong generalization capabilities, allowing for easy integration into other VINS systems and enhancing pose estimation performance. This integration not only elevates pose estimation accuracy but also holds great promise for advancing applications in intelligent driving and unmanned systems.

关键词： Simultaneous localization and mapping Optimization Cameras Pose estimation Robots Mutual information Intelligent vehicles

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Motion Capture and Gait Analysis of Ants with Leg Injuries

Motion Capture and Gait Analysis of Ants with Leg Injuries

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Chinese Control Conference (CCC)

作者： Ming Zeng Yuanhao Li Chang Meng Shutong Zhong Zhijing Wang Feng Zhao Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Institute of Robotics and Automation Systems School of Electrical and Information Engineering Tianjin University Tianjin

Many insects in nature, such as ants, recover quickly after sustaining leg injuries. If this capability could be imparted to multi-legged robots, it would drastically enhance their ability to resist destruction and adapt to the environment. In this paper, the Camponotus Japonicus is used as the research object, and its locomotive patterns are studied under different leg-loss conditions. To facilitate this, a high-speed acquisition system, with a dual-view (bottom view and side view) setup, is designed to analyze the ant's linear gaits. Subsequently, the pivotal gait characteristics of the injured ants are extracted by keyframe annotation technology, including the gait phase diagram, duty factor, angle before support, angle before swing, and leg swing range. Lastly, according to the obtained gait characteristics, the general and specific laws of gait adjustment in the absence of one or two legs are systematically analyzed, which provides the basis for parametric settings and strategy optimization of robotic injured gait self-adjustment.

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Machine learning approaches for predicting Ankle Dorsi Plantar Moments in Cerebral Palsy gait analysis: A comparative study

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Gait & Posture 2024年 113卷 198-199页

作者： Mustafa Erkam Özates Firooz Salami Sebastian I. Wolf Yunus Ziya Arslan Turkish German University Department of Electrical and Electronics Engineering- Faculty of Engineering Istanbul Turkey Heidelberg University Hospital Clinic for Orthopedics and Trauma Surgery Heidelberg Germany Turkish German University Department of Robotics and Intelligent Systems- Institute of Graduate Studies in Science and Engineering- Istanbul Turkey

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Feature Space Augmentation and Old Class Space Preservation for Class Incremental Learning

Feature Space Augmentation and Old Class Space Preservation ...

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Chinese Control Conference (CCC)

作者： Ming Zeng Siying Li Jiazhe Liu Yuanhao Li Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Institute of Robotics and Automation Systems School of Electrical and Information Engineering Tianjin University Tianjin

The ability of continuously learning new knowledge without forgetting old ones is crucial to adapt to an ever-changing world. This scenario becomes more challenging when the previous data are not available. Current class incremental learning (CIL) approaches tend to incorporate new classes with backward compatibility, i.e., maintaining discriminability of old ones. By contrast, we focus on the extensibility and compatibility for future new classes in the early stage. Our proposed method achieves this by augmenting feature space. In detail, a large number of pseudo-new classes are generated via real example mixture and then train the initial spatially augmented model using pseudo-new classes and the base classes. Besides, considering the fact that it is impossible to maintain backward compatibility with only one prototype for each old class, the updated model also needs to preserve old class space in incremental stages. We employ a certain perturbation to the old class prototypes to effectively avoid old class space from being over-squeezed by the samples of new classes. The experiments on CIFAR-100 and ImageNet-Subset (100 classes) demonstrate that our method substantially reduces the overlap of old and new classes, outperforming state-of-the-art various baselines.

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Fuzzy Based Steering Control of a Multi-Joint AUV

Fuzzy Based Steering Control of a Multi-Joint AUV

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Control Conference (ANZCC), Australian and New Zealand

作者： Zakarya Omar Ke-Xian Liu Chao Ren Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Intelligent Un-manned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China Faculty of Engineering Sana'a University Sana'a Yemen

The multi-joint structure of our Autonomous Underwater Vehicle (AUV) enhances its maneuverability, allowing it to navigate in the underwater environment with greater flexibility. However, this added maneuverability poses challenges to the steering process. When the multi-joint AUV (MJ-AUV) performs steering maneuvers, its joints undergo rotation, leading to a change in the orientation of the cabins with respect to the overall forward heading of the vehicle. As a result, this change in orientation affects the values of the hydrodynamic reactions, including Coriolis and damping, experienced by the cabins. The dynamic interaction between the joints' rotation and the resulting change in hydrodynamic forces significantly impacts the steering performance and stability of the MJ-AUV. Understanding and addressing these effects are crucial for the development of effective control strategies that ensure precise and reliable steering in various underwater environments. Overcoming the difficulties posed by joint rotation during steering can lead to advancements in MJ-AUV navigation and expand their potential applications in complex underwater missions. This research proposes a fuzzy-based control with sliding mode control (SMC) approach for the steering of an MJAUV. The developed fuzzy-based SMC control algorithm is validated through extensive MATLAB simulations. The results demonstrate improved tracking performance and robustness in comparison to the SMC control method. Moreover, the proposed approach shows superior trajectory tracking accuracy while mitigating undesired chattering effects associated with standard SMC techniques.

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Enhancing Robustness in Manipulability Assessment: The Pseudo-Ellipsoid Approach

Enhancing Robustness in Manipulability Assessment: The Pseud...

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

作者： Erfan Shahriari Kim Kristin Peper Matej Hoffmann Sami Haddadin Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence Technical University of Munich (TUM) Germany Department of Mechanical Engineering Newman Laboratory for Biomechanics and Human Rehabilitation Massachusetts Institute of Technology (MIT) USA Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague (CTU) Czech Republic

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Manipulability analysis is a methodology employed to assess the capacity of an articulated system, at a specific configuration, to produce motion or exert force in diverse directions. The conventional method entails generating a virtual ellipsoid using the system’s configuration and model. Yet, this approach poses challenges when applied to systems such as the human body, where direct access to such information is limited, necessitating reliance on estimations. Any inaccuracies in these estimations can distort the ellipsoid’s configuration, potentially compromising the accuracy of the manipulability assessment. To address this issue, this article extends the standard approach by introducing the concept of the manipulability pseudo-ellipsoid. Through a series of theoretical analyses, simulations, and experiments, the article demonstrates that the proposed method exhibits reduced sensitivity to noise in sensory information, consequently enhancing the robustness of the approach.

关键词： Analytical models Visualization Accuracy Sensitivity analysis Noise Force Estimation Robustness Ellipsoids Standards

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ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in Orchards

arXiv

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arXiv 2023年

作者： Barros, T. Garrote, L. Conde, P. Coombes, M.J. Liu, C. Premebida, C. Nunes, U.J. The University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal The Dept. of Aeronautical and Automotive Engineering LUCAS Lab. Loughborough University United Kingdom

Robust and reliable place recognition and loop closure detection in agricultural environments is still an open problem. In particular, orchards are a difficult case study due to structural similarity across the entire field. In this work, we address the place recognition problem in orchards resorting to 3D LiDAR data, which is considered a key modality for robustness. Hence, we propose ORCHNet, a deep-learning-based approach that maps 3D-LiDAR scans to global descriptors. Specifically, this work proposes a new global feature aggregation approach, which fuses multiple aggregation methods into a robust global descriptor. ORCHNet is evaluated on real-world data collected in orchards, comprising data from the summer and autumn seasons. To assess the robustness, We compare ORCHNet with state-of-the-art aggregation approaches on data from the same season and across seasons. Moreover, we additionally evaluate the proposed approach as part of a localization framework, where ORCHNet is used as a loop closure detector. The empirical results indicate that, on the place recognition task, ORCHNet outperforms the remaining approaches, and is also more robust across seasons. As for the localization, the edge cases where the path goes through the trees are solved when integrating ORCHNet as a loop detector, showing the potential applicability of the proposed approach in this task. The code will be publicly available at:https://***/Cybonic/***. © 2023, CC BY.

关键词： Orchards

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Robust tube-based LPV-MPC for autonomous lane keeping

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IFAC-PapersOnLine 2022年第35期55卷 103-108页

作者： Maryam Nezami Hossam Seddik Abbas Ngoc Thinh Nguyen Georg Schildbach Institute for Electrical Engineering in Medicine University of Lübeck Lübeck Germany Institute for Robotics and Cognitive Systems University of Lübeck Lübeck Germany

This paper proposes a control architecture for autonomous lane keeping by a vehicle. In this paper, the vehicle dynamics consist of two parts: lateral and longitudinal dynamics. Therefore, the control architecture comprises two subsequent controllers. A longitudinal model predictive control (MPC) makes the vehicle track the desired longitudinal speeds that are assumed to be generated by a speed planner. The longitudinal speeds are then passed to a lateral MPC for lane keeping. Due to the dependence of the lateral dynamics on the longitudinal speed, they are represented in a linear parameter-varying (LPV) form, where its scheduling parameter is the longitudinal speed of the vehicle. In order to deal with the imprecise information of the future longitudinal speed (the scheduling parameter), a bound of uncertainty is considered around the nominal trajectory of the future longitudinal velocities. Then, a tube-based LPV-MPC is adopted to control the lateral dynamics for attaining the lane keeping goal. In the end, the effectiveness of the proposed methods is illustrated by carrying out simulation tests.

关键词： Automotive Dynamic Uncertain systems LPVS Predictive Control Optimal Control Robustness Issues

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Trajectory Planning for Non-Prehensile Object Transportation

Trajectory Planning for Non-Prehensile Object Transportation

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

作者： Lingyun Chen Haoyu Yu Liding Zhang Abdeldjallil Naceri Abdalla Swikir Sami Haddadin Munich Institute of Robotics and Machine Intelligence and the Chair of Robotics Science and Systems Intelligence Technical University of Munich Germany Centre for Tactile Internet With Human-in-the-Loop (CeTI) Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Albaida Libya

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Non-prehensile transportation of unstable objects presents a challenging task in robotics. To ensure the success of the transportation, it is necessary to consider both the object’s stability via contact dynamics and the motion constraints of the robot. We propose two novel trajectory planning methods derived from sampling and dynamic programming algorithms, tested on a 7-DoF Franka Emika robot against common strategies like Model Predictive Control (MPC) and S-curve planning, particularly under the constraint of a non-rotating tray. The results demonstrate the effectiveness of our methodologies in improving transportation speed. This research contributes to advancements in robotic manipulation techniques by tackling non-prehensile manipulation of dynamically unstable objects.

关键词： Trajectory planning Shape Refining Transportation Sampling methods Stability analysis Trajectory Planning Dynamic programming Predictive control

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