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Parameter Identification of AUV Motion Model Based on NARX Steady-State Response 7

Parameter Identification of AUV Motion Model Based on NARX S...

引用

7th IEEE international conference on Advanced robotics and Mechatronics

作者： Guo, Xiaojun Huang, Zhiping Su, Shaojing Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha Peoples R China

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

As a research tool autonomous underwater vehicles (AUVs) play an important role in various fields. However, due to the limitation of the number of field tests, poor information is the basic characteristic and background of most AUV test data processing. The application of simulation model and simulation data is an important way in the existing poor information test. How to use the field test data to identify and correct the parameters of the simulation model is an important point for the simulation test data to be integrated into the field data. For the characterization of AUV motion model and nonlinear polynomial autoregressive models with exogenous inputs (NARX) model, this paper proposes the use of steady-state response method to realize parameter identification based on field data. Then, the prior information is extracted by combining the existing data. On the basis of a priori constraints, three methods are used for grey box identification, the recursive least squares method with forgetting factor, the parallel recursive prediction error algorithm, recursive prediction error algorithm (RPEA), the steady-state response based on the NARX model. The accumulated error after identification proves that the steady-state response method based on the NARX model has better fitting accuracy.

关键词： Parameter estimation Mechatronics Fitting Predictive models Prediction algorithms Data processing Data models

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Range Image-based LiDAR Localization for Autonomous Vehicles

Range Image-based LiDAR Localization for Autonomous Vehicles

引用

IEEE international conference on robotics and automation (ICRA)

作者： Chen, Xieyuanli Vizzo, Ignacio Labe, Thomas Behley, Jens Stachniss, Cyrill Univ Bonn Bonn Germany

ISBN: (纸本)9781728190778

Robust and accurate, map-based localization is crucial for autonomous mobile systems. In this paper, we exploit range images generated from 3D LiDAR scans to address the problem of localizing mobile robots or autonomous cars in a map of a large-scale outdoor environment represented by a triangular mesh. We use the Poisson surface reconstruction to generate the mesh-based map representation. Based on the range images generated from the current LiDAR scan and the synthetic rendered views from the mesh-based map, we propose a new observation model and integrate it into a Monte Carlo localization framework, which achieves better localization performance and generalizes well to different environments. We test the proposed localization approach on multiple datasets collected in different environments with different LiDAR scanners. The experimental results show that our method can reliably and accurately localize a mobile system in different environments and operate online at the LiDAR sensor frame rate to track the vehicle pose.

关键词： Location awareness Surface reconstruction Laser radar Three-dimensional displays Monte Carlo methods conferences Robot sensing systems

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POLITE: Preferences Combined with Highlights in Reinforcement Learning

POLITE: Preferences Combined with Highlights in Reinforcemen...

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IEEE international conference on robotics and automation (ICRA)

作者： Simon Holk Daniel Marta Iolanda Leite Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Many solutions to address the challenge of robot learning have been devised, namely through exploring novel ways for humans to communicate complex goals and tasks in reinforcement learning (RL) setups. One way that experienced recent research interest directly addresses the problem by considering human feedback as preferences between pairs of trajectories (sequences of state-action pairs). However, when simply attributing a single preference to a pair of trajectories that contain many agglomerated steps, key pieces of information are lost in the process. We amplify the initial definition of preferences to account for highlights: state-action pairs of relatively high information (high/low reward) within a preferred trajectory. To include the additional information, we design novel regularization methods within a preference learning framework. To this extent, we present our method which is able to greatly reduce the necessary amount of preferences, by permitting the highlighting of favoured trajectories, in order to reduce the entropy of the credit assignment. We show the effectiveness of our work in both simulation and a user study, which analyzes the feedback given and its implications. We also use the total collected feedback to train a robot policy for socially compliant trajectories in a simulated social navigation environment. We release code and video examples at https://***/view/rl-polite

关键词： Codes Shape Navigation Design methodology Reinforcement learning Robot learning Entropy

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1st international conference on Agriculture-Centric Computation, ICA 2023

1st International Conference on Agriculture-Centric Computat...

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1st international conference on Agriculture-Centric Computation, ICA 2023

ISBN: (纸本)9783031436048

The proceedings contain 18 papers. The special focus in this conference is on international conference on Agriculture-Centric Computation. The topics include: Machine Learning methods for Crop Yield Prediction;real-Time Plant Disease Detection: A Comparative Study;fusedNet Model for Varietal Classification of Rice Seeds;privacy-Preserving Pest Detection Using Personalized Federated Learning;IRPD: In-Field Radish Plant Dataset;fast Rotated Bounding Box Annotations for Object Detection;preface;Empirical Analysis and Evaluation of Factors Influencing Adoption of AI-Based automation Solutions for Sustainable Agriculture;Investigation of the Bulk and Electronic Properties of Boron/Nitrogen/Indium Doped Armchair Graphene Nanoribbon for Sensing Plant VOC: A DFT Study;fruit Segregation Using Deep Learning;Application of Near-Infrared (NIR) Hyperspectral Imaging System for Protein Content Prediction in Chickpea Flour;estimation of Leaf Parameters in Punjab Region Through Multi-spectral Drone Images Using Deep Learning models;a Review on Applications of Artificial Intelligence for Identifying Soil Nutrients;intelligent Chatbot Assistant in Agriculture Domain;automated Agriculture News Collection, Analysis, and Recommendation;classification of Crops Based on Band Quality and Redundancy from the Hyperspectral Image.

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Gradient Adjustment for better Differentiable Binarization in scene text detection

Gradient Adjustment for better Differentiable Binarization i...

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Artificial Intelligence and Electromechanical automation (AIEA), international conference on

作者： Qing Mu Shuqiang Wang Dept. of Information and Electrical Engineering Hebei University of Engineering Handan China

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

Text detection, which aims to locate text areas in images, is an important step in reading text in scene images and is essential for office automation, visual search, blind assistance, etc. The methods(models) based on Differentiable Binarization (DB) have gained wide attention in segmentation-based methods due to better performance via enabling thresholds for binarization during backpropagation and simplifying post-processing. Thresholds for binarization require supervision by probability masks and threshold masks, which are generated from the ground truth. Therefore, improving the performance of DB models (DBnet [1] or Dbnet++ [2]) can be achieved by adopting a more rational label generation preprocess, which would allow for better supervision. In this paper, we propose a module in label generation to generate a gradient adjustment mask named GAM as an indicator to make better Differentiable Binarization. We will explain the motivation for this approach and conduct experiments on different datasets and DB models to demonstrate their effectiveness and robustness.

关键词： Training Backpropagation Visualization Office automation Image segmentation Text detection Robustness Inference algorithms Convolutional neural networks Artificial intelligence

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Data-free Neural Representation Compression with Riemannian Neural Dynamics 41

Data-free Neural Representation Compression with Riemannian ...

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41st international conference on Machine Learning, ICML 2024

作者： Pei, Zhengqi Zhang, Anran Wang, Shuhui Ji, Xiangyang Huang, Qingming Institute of Computing Technology Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Peng Cheng Laboratory China Department of Automation Tsinghua University China School of Computer Science and Technology University of Chinese Academy of Sciences China

Neural models are equivalent to dynamic systems from a physics-inspired view, implying that computation on neural networks can be interpreted as the dynamical interactions between neurons. However, existing work models neuronal interaction as a weight-based linear transformation, and the nonlinearity comes from the nonlinear activation functions, which leads to limited nonlinearity and data-fitting ability of the whole neural model. Inspired by Riemannian geometry, we interpret neural structures by projecting neurons onto the Riemannian neuronal state space and model neuronal interaction with Riemannian metric (RieM), which provides a neural representation framework with higher parameter efficiency. With RieM, we further design a novel data-free neural compression mechanism that does not require additional fine-tuning with real data. Using backbones like ResNet and Vision Transformer, we conduct extensive experiments on datasets such as MNIST, CIFAR-100, ImageNet-1k, and COCO object detection. Empirical results show that, under equal compression rates and computational complexity, models compressed with RieM achieve superior inference accuracy compared to existing data-free compression methods. Copyright 2024 by the author(s)

关键词： Neural network models

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Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects

Emergent Hand Morphology and Control from Optimizing Robust ...

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IEEE international conference on robotics and automation (ICRA)

作者： Pan, Xinlei Garg, Animesh Anandkumar, Animashree Zhu, Yuke NVIDIA Santa Clara CA 95051 USA Univ Calif Berkeley Berkeley CA 94720 USA Univ Toronto Vector Inst Toronto ON Canada CALTECH Pasadena CA 91125 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781728190778

Evolution in nature illustrates that the creatures' biological structure and their sensorimotor skills adapt to the environmental changes for survival. Likewise, the ability to morph and acquire new skills can facilitate an embodied agent to solve tasks of varying complexities. In this work, we introduce a data-driven approach where effective hand designs naturally emerge for the purpose of grasping diverse objects. Jointly optimizing morphology and control imposes computational challenges since it requires constant evaluation of a black-box function that measures the performance of a combination of embodiment and behavior. We develop a novel Bayesian Optimization algorithm that efficiently co-designs the morphology and grasping skills through learned latent-space representations. We design the grasping tasks based on a taxonomy of human grasp types: power grasp, pinch grasp, and lateral grasp. Through experimentation and comparative study, we demonstrate that our approach discovers robust and cost-efficient hand morphologies for grasping novel objects.

关键词： Costs Taxonomy Morphology Optimization methods Grasping Robot sensing systems Hardware

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Multi-view object pose estimation from correspondence distributions and epipolar geometry

Multi-view object pose estimation from correspondence distri...

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IEEE international conference on robotics and automation (ICRA)

作者： Rasmus Laurvig Haugaard Thorbjorn Mosekjaer Iversen SDU Robotics Maersk Mc-Kinney Moller Institute University of Southern Denmark.

In many automation tasks involving manipulation of rigid objects, the poses of the objects must be acquired. Vision-based pose estimation using a single RGB or RGB-D sensor is especially popular due to its broad applicability. However, single-view pose estimation is inherently limited by depth ambiguity and ambiguities imposed by various phenom-ena like occlusion, self-occlusion, reflections, etc. Aggregation of information from multiple views can potentially resolve these ambiguities, but the current state-of-the-art multi-view pose estimation method only uses multiple views to aggregate single-view pose estimates, and thus rely on obtaining good single-view estimates. We present a multi-view pose estimation method which aggregates learned 2D-3D distributions from multiple views for both the initial estimate and optional refinement. Our method performs probabilistic sampling of 3D-3D correspondences under epipolar constraints using learned 2D-3D correspondence distributions which are implicitly trained to respect visual ambiguities such as symmetry. Evaluation on the T-LESS dataset shows that our method reduces pose estimation errors by 80–91% compared to the best single-view method, and we present state-of-the-art results on T-LESS with four views, even compared with methods using five and eight views.

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Mesh Based Analysis of Low Fractal Dimension Reinforcement Learning Policies

Mesh Based Analysis of Low Fractal Dimension Reinforcement L...

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IEEE international conference on robotics and automation (ICRA)

作者： Gillen, Sean Byl, Katie Univ Calif Santa Barbara Elect & Comp Engn Dept Santa Barbara CA 93106 USA

ISBN: (纸本)9781728190778

In previous work, using a process we call meshing, the reachable state spaces for various continuous and hybrid systems were approximated as a discrete set of states which can then be synthesized into a Markov chain. One of the applications for this approach has been to analyze locomotion policies obtained by reinforcement learning, in a step towards making empirical guarantees about the stability properties of the resulting system. In a separate line of research, we introduced a modified reward function for on-policy reinforcement learning algorithms that utilizes a "fractal dimension" of rollout trajectories. This reward was shown to encourage policies that induce individual trajectories which can be more compactly represented as a discrete mesh. In this work, we combine these two threads of research by building meshes of the reachable state space of a system subject to disturbances and controlled by policies obtained with the modified reward. Our analysis shows that the modified policies do produce much smaller reachable meshes. This shows that agents trained with the fractal dimension reward transfer their desirable quality of having a more compact state space to a setting with external disturbances. The results also suggest that the previous work using mesh based tools to analyze RL policies may be extended to higher dimensional systems or to higher resolution meshes than would have otherwise been possible.

关键词： Training Visualization Monte Carlo methods Reinforcement learning Tools Aerospace electronics Fractals

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Dynamic Gait Switching for Hexapod Robots

Dynamic Gait Switching for Hexapod Robots

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international conference on Autonomous Robots and Agents, ICARA

作者： James Florin Petri Gerard Lacey Electronic Engineering Department Maynooth University Maynooth Ireland

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

Hexapod robots exhibit superior navigation capabilities on rough and unmodified terrains, but gait control and switching are complex. This paper describes a novel gait library approach that allows for dynamic gait switching that facilitates faster gait transitions, rapid response to limb loss, and flexible configuration for different hexapods. The gait library is validated on the open-source H.O.R.E.A. platform in terms of gait switching times and rapid response to loss of a limb. The gait switching is relative to the swing speed, resulting in a fast response time. Additionally, the gait manager adapts to the loss of one or two limbs, resulting in an approximate 23% drop in speed with each leg removed, except for the wave gait, which showed an increase of around 10% for each leg.

关键词： Legged locomotion Adaptation models automation Navigation Switches Libraries Time factors

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