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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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Teach and Repeat Navigation: A Robust Control Approach

Teach and Repeat Navigation: A Robust Control Approach

引用

IEEE international conference on robotics and automation (ICRA)

作者： Payam Nourizadeh Michael Milford Tobias Fischer QUT Centre for Robotics Queensland University of Technology Brisbane Australia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Robot navigation requires an autonomy pipeline that is robust to environmental changes and effective in varying conditions. Teach and Repeat (T&R) navigation has shown high performance in autonomous repeated tasks under challenging circumstances, but research within T&R has predominantly focused on motion planning as opposed to motion control. In this paper, we propose a novel T&R system based on a robust motion control technique for a skid-steering mobile robot using sliding-mode control that effectively handles uncertainties that are particularly pronounced in the T&R task, where sensor noises, parametric uncertainties, and wheel-terrain interaction are common challenges. We first theoretically demonstrate that the proposed T&R system is globally stable and robust while considering the uncertainties of the closed-loop system. When deployed on a Clearpath Jackal robot, we then show the global stability of the proposed system in both indoor and outdoor environments covering different terrains, outperforming previous state-of-the-art methods in terms of mean average trajectory error and stability in these challenging environments. This paper makes an important step towards long-term autonomous T&R navigation with ensured safety guarantees.

关键词： Uncertainty Navigation Wheels Robot sensing systems Robustness Odometry Mobile robots

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Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observations

Active Bayesian Multi-class Mapping from Range and Semantic ...

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IEEE international conference on robotics and automation (ICRA)

作者： Asgharivaskasi, Arash Atanasov, Nikolay Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)9781728190778

Many robot applications call for autonomous exploration and mapping of unknown and unstructured environments. Information-based exploration techniques, such as Cauchy-Schwarz quadratic mutual information (CSQMI) and fast Shannon mutual information (FSMI), have successfully achieved active binary occupancy mapping with range measurements. However, as we envision robots performing complex tasks specified with semantically meaningful objects, it is necessary to capture semantic categories in the measurements, map representation, and exploration objective. This work develops a Bayesian multi-class mapping algorithm utilizing rangecategory measurements. We derive a closed-form efficiently computable lower bound for the Shannon mutual information between the multi-class map and the measurements. The bound allows rapid evaluation of many potential robot trajectories for autonomous exploration and mapping. We compare our method against frontier-based and FSMI exploration and apply it in a 3-D photo-realistic simulation environment.

关键词： Solid modeling Uncertainty Semantics Probabilistic logic Bayes methods Trajectory Velocity measurement

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Autonomous Vehicle Motion Planning via Recurrent Spline Optimization

Autonomous Vehicle Motion Planning via Recurrent Spline Opti...

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IEEE international conference on robotics and automation (ICRA)

作者： Xu, Wenda Wang, Qian Dolan, John M. Carnegie Mellon Univ ECE Pittsburgh PA 15213 USA Clemson Univ Int Ctr Automot Res CU ICAR Appl Dynam & Control Grp Greenville SC 29607 USA Carnegie Mellon Univ Fac Robot Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9781728190778

Trajectory planning in dynamic environments can be decomposed into two sub-problems: 1) planning a path to avoid static obstacles, 2) then planning a speed profile to avoid dynamic obstacles. This is also called path-speed decomposition. In this work, we present a novel approach to solve the first sub-problem, motion planning with static obstacles. From an optimization perspective, motion planning for autonomous vehicles can be viewed as non-convex constrained nonlinear optimization, which requires a good enough initial guess to start and is often sensitive to algorithm parameters. We formulate motion planning as convex spline optimization. The convexity of the formulated problem makes it able to be solved fast and reliably, while guaranteeing a global optimum. We then reorganize the constrained spline optimization into a recurrent formulation, which further reduces the computational time to be linear in the optimization horizon size. The proposed method can be applied to both trajectory generation and motion planning problems. Its effectiveness is demonstrated in challenging scenarios such as tight lane changes and sharp turns.

关键词： Smoothing methods Three-dimensional displays Trajectory planning Dynamics Planning Splines (mathematics) Vehicle dynamics

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Shared Control of Robot-Robot Collaborative Lifting with Agent Postural and Force Ergonomic Optimization

Shared Control of Robot-Robot Collaborative Lifting with Age...

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IEEE international conference on robotics and automation (ICRA)

作者： Rapetti, Lorenzo Tirupachuri, Yeshasvi Ranavolo, Alberto Kawakami, Tomohiro Yoshiike, Takahide Pucci, Daniele Ist Italiano Tecnol Ctr Robot Technol Dynam Interact Control Genoa Italy Univ Manchester Machine Learning & Optimisat Manchester Lancs England INAIL Dept Occupat & Environm Med Epidemiol & Hyg Rome Italy Honda Res & Dev Co Ltd Saitama Japan

ISBN: (纸本)9781728190778

Humans show specialized strategies for efficient collaboration. Transferring similar strategies to humanoid robots can improve their capability to interact with other agents, leading the way to complex collaborative scenarios with multiple agents acting on a shared environment. In this paper we present a control framework for robot-robot collaborative lifting. The proposed shared controller takes into account the joint action of both the robots thanks to a centralized controller that communicates with them, and solves the whole-system optimization. Efficient collaboration is ensured by taking into account the ergonomic requirements of the robots through the optimization of posture and contact forces. The framework is validated in an experimental scenario with two iCub humanoid robots performing different payload lifting sequences.

关键词： Smoothing methods Ergonomics Force Collaboration Humanoid robots Grasping Torque measurement

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Autonomous Exploration of Unknown 3D Environments Using a Frontier-Based Collector Strategy

Autonomous Exploration of Unknown 3D Environments Using a Fr...

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IEEE international conference on robotics and automation (ICRA)

作者： Iván D. Changoluisa Caiza Ana Milas Marco A. Montes Grova Francisco Javier Pérez-Grau Tamara Petrovic University of Zagreb Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems Zagreb Croatia Advanced Center for Aerospace Technologies (CATEC) Aerospace Technology Park of Andalusia Seville Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Autonomous exploration using unmanned aerial vehicles (UAVs) is essential for various tasks such as building inspections, rescue operations, deliveries, and warehousing. However, there are two main limitations to previous approaches: they may not be able to provide a complete map of the environment and assume that the map built during exploration is accurate enough for safe navigation, which is usually not the case. To address these limitations, a novel exploration method is proposed that combines frontier-based exploration with a collector strategy that achieves global exploration and complete map creation. In each iteration, the collector strategy stores and validates frontiers detected during exploration and selects the next best frontier to navigate to. The collector strategy ensures global exploration by balancing the exploitation of a known map with the exploration of unknown areas. In addition, the online path replanning ensures safe navigation through the map created during motion. The performance of the proposed method is verified by exploring 3D simulation environments in comparison with the state-of-the-art methods. Finally, the proposed approach is validated in a real-world experiment.

关键词： Three-dimensional displays Video on demand Navigation Warehousing Buildings Inspection Web sites

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Lyapunov-Based Kinematic Anti-Prance Control for a Modular Rimless-Wheel Exploration Rover

Lyapunov-Based Kinematic Anti-Prance Control for a Modular R...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Walter Schindler German Aerospace Center (DLR) Institute of System Dynamics and Control Weßling Germany

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

The Scout rover is a novel rover concept developed by the German Aerospace Center (DLR) for the exploration of extreme extraterrestrial environments. Its locomotion platform is designed to tackle rough and rugged terrain, rock and boulder traverses, craters and cave systems on foreign celestial bodies. The Scout features a modular design with a compliant rover backbone and elastic rimless wheels. The backbone consists of rigid segments which are coupled by elastic vertebrae in-between. Under unfavorable conditions, the backbone deflects vertically, individual segments rear up and the rover begins to prance. Unintended backbone deformation and rearing-up negatively affect rover locomotion capabilities. This contribution presents a robust kinematic chassis controller for monitoring and controlling the backbone configuration. The backbone deformation is generically modeled upon an elementary backbone unit based on the nonlinear beam theory and a kinematic surrogate model. Lyapunov theory is used to synthesize a backbone controller upon the surrogate model for the elementary backbone unit. A kinematic chassis controller is established upon the elementary backbone controller in order to reduce unintended backbone deformations on chassis level. The overall controller is verified by means of test scenarios on different terrains on the Scout outdoor testbed. The controller significantly reduces backbone deformation on rough terrains.

关键词： Deformable models automation Deformation Wheels Kinematics Rocks Robots Monitoring

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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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Enhancing Autonomous Robot Safety through Localization Performance Analysis

Enhancing Autonomous Robot Safety through Localization Perfo...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Ulaş Süreyya Bingöl Akif Hacınecipoğlu Mustafa Mert Ankaralı Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Milvus Robotics Ankara Turkey Center for Robotics & AI Middle East Technical University Ankara Turkey

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Autonomous robots play a vital role in various applications, including industrial automation and logistics. Guaranteeing their safe and dependable operation is of utmost importance, particularly in human-robot collaborative scenarios. While existing safety standards address numerous aspects of autonomous systems, the persisting challenges linked to localization issues demand attention. Accurate localization is paramount for safety in autonomous robotic systems due to its direct impact on preventing accidents, mitigating risks, and ensuring human well-being. In scenarios where robots operate alongside humans, such as collaborative workspaces or shared environments, precise localization is crucial to avoid collisions and potential harm to individuals. Furthermore, robust localization enhances system dependability by minimizing errors and ensuring consistent performance, vital for industrial automation and logistics where disruptions can lead to costly downtime. To proactively manage these risks and ensure uninterrupted operations, this paper introduces a novel solution: the Localization Performance Analyzer (LPA). We present an architecture for estimating localization performance and a set of innovative features that serve as predictors of localization quality. Our findings indicate that implementing the LPA algorithm in a fleet of robots leads to more accurate estimations of localization performance. Our results from ablation studies demonstrate that integrating the LPA algorithm into a fleet of robots significantly enhances the accuracy of localization performance estimation. The predictor model and trainer is available at https://***/ulassbin/localization_performance_analyser.

关键词： Location awareness Measurement automation Accuracy Service robots Predictive models Feature extraction Prediction algorithms Safety Logistics

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A Coupling Enhanced Emergency Braking Approach for Double Pendulum Cranes

A Coupling Enhanced Emergency Braking Approach for Double Pe...

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2022 IEEE international conference on Real-Time Computing and robotics, RCAR 2022

作者： Chen, He Li, Mengyuan Wu, Yinan Hebei University of Technology School of Artificial Intelligence Tianjin300401 China Nankai University Inst. of Robotics and Automat. Info. System and the Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Tianjin300350 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Crane systems are commonly used transportation tools in industry to transport heavy cargoes to corresponding target positions. If the cargo shape is large which could not be regarded as a mass point or the hook mass also needs to be taken into account, the cranes would perform as double pendulum systems, whose dynamics are more complex than those of single pendulum systems. Since the working environment for crane systems can be very complex in practice, unavoidable emergency situations may occur, which are very dangerous. To avoid possible accidents, effective emergency braking methods are urgently needed, especially for double pendulum cranes with large cargoes. Considering this fact, this paper presents a coupling enhanced braking strategy for double pendulum cranes, which stops the trolley fast and achieves double pendulum vibration elimination objectives simultaneously. Specifically, an elaborately designed variable consisting of the trolley movement, the hook's swing, and the payload's swing, is utilized as feedback, which enhances the system couplings and improves the vibration elimination ability. Some constraint items are also introduced to restrict the payload in a safety domain and further avoid possible collisions. The effectiveness is rigorously analyzed and proved by mathematical analysis. Some experiments are executed to show the performance of the designed strategy. © 2022 IEEE.

关键词： Cranes

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