Pub Date : 2024-10-29DOI: 10.1109/LRA.2024.3487512
Kailuan Tang;Shaowu Tang;Chenghua Lu;Shijian Wu;Sicong Liu;Juan Yi;Jian S. Dai;Zheng Wang
Interactions with environmental objects can induce substantial alterations in both exteroceptive and proprioceptive signals. However, the deployment of exteroceptive sensors within underwater soft manipulators encounters numerous challenges and constraints, thereby imposing limitations on their perception capabilities. In this article, we present a novel learning-based exteroceptive approach that utilizes internal proprioceptive signals and harnesses the principles of soft actuator network (SAN). Deformation and vibration resulting from external collisions tend to propagate through the SANs in underwater soft manipulators and can be detected by proprioceptive sensors. We extract features from the sensor signals and develop a fully-connected neural network (FCNN)-based classifier to determine collision positions. We have constructed a training dataset and an independent validation dataset for the purpose of training and validating the classifier. The experimental results affirm that the proposed method can identify collision locations with an accuracy level of 97.11% using the independent validation dataset, which exhibits potential applications within the domain of underwater soft robotics perception and control.
与环境物体的相互作用会引起外部感觉和本体感觉信号的巨大变化。然而，在水下软机械手中部署外感知传感器会遇到许多挑战和限制，从而对其感知能力造成限制。在这篇文章中，我们提出了一种基于学习的新型外感知方法，它利用内部本体感觉信号和软致动器网络（SAN）原理。外部碰撞产生的变形和振动往往会通过水下软体机械手的 SAN 传播，并可被本体感觉传感器检测到。我们从传感器信号中提取特征，并开发了基于全连接神经网络（FCNN）的分类器来确定碰撞位置。我们构建了一个训练数据集和一个独立的验证数据集，用于训练和验证分类器。实验结果表明，利用独立验证数据集，所提出的方法能够以 97.11% 的准确率识别碰撞位置，在水下软机器人感知和控制领域具有潜在的应用前景。
{"title":"Learning Based Exteroception of Soft Underwater Manipulator With Soft Actuator Network","authors":"Kailuan Tang;Shaowu Tang;Chenghua Lu;Shijian Wu;Sicong Liu;Juan Yi;Jian S. Dai;Zheng Wang","doi":"10.1109/LRA.2024.3487512","DOIUrl":"https://doi.org/10.1109/LRA.2024.3487512","url":null,"abstract":"Interactions with environmental objects can induce substantial alterations in both exteroceptive and proprioceptive signals. However, the deployment of exteroceptive sensors within underwater soft manipulators encounters numerous challenges and constraints, thereby imposing limitations on their perception capabilities. In this article, we present a novel learning-based exteroceptive approach that utilizes internal proprioceptive signals and harnesses the principles of soft actuator network (SAN). Deformation and vibration resulting from external collisions tend to propagate through the SANs in underwater soft manipulators and can be detected by proprioceptive sensors. We extract features from the sensor signals and develop a fully-connected neural network (FCNN)-based classifier to determine collision positions. We have constructed a training dataset and an independent validation dataset for the purpose of training and validating the classifier. The experimental results affirm that the proposed method can identify collision locations with an accuracy level of 97.11% using the independent validation dataset, which exhibits potential applications within the domain of underwater soft robotics perception and control.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11082-11089"},"PeriodicalIF":4.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This letter presents Open-Structure, a novel benchmark dataset for evaluating visual odometry and SLAM methods. Compared to existing public datasets that primarily offer raw images, Open-Structure provides direct access to point and line measurements, correspondences, structural associations, and co-visibility factor graphs, which can be fed to various stages of SLAM pipelines to mitigate the impact of data preprocessing modules in ablation experiments. The dataset comprises two distinct types of sequences from the perspective of scenarios. The first type maintains reasonable observation and occlusion relationships, as these critical elements are extracted from public image-based sequences using our dataset generator. In contrast, the second type consists of carefully designed simulation sequences that enhance dataset diversity by introducing a wide range of trajectories and observations. Furthermore, a baseline is proposed using our dataset to evaluate widely used modules, including camera pose tracking, parametrization, and factor graph optimization, within SLAM systems. By evaluating these state-of-the-art algorithms across different scenarios, we discern each module's strengths and weaknesses in the context of camera tracking and optimization processes.
本文介绍了用于评估视觉里程测量和 SLAM 方法的新型基准数据集 Open-Structure。与主要提供原始图像的现有公共数据集相比，Open-Structure 数据集可直接获取点和线的测量结果、对应关系、结构关联和共视因子图，这些数据可输入 SLAM 管道的各个阶段，以减轻消融实验中数据预处理模块的影响。从场景的角度来看，该数据集包括两种不同类型的序列。第一种类型保持了合理的观察和遮挡关系，因为这些关键要素是利用我们的数据集生成器从基于图像的公共序列中提取的。相比之下，第二种类型由精心设计的模拟序列组成，通过引入各种轨迹和观测数据来增强数据集的多样性。此外，我们还提出了一个基线，利用我们的数据集来评估 SLAM 系统中广泛使用的模块，包括相机姿态跟踪、参数化和因子图优化。通过在不同场景下对这些先进算法进行评估，我们发现了每个模块在摄像机跟踪和优化过程中的优缺点。
{"title":"Open-Structure: Structural Benchmark Dataset for SLAM Algorithms","authors":"Yanyan Li;Zhao Guo;Ze Yang;Yanbiao Sun;Liang Zhao;Federico Tombari","doi":"10.1109/LRA.2024.3487071","DOIUrl":"https://doi.org/10.1109/LRA.2024.3487071","url":null,"abstract":"This letter presents Open-Structure, a novel benchmark dataset for evaluating visual odometry and SLAM methods. Compared to existing public datasets that primarily offer raw images, Open-Structure provides direct access to point and line measurements, correspondences, structural associations, and co-visibility factor graphs, which can be fed to various stages of SLAM pipelines to mitigate the impact of data preprocessing modules in ablation experiments. The dataset comprises two distinct types of sequences from the perspective of scenarios. The first type maintains reasonable observation and occlusion relationships, as these critical elements are extracted from public image-based sequences using our dataset generator. In contrast, the second type consists of carefully designed simulation sequences that enhance dataset diversity by introducing a wide range of trajectories and observations. Furthermore, a baseline is proposed using our dataset to evaluate widely used modules, including camera pose tracking, parametrization, and factor graph optimization, within SLAM systems. By evaluating these state-of-the-art algorithms across different scenarios, we discern each module's strengths and weaknesses in the context of camera tracking and optimization processes.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11457-11464"},"PeriodicalIF":4.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Complex operational scenarios increasingly demand that industrial robots sequentially resolve multiple interrelated problems to accomplish complex operational tasks, necessitating robots to have the capacity for not only learning through interaction with the environment but also for continual learning. Current deep reinforcement learning methods have demonstrated substantial prowess in enabling robots to learn individual simple operational skills. However, catastrophic forgetting regarding the continual learning of various distinct tasks under a unified control policy remains a challenge. The lengthy sequential decision-making trajectory in reinforcement learning scenarios results in a massive state-action search space for the agent. Moreover, low-value state-action samples exacerbate the difficulty of continuous learning in reinforcement learning problems. In this letter, we propose a Continual Reinforcement Learning (CRL) method that accommodates the incremental multiskill learning demands of robots. We transform the tightly coupled structure in Guided Policy Search (GPS) algorithms, which closely intertwine local and global policies, into a loosely coupled structure. This revised structure updates the global policy only after the local policy for a specific task has converged, enabling online learning. In incrementally learning new tasks, the global policy is updated using hard parameter sharing and Memory Aware Synapses (MAS), creating task-specific layers while penalizing significant parameter changes in shared layers linked to prior tasks. This method reduces overfitting and mitigates catastrophic forgetting in robotic CRL. We validate our method on PR2, UR5 and Sawyer robots in simulators as well as on a real UR5 robot.
{"title":"Mitigating Catastrophic Forgetting in Robot Continual Learning: A Guided Policy Search Approach Enhanced With Memory-Aware Synapses","authors":"Qingwei Dong;Peng Zeng;Yunpeng He;Guangxi Wan;Xiaoting Dong","doi":"10.1109/LRA.2024.3487484","DOIUrl":"https://doi.org/10.1109/LRA.2024.3487484","url":null,"abstract":"Complex operational scenarios increasingly demand that industrial robots sequentially resolve multiple interrelated problems to accomplish complex operational tasks, necessitating robots to have the capacity for not only learning through interaction with the environment but also for continual learning. Current deep reinforcement learning methods have demonstrated substantial prowess in enabling robots to learn individual simple operational skills. However, catastrophic forgetting regarding the continual learning of various distinct tasks under a unified control policy remains a challenge. The lengthy sequential decision-making trajectory in reinforcement learning scenarios results in a massive state-action search space for the agent. Moreover, low-value state-action samples exacerbate the difficulty of continuous learning in reinforcement learning problems. In this letter, we propose a Continual Reinforcement Learning (CRL) method that accommodates the incremental multiskill learning demands of robots. We transform the tightly coupled structure in Guided Policy Search (GPS) algorithms, which closely intertwine local and global policies, into a loosely coupled structure. This revised structure updates the global policy only after the local policy for a specific task has converged, enabling online learning. In incrementally learning new tasks, the global policy is updated using hard parameter sharing and Memory Aware Synapses (MAS), creating task-specific layers while penalizing significant parameter changes in shared layers linked to prior tasks. This method reduces overfitting and mitigates catastrophic forgetting in robotic CRL. We validate our method on PR2, UR5 and Sawyer robots in simulators as well as on a real UR5 robot.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11242-11249"},"PeriodicalIF":4.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-29DOI: 10.1109/LRA.2024.3487496
Charles Hamesse;Michiel Vlaminck;Hiep Luong;Rob Haelterman
We propose the D epth- V isual- I nertial (DVI) mapping system: a robust multi-sensor fusion framework for dense 3D mapping using time-of-flight cameras equipped with RGB and IMU sensors. Inspired by recent developments in real-time LiDAR-based odometry and mapping, our system uses an error-state iterative Kalman filter for state estimation: it processes the inertial sensor's data for state propagation, followed by a state update first using visual-inertial odometry, then depth-based odometry. This sensor fusion scheme makes our system robust to degenerate scenarios (e.g. lack of visual or geometrical features, fast rotations) and to noisy sensor data, like those that can be obtained with off-the-shelf time-of-flight DVI sensors. For evaluation, we propose the new Bunker DVI Dataset, featuring data from multiple DVI sensors recorded in challenging conditions reflecting search-and-rescue operations. We show the superior robustness and precision of our method against previous work. Following the open science principle, we make both our source code and dataset publicly available.
我们提出了深度-视觉-惯性（DVI）测绘系统：这是一种稳健的多传感器融合框架，用于使用配备了 RGB 和 IMU 传感器的飞行时间照相机进行密集 3D 测绘。受基于激光雷达的实时里程测量和制图的最新发展的启发，我们的系统使用误差状态迭代卡尔曼滤波器进行状态估计：它处理惯性传感器的数据进行状态传播，然后首先使用视觉惯性里程测量进行状态更新，接着使用基于深度的里程测量进行状态更新。这种传感器融合方案使我们的系统对退化场景（如缺乏视觉或几何特征、快速旋转）和嘈杂的传感器数据（如使用现成的飞行时间 DVI 传感器获得的数据）具有鲁棒性。为了进行评估，我们提出了新的掩体 DVI 数据集，该数据集由多个 DVI 传感器在具有挑战性的条件下记录的数据组成，反映了搜救行动的情况。与之前的研究相比，我们的方法具有更高的鲁棒性和精确性。遵循开放科学原则，我们公开了源代码和数据集。
{"title":"Depth-Visual-Inertial (DVI) Mapping System for Robust Indoor 3D Reconstruction","authors":"Charles Hamesse;Michiel Vlaminck;Hiep Luong;Rob Haelterman","doi":"10.1109/LRA.2024.3487496","DOIUrl":"https://doi.org/10.1109/LRA.2024.3487496","url":null,"abstract":"We propose the \u0000<underline>D</u>\u0000epth-\u0000<underline>V</u>\u0000isual-\u0000<underline>I</u>\u0000nertial (DVI) mapping system: a robust multi-sensor fusion framework for dense 3D mapping using time-of-flight cameras equipped with RGB and IMU sensors. Inspired by recent developments in real-time LiDAR-based odometry and mapping, our system uses an error-state iterative Kalman filter for state estimation: it processes the inertial sensor's data for state propagation, followed by a state update first using visual-inertial odometry, then depth-based odometry. This sensor fusion scheme makes our system robust to degenerate scenarios (e.g. lack of visual or geometrical features, fast rotations) and to noisy sensor data, like those that can be obtained with off-the-shelf time-of-flight DVI sensors. For evaluation, we propose the new Bunker DVI Dataset, featuring data from multiple DVI sensors recorded in challenging conditions reflecting search-and-rescue operations. We show the superior robustness and precision of our method against previous work. Following the open science principle, we make both our source code and dataset publicly available.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11313-11320"},"PeriodicalIF":4.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This letter addresses the problem of controlling an autonomous agent to localize and circumnavigate a stationary or slowly moving target in the presence of an unknown wind disturbance. First, we introduce a novel wind estimator that utilizes bearing-only measurements to adaptively estimate the wind velocity. Then, we develop an estimator-coupled circumnavigation controller to mitigate wind effects, enabling the agent to move in a circular orbit centered at the target with a predefined radius. We analytically prove that the estimation and control errors are locally exponentially convergent when the wind is constant and the target is stationary. Then, the robustness of the method is evaluated for a slowly moving target and time-varying wind. It is shown that the circumnavigation errors converge to small neighborhoods of the origin whose sizes depend on the velocities of the wind and that of the target. Comprehensive simulation and experiments using unmanned aerial vehicles (UAVs) illustrate the efficacy of the proposed estimator and controller.
{"title":"Adaptive Bearing-Only Target Localization and Circumnavigation Under Unknown Wind Disturbance: Theory and Experiments","authors":"Donglin Sui;Mohammad Deghat;Zhiyong Sun;Mohsen Eskandari","doi":"10.1109/LRA.2024.3487483","DOIUrl":"https://doi.org/10.1109/LRA.2024.3487483","url":null,"abstract":"This letter addresses the problem of controlling an autonomous agent to localize and circumnavigate a stationary or slowly moving target in the presence of an unknown wind disturbance. First, we introduce a novel wind estimator that utilizes bearing-only measurements to adaptively estimate the wind velocity. Then, we develop an estimator-coupled circumnavigation controller to mitigate wind effects, enabling the agent to move in a circular orbit centered at the target with a predefined radius. We analytically prove that the estimation and control errors are locally exponentially convergent when the wind is constant and the target is stationary. Then, the robustness of the method is evaluated for a slowly moving target and time-varying wind. It is shown that the circumnavigation errors converge to small neighborhoods of the origin whose sizes depend on the velocities of the wind and that of the target. Comprehensive simulation and experiments using unmanned aerial vehicles (UAVs) illustrate the efficacy of the proposed estimator and controller.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11321-11328"},"PeriodicalIF":4.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-29DOI: 10.1109/LRA.2024.3487489
Maciej K. Wozniak;Mattias Hansson;Marko Thiel;Patric Jensfelt
In this study, we address a gap in existing unsupervised domain adaptation approaches on LiDAR-based 3D object detection, which have predominantly concentrated on adapting between established, high-density autonomous driving datasets. We focus on sparser point clouds, capturing scenarios from different perspectives: not just from vehicles on the road but also from mobile robots on sidewalks, which encounter significantly different environmental conditions and sensor configurations. We introduce Unsupervised Adversarial Domain Adaptation for 3D Object Detection ( UADA3D ). UADA3D does not depend on pre-trained source models or teacher-student architectures. Instead, it uses an adversarial approach to directly learn domain-invariant features. We demonstrate its efficacy in various adaptation scenarios, showing significant improvements in both self-driving car and mobile robot domains.
{"title":"UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection With Sparse LiDAR and Large Domain Gaps","authors":"Maciej K. Wozniak;Mattias Hansson;Marko Thiel;Patric Jensfelt","doi":"10.1109/LRA.2024.3487489","DOIUrl":"https://doi.org/10.1109/LRA.2024.3487489","url":null,"abstract":"In this study, we address a gap in existing unsupervised domain adaptation approaches on LiDAR-based 3D object detection, which have predominantly concentrated on adapting between established, high-density autonomous driving datasets. We focus on sparser point clouds, capturing scenarios from different perspectives: not just from vehicles on the road but also from mobile robots on sidewalks, which encounter significantly different environmental conditions and sensor configurations. We introduce Unsupervised Adversarial Domain Adaptation for 3D Object Detection (\u0000<bold>UADA3D</b>\u0000). UADA3D does not depend on pre-trained source models or teacher-student architectures. Instead, it uses an adversarial approach to directly learn domain-invariant features. We demonstrate its efficacy in various adaptation scenarios, showing significant improvements in both self-driving car and mobile robot domains.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11210-11217"},"PeriodicalIF":4.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1109/LRA.2024.3484113
{"title":"IEEE Robotics and Automation Society Information","authors":"","doi":"10.1109/LRA.2024.3484113","DOIUrl":"https://doi.org/10.1109/LRA.2024.3484113","url":null,"abstract":"","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 11","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737235","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1109/LRA.2024.3484115
{"title":"IEEE Robotics and Automation Letters Information for Authors","authors":"","doi":"10.1109/LRA.2024.3484115","DOIUrl":"https://doi.org/10.1109/LRA.2024.3484115","url":null,"abstract":"","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 11","pages":"C4-C4"},"PeriodicalIF":4.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1109/LRA.2024.3487499
Huichen Ma;Junjie Zhou;Chen-Hua Yeow;Lijun Meng
In order to achieve a significant reduction in state estimation error and improved convergence speed, ensuring real-time responsiveness and computational efficiency, this article proposes a joint approach that combines dynamic modeling and observers to achieve accurate nonlinear state estimation of the functional soft actuator. First, inspired by the viscoelastic model, a general framework for modeling the 2D dynamics of the pneumatic network soft actuator under external conditions was studied. The dimensionless dynamic model of the soft actuator's bending deformation is derived through the dimensional analysis method. Then, an adaptive extended Kalman particle filter (aEKPF) is used for state estimation. It can restrain noise from pressure sensors and reduce drift error from rate gyroscopes. The closed-loop performance of the nonlinear pose estimation combined with the conventional control method was experimentally assessed using soft actuators and soft crawling. Results show that the aEKPF can accurately estimate the state from noise sensor measurements. Compared with conventional EKF, aEKPF improves the performance by more than 50% in terms of state estimation error and convergence speed. At the same time, in the rectilinear crawling test, the mean centroid offset in different environments is less than 3% of the soft crawling module width, verifying the effectiveness and robustness of this strategy in accurate state estimation and stability control.
{"title":"State Estimation by Joint Approach With Dynamic Modeling and Observer for Soft Actuator","authors":"Huichen Ma;Junjie Zhou;Chen-Hua Yeow;Lijun Meng","doi":"10.1109/LRA.2024.3487499","DOIUrl":"https://doi.org/10.1109/LRA.2024.3487499","url":null,"abstract":"In order to achieve a significant reduction in state estimation error and improved convergence speed, ensuring real-time responsiveness and computational efficiency, this article proposes a joint approach that combines dynamic modeling and observers to achieve accurate nonlinear state estimation of the functional soft actuator. First, inspired by the viscoelastic model, a general framework for modeling the 2D dynamics of the pneumatic network soft actuator under external conditions was studied. The dimensionless dynamic model of the soft actuator's bending deformation is derived through the dimensional analysis method. Then, an adaptive extended Kalman particle filter (aEKPF) is used for state estimation. It can restrain noise from pressure sensors and reduce drift error from rate gyroscopes. The closed-loop performance of the nonlinear pose estimation combined with the conventional control method was experimentally assessed using soft actuators and soft crawling. Results show that the aEKPF can accurately estimate the state from noise sensor measurements. Compared with conventional EKF, aEKPF improves the performance by more than 50% in terms of state estimation error and convergence speed. At the same time, in the rectilinear crawling test, the mean centroid offset in different environments is less than 3% of the soft crawling module width, verifying the effectiveness and robustness of this strategy in accurate state estimation and stability control.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11706-11713"},"PeriodicalIF":4.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1109/LRA.2024.3487077
Yi Huang;Jiacheng Kuai;Shisheng Cui;Ziyang Meng;Jian Sun
This letter develops two distributed algorithms to solve multi-robot task assignment problems (MTAP). We first describe MTAP as an integer linear programming (ILP) problem and then reformulate it as a relaxed convex optimization problem. Based on the saddle-point dynamics, we propose two distributed optimization algorithms using optimistic gradient decent ascent (OGDA) and extra-gradient (EG) methods, which achieve exact convergence to an optimal solution of the relaxed problem. In most cases, such an solution reflects the optimality of the original ILP problems. For some special ILP problems, we provide a perturbation-based distributed method to avoid the inconsistency phenomenon, such that an optimal solution to any ILP problem is obtained. Compared with some decentralized algorithms requiring a central robot that communicates with the other robots, our developed algorithms are fully distributed, in which each robot only communicates with the nearest neighbors for an arbitrary connected graph. We evaluate the developed algorithms in terms of computation, communication, and data storage complexities, and compare them with some typical algorithms. It is shown that the developed algorithms have low computational and communication complexities. We also verify the effectiveness of our algorithms via numerical examples.
{"title":"Distributed Algorithms via Saddle-Point Dynamics for Multi-Robot Task Assignment","authors":"Yi Huang;Jiacheng Kuai;Shisheng Cui;Ziyang Meng;Jian Sun","doi":"10.1109/LRA.2024.3487077","DOIUrl":"https://doi.org/10.1109/LRA.2024.3487077","url":null,"abstract":"This letter develops two distributed algorithms to solve multi-robot task assignment problems (MTAP). We first describe MTAP as an integer linear programming (ILP) problem and then reformulate it as a relaxed convex optimization problem. Based on the saddle-point dynamics, we propose two distributed optimization algorithms using optimistic gradient decent ascent (OGDA) and extra-gradient (EG) methods, which achieve exact convergence to an optimal solution of the relaxed problem. In most cases, such an solution reflects the optimality of the original ILP problems. For some special ILP problems, we provide a perturbation-based distributed method to avoid the inconsistency phenomenon, such that an optimal solution to any ILP problem is obtained. Compared with some decentralized algorithms requiring a central robot that communicates with the other robots, our developed algorithms are fully distributed, in which each robot only communicates with the nearest neighbors for an arbitrary connected graph. We evaluate the developed algorithms in terms of computation, communication, and data storage complexities, and compare them with some typical algorithms. It is shown that the developed algorithms have low computational and communication complexities. We also verify the effectiveness of our algorithms via numerical examples.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11178-11185"},"PeriodicalIF":4.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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