Robotica最新文献_第6页

A multi-stage localization framework for accurate and precise docking of autonomous mobile robots (AMRs) 用于自主移动机器人（AMR）精确对接的多级定位框架

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-05-03 DOI: 10.1017/s0263574724000602

Abdurrahman Yilmaz, Hakan Temeltas

Autonomous navigation has been a long-standing research topic, and researchers have worked on many challenging problems in indoor and outdoor environments. One application area of navigation solutions is material handling in industrial environments. With Industry 4.0, the simple problem in traditional factories has evolved into the use of autonomous mobile robots within flexible production islands in a self-decision-making structure. Two main stages of such a navigation system are safe transportation of the vehicle from one point to another and reaching destinations at industrial standards. The main concern in the former is roughly determining the vehicle’s pose to follow the route, while the latter aims to reach the target with high accuracy and precision. Often, it may not be possible or require extra effort to satisfy requirements with a single localization method. Therefore, a multi-stage localization approach is proposed in this study. Particle filter-based large-scale localization approaches are utilized during the vehicle’s movement from one point to another, while scan-matching-based methods are used in the docking stage. The localization system enables the appropriate approach based on the vehicle’s status and task through a decision-making mechanism. The decision-making mechanism uses a similarity metric obtained through the correntropy criterion to decide when and how to switch from large-scale localization to precise localization. The feasibility and performance of the developed method are corroborated through field tests. These evaluations demonstrate that the proposed method accomplishes tasks with sub-centimeter and sub-degree accuracy and precision without affecting the operation of the navigation algorithms in real time.

自主导航是一个由来已久的研究课题，研究人员已经在室内和室外环境中解决了许多具有挑战性的问题。导航解决方案的一个应用领域是工业环境中的材料处理。随着工业 4.0 的发展，传统工厂中的简单问题已演变为在自主决策结构的柔性生产岛中使用自主移动机器人。这种导航系统的两个主要阶段是将车辆从一个点安全运送到另一个点，以及按照工业标准到达目的地。前者的主要关注点是大致确定车辆的姿态以遵循路线，而后者的目标则是高精度、高准确度地到达目标。通常情况下，单一的定位方法可能无法满足要求，或者需要付出额外的努力。因此，本研究提出了一种多阶段定位方法。在飞行器从一个点移动到另一个点的过程中使用基于粒子滤波器的大规模定位方法，而在对接阶段则使用基于扫描匹配的方法。定位系统通过决策机制，根据车辆的状态和任务选择适当的方法。决策机制使用通过熵准则获得的相似度量来决定何时以及如何从大规模定位切换到精确定位。实地测试证实了所开发方法的可行性和性能。这些评估结果表明，所提出的方法可以在不影响导航算法实时运行的情况下，以亚厘米和亚度的精度和准确度完成任务。

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引用次数: 0

Compliant variable admittance adaptive fixed-time sliding mode control for trajectory tracking of robotic manipulators 用于机器人操纵器轨迹跟踪的顺应式可变导纳自适应固定时间滑动模式控制

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-05-02 DOI: 10.1017/s0263574724000468

Hang Gao, Chao Ma, Xiaodong Zhang, Cheng Zhou

This paper presents a compliant variable admittance adaptive fixed-time sliding mode control (SMC) algorithm for trajectory tracking of robotic manipulators. Specifically, a compliant variable admittance algorithm and an adaptive fixed-time SMC algorithm are combined to construct a double-loop control structure. In the outer loop, the variable admittance algorithm is developed to adjust admittance parameters during a collision to minimize the collision time, which gives the robot compliance property and reduce the rigid collision influence. Then, by employing the Lyapunov theory and the fixed-time stability theory, a new nonsingular sliding mode manifold is proposed and an adaptive fixed-time SMC algorithm is presented in the inner loop. More precisely, this approach enables rapid convergence, enhanced steady-state tracking precision, and a settling time that is independent of system initial states. As a result, the effectiveness and improved performance of the proposed algorithm are demonstrated through extensive simulations and experimental results.

本文提出了一种用于机器人操纵器轨迹跟踪的顺变导纳自适应固定时间滑模控制（SMC）算法。具体来说，将顺应式可变导纳算法和自适应固定时间 SMC 算法相结合，构建了一个双环控制结构。在外环中，开发了可变导纳算法，在碰撞过程中调整导纳参数，使碰撞时间最小化，从而使机器人具有顺应性，减少刚性碰撞的影响。然后，利用 Lyapunov 理论和定时稳定性理论，提出了一种新的非奇异滑模流形，并在内环中提出了一种自适应定时 SMC 算法。更确切地说，这种方法能实现快速收敛、更高的稳态跟踪精度以及与系统初始状态无关的稳定时间。因此，通过大量的模拟和实验结果，证明了所提算法的有效性和性能改进。

引用次数: 0

Learning vision-based robotic manipulation tasks sequentially in offline reinforcement learning settings 在离线强化学习设置中按顺序学习基于视觉的机器人操纵任务

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-05-02 DOI: 10.1017/s0263574724000389

Sudhir Pratap Yadav, Rajendra Nagar, Suril V. Shah

With the rise of deep reinforcement learning (RL) methods, many complex robotic manipulation tasks are being solved. However, harnessing the full power of deep learning requires large datasets. Online RL does not suit itself readily into this paradigm due to costly and time-consuming agent-environment interaction. Therefore, many offline RL algorithms have recently been proposed to learn robotic tasks. But mainly, all such methods focus on a single-task or multitask learning, which requires retraining whenever we need to learn a new task. Continuously learning tasks without forgetting previous knowledge combined with the power of offline deep RL would allow us to scale the number of tasks by adding them one after another. This paper investigates the effectiveness of regularisation-based methods like synaptic intelligence for sequentially learning image-based robotic manipulation tasks in an offline-RL setup. We evaluate the performance of this combined framework against common challenges of sequential learning: catastrophic forgetting and forward knowledge transfer. We performed experiments with different task combinations to analyse the effect of task ordering. We also investigated the effect of the number of object configurations and the density of robot trajectories. We found that learning tasks sequentially helps in the retention of knowledge from previous tasks, thereby reducing the time required to learn a new task. Regularisation-based approaches for continuous learning, like the synaptic intelligence method, help mitigate catastrophic forgetting but have shown only limited transfer of knowledge from previous tasks.

随着深度强化学习（RL）方法的兴起，许多复杂的机器人操纵任务正在得到解决。然而，要充分发挥深度学习的威力，需要大量的数据集。由于代理与环境之间的交互成本高、耗时长，在线强化学习并不适合这种模式。因此，最近提出了许多离线 RL 算法来学习机器人任务。但主要而言，所有这些方法都侧重于单任务或多任务学习，每当我们需要学习新任务时，都需要重新训练。在不遗忘先前知识的情况下持续学习任务，再加上离线深度 RL 的强大功能，我们就可以通过一个接一个地添加任务来扩展任务数量。本文研究了基于正则化的方法（如突触智能）在离线 RL 设置中连续学习基于图像的机器人操作任务的有效性。我们针对顺序学习中常见的挑战：灾难性遗忘和前向知识转移，对这一组合框架的性能进行了评估。我们进行了不同任务组合的实验，以分析任务排序的影响。我们还研究了物体配置数量和机器人轨迹密度的影响。我们发现，按顺序学习任务有助于保留之前任务的知识，从而减少学习新任务所需的时间。基于正则化的持续学习方法（如突触智能法）有助于减轻灾难性遗忘，但对先前任务知识的迁移却十分有限。

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引用次数: 0

Modeling, simulations and experiments with generally routed cable-driven continuum robots and their application to three-fingered grippers 一般线缆驱动连续机器人的建模、模拟和实验及其在三指抓手中的应用

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-04-30 DOI: 10.1017/s0263574724000651

Soumya Kanti Mahapatra, Ashitava Ghosal

This paper deals with generally routed, pre-bent cable-driven continuum robots (CCR). A CCR consists of a flexible backbone to which multiple disks are attached. Cables are passed through holes in the disk, and when pulled, the flexible backbone and the CCR can attain different shapes based on their routing and backbone configuration. An optimization-based approach, using minimization of strain energy, is shown to give good results for the pose and motion of the CCR and to determine contact with external objects. The pose, motion, and the contact obtained from the model are shown to match very well with experimental results obtained from a 3D-printed CCR. An algorithm is proposed to generate the pre-bent backbone for a CCR which on actuation can attain the desired shape. Using the algorithm, three 3D-printed CCRs with pre-bent backbones are fabricated and these are used to demonstrate a compliant gripper that can grip a spherical object similar to that done by tentacles, and another three-fingered gripper with straight backbone CCRs is used to orient a square object gripped at the end.

本文讨论的是一般路由、预弯电缆驱动连续机器人（CCR）。CCR 由一根柔性主干组成，主干上连接着多个圆盘。电缆穿过圆盘上的孔，拉动时，柔性主干和 CCR 可根据其布线和主干配置获得不同的形状。研究表明，基于应变能最小化的优化方法可以很好地确定 CCR 的姿态和运动，并确定与外部物体的接触。从模型中获得的姿势、运动和接触情况与三维打印 CCR 的实验结果非常吻合。还提出了一种算法，用于生成 CCR 的预弯曲骨架，该骨架在驱动时可达到所需的形状。利用该算法，制造出了三个具有预弯曲骨干的三维打印 CCR，并用它们演示了一个可抓取球形物体（类似于触手的抓取）的顺应性抓手，以及另一个具有直骨干 CCR 的三指抓手，用于确定末端抓取的方形物体的方向。

引用次数: 0

An asymmetric S-curve trajectory planning based on an improved jerk profile 基于改进的颠簸曲线的非对称 S 曲线轨迹规划

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-04-29 DOI: 10.1017/s0263574724000031

Chol Jun Han, Kwang Rim Song, Un-Ryong Rim

In this paper, a method of planning the expanded S-curve trajectory of robotic manipulators is proposed to minimize the execution time as well as to achieve the smoother trajectory generation in the deceleration stage for point-to-point motions. An asymmetric parameter is added to the piecewise sigmoid function for an improved jerk profile. This asymmetric profile is continuous and infinitely differentiable. Based on this profile, two analytical algorithms are presented. One is applied to determine the suitable time intervals of trajectory satisfying the time optimality under the kinematic constraints, and the other is to determine the asymmetric parameter generating the minimum execution time. Also, the calculation procedure for the time-scaled synchronization for all joints is given to decrease unnecessary loads onto the actuators. The velocity, acceleration, jerk and snap (the derivative of jerk) of the joints and the end-effector are equal to zero at two end points of motion. The simulation results through 3 DOF and 6 DOF robotic manipulators show that our approach reduces the jerk and snap of the deceleration stage effectively while decreasing the total execution time. Also, the analysis for a single DOF mass-spring-damper system indicates that the residual vibration could be reduced to 10% more than the benchmark techniques in case velocity, acceleration and jerk are limited to 1.24 m/s, 6 m/s2 and 80 m/s3, respectively and displacement is set to 0.8m. These results manifest that the performance of reducing residual vibrations is good and demonstrate an important characteristic of the proposed profile suitable for point-to-point motion.

本文提出了一种规划机器人机械手扩展 S 曲线轨迹的方法，以最大限度地减少执行时间，并在点对点运动的减速阶段实现更平滑的轨迹生成。在片状 sigmoid 函数中添加了一个非对称参数，以改善挺举曲线。这种非对称曲线是连续和无限可微的。在此基础上，提出了两种分析算法。一种算法用于确定在运动学约束条件下满足时间最优性的合适轨迹时间间隔，另一种算法用于确定产生最短执行时间的非对称参数。此外，还给出了所有关节的时标同步计算程序，以减少执行器上不必要的负载。在运动的两个端点，关节和末端执行器的速度、加速度、颠簸和快度（颠簸的导数）都等于零。3 DOF 和 6 DOF 机械手的仿真结果表明，我们的方法在减少总执行时间的同时，有效降低了减速阶段的颠簸和卡滞。此外，对单 DOF 质量-弹簧-阻尼系统的分析表明，在速度、加速度和颠簸分别限制为 1.24 m/s、6 m/s2 和 80 m/s3 且位移设置为 0.8m 的情况下，残余振动可比基准技术减少 10%。这些结果表明，减少残余振动的性能良好，并证明了所提出的轮廓适合点对点运动的一个重要特征。

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引用次数: 0

A visual SLAM-based lightweight multi-modal semantic framework for an intelligent substation robot 基于视觉 SLAM 的智能变电站机器人轻量级多模态语义框架

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-04-19 DOI: 10.1017/s0263574724000511

Shaohu Li, Jason Gu, Zhijun Li, Shaofeng Li, Bixiang Guo, Shangbing Gao, Feng Zhao, Yuwei Yang, Guoxin Li, Lanfang Dong

Visual simultaneous localisation and mapping (vSLAM) has shown considerable promise in positioning and navigating across a variety of indoor and outdoor settings, significantly enhancing the mobility of robots employed in industrial and everyday services. Nonetheless, the prevalent reliance of vSLAM technology on the assumption of static environments has led to suboptimal performance in practical implementations, particularly in unstructured and dynamically noisy environments such as substations. Despite advancements in mitigating the influence of dynamic objects through the integration of geometric and semantic information, existing approaches have struggled to strike an equilibrium between performance and real-time responsiveness. This study introduces a lightweight, multi-modal semantic framework predicated on vSLAM, designed to enable intelligent robots to adeptly navigate the dynamic environments characteristic of substations. The framework notably enhances vSLAM performance by mitigating the impact of dynamic objects through a synergistic combination of object detection and instance segmentation techniques. Initially, an enhanced lightweight instance segmentation network is deployed to ensure both the real-time responsiveness and accuracy of the algorithm. Subsequently, the algorithm’s performance is further refined by amalgamating the outcomes of detection and segmentation processes. With a commitment to maximising performance, the framework also ensures the algorithm’s real-time capability. Assessments conducted on public datasets and through empirical experiments have demonstrated that the proposed method markedly improves both the accuracy and real-time performance of vSLAM in dynamic environments.

视觉同步定位和绘图（vSLAM）技术在各种室内外环境中的定位和导航方面显示出了巨大的潜力，大大提高了工业和日常服务机器人的机动性。然而，由于 vSLAM 技术普遍依赖于静态环境假设，因此在实际应用中，尤其是在变电站等非结构化和动态嘈杂的环境中，其性能并不理想。尽管通过整合几何和语义信息在减轻动态物体影响方面取得了进步，但现有方法仍难以在性能和实时响应能力之间取得平衡。本研究在 vSLAM 的基础上引入了一个轻量级、多模态语义框架，旨在使智能机器人能够熟练地在变电站特有的动态环境中导航。该框架通过对象检测和实例分割技术的协同组合，减轻了动态对象的影响，从而显著提高了 vSLAM 的性能。首先，部署了一个增强型轻量级实例分割网络，以确保算法的实时响应性和准确性。随后，通过合并检测和分割过程的结果，进一步完善算法的性能。由于致力于最大限度地提高性能，该框架还确保了算法的实时性。在公共数据集上进行的评估和实证实验表明，所提出的方法显著提高了 vSLAM 在动态环境中的准确性和实时性。

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引用次数: 0

Radial basis function neural networks for formation control of unmanned aerial vehicles 用于无人飞行器编队控制的径向基函数神经网络

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-04-19 DOI: 10.1017/s0263574724000559

Duy-Nam Bui, Manh Duong Phung

This paper addresses the problem of controlling multiple unmanned aerial vehicles (UAVs) cooperating in a formation to carry out a complex task such as surface inspection. We first use the virtual leader-follower model to determine the topology and trajectory of the formation. A double-loop control system combining backstepping and sliding mode control techniques is then designed for the UAVs to track the trajectory. A radial basis function neural network capable of estimating external disturbances is developed to enhance the robustness of the controller. The stability of the controller is proven by using the Lyapunov theorem. A number of comparisons and software-in-the-loop tests have been conducted to evaluate the performance of the proposed controller. The results show that our controller not only outperforms other state-of-the-art controllers but is also sufficient for complex tasks of UAVs such as collecting surface data for inspection. The source code of our controller can be found at https://github.com/duynamrcv/rbf_bsmc.

本文探讨了如何控制多架无人驾驶飞行器（UAV）以编队形式合作执行复杂任务（如表面检测）的问题。我们首先使用虚拟领导者-追随者模型来确定编队的拓扑结构和轨迹。然后为无人机设计一个结合了反步进和滑模控制技术的双环控制系统，以跟踪轨迹。为了增强控制器的鲁棒性，还开发了一个能够估计外部干扰的径向基函数神经网络。利用 Lyapunov 定理证明了控制器的稳定性。为了评估所提出控制器的性能，我们进行了大量的比较和软件在环测试。结果表明，我们的控制器不仅性能优于其他最先进的控制器，而且足以胜任无人机的复杂任务，如采集表面数据进行检测。我们控制器的源代码可在 https://github.com/duynamrcv/rbf_bsmc 上找到。

引用次数: 0

Design and singularity analysis of a parallel mechanism with origami-inspired reconfigurable 5R closed-loop linkages 受折纸启发的可重构 5R 闭环连杆并联机构的设计与奇异性分析

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-04-19 DOI: 10.1017/s0263574724000560

Yili Kuang, Haibo Qu, Xiao Li, Xiaolei Wang, Sheng Guo

The reconfigurable mechanisms can satisfy the requirements of changing environments, working conditions, and tasks on the function and performance of the mechanism and can be applied to machine tool manufacturing, space detection, etc. Inspired by the single-vertex fivefold origami pattern, a new reconfigurable parallel mechanism is proposed in this paper, which has special singular positions and stable motion due to replicating the stabilizing kinematic properties of origami. Through analyzing the topologic change of the folding process of the pattern and treating it as a reconfigurable joint, a new reconfigurable parallel mechanism with 3, 4, 5, or 6 degrees of freedom is obtained. Then, the kinematics solution, workspace, and singularity of the mechanism are calculated. The results indicate that the singular configuration of the origami-derived reconfigurable parallel mechanism is mainly located in a special plane, and the scope of the workspace is still large after the configuration change. The mechanism has the potential to adapt to multiple tasks and working conditions through the conversion among different configurations by folding reconfigurable joints on the branch chain.

可重构机构可以满足不断变化的环境、工作条件和任务对机构功能和性能的要求，可应用于机床制造、空间探测等领域。受单顶点五折折纸图案的启发，本文提出了一种新的可重构并联机构，由于复制了折纸的稳定运动特性，该机构具有特殊的奇异位置和稳定的运动。通过分析图案折叠过程中的拓扑变化，并将其视为可重构的关节，得到了具有 3、4、5 或 6 个自由度的新型可重构并联机构。然后，计算了该机构的运动学解、工作空间和奇异性。结果表明，折纸衍生的可重构并联机构的奇异构型主要位于一个特殊平面内，构型改变后的工作空间范围仍然很大。通过折叠支链上的可重构关节实现不同构型之间的转换，该机构具有适应多种任务和工作条件的潜力。

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引用次数: 0

Research on welding seam tracking algorithm for automatic welding process of X-shaped tip of concrete piles using laser distance sensor 使用激光测距传感器的混凝土桩 X 型桩尖自动焊接过程中的焊缝跟踪算法研究

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-04-18 DOI: 10.1017/s0263574724000535

Cao Tri Huynh, Tri Cong Phung

The manufacturing of the X-shaped tip of prestressed centrifugal concrete piles is nowadays done half automatically by combining the manual worker and the automatic welding robot. To make this welding process full automatically, the welding seam tracking algorithm is considered. There are many types of sensors that can be used to detect the welding seam such as vision sensor, laser vision sensor, arc sensor, or touch sensor. Each type of sensor has its advantages and disadvantages. In this paper, an algorithm for welding seam tracking using laser distance sensor is proposed. Firstly, the fundamental mathematics theory of the algorithm is presented. Next, the positioning table system supports the procedure is designed and manufactured. The object of this research is the fillet joint because of the characteristics of the X-shaped tip of the concrete piles. This paper proposes a new method to determine the welding trajectory of the tip using laser distance sensor. After that, the experimental results are received to verify the proposed idea. Finally, the improved proposal of the algorithm is considered to increase the accuracy of the suggested algorithm.

如今，预应力离心混凝土管桩 X 形尖端的制造是通过人工和自动焊接机器人相结合的方式半自动完成的。为了实现全自动焊接，需要考虑焊缝跟踪算法。有多种类型的传感器可用于检测焊缝，如视觉传感器、激光视觉传感器、电弧传感器或触摸传感器。每种传感器都有其优缺点。本文提出了一种利用激光测距传感器进行焊缝跟踪的算法。首先，介绍了算法的基本数学理论。然后，设计并制造了支持该程序的定位台系统。由于混凝土桩 X 型桩尖的特点，本研究的对象是圆角接头。本文提出了一种利用激光测距传感器确定桩尖焊接轨迹的新方法。然后，通过实验结果来验证所提出的想法。最后，考虑了改进算法的建议，以提高建议算法的准确性。

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引用次数: 0

Multi-objective optimal trajectory planning for robot manipulator attention to end-effector path limitation 关注末端执行器路径限制的机器人操纵器多目标优化轨迹规划

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-04-17 DOI: 10.1017/s0263574724000481

Jintao Ye, Lina Hao, Hongtai Cheng

In the process of trajectory optimization for robot manipulator, the path that is generated may deviate from the intended path because of the adjustment of trajectory parameters, if there is limitation of end-effector path in Cartesian space for specific tasks, this phenomenon is dangerous. This paper proposes a methodology that is based on the Pareto front to address this issue, and the methodology takes into account both the multi-objective optimization of robotic arm and the quality of end-effector path. Based on dung beetle optimizer, this research proposes improved non-dominated sorting dung beetle optimizer. This paper interpolates manipulator trajectory with quintic B-spline curves, achieves multi-objective trajectory optimization that simultaneously optimizes traveling time, energy consumption, and mean jerk, proposes a trajectory selection strategy that is based on Pareto solution set by introducing the concept of Fréchet distance, and the strategy enables the end-effector to approach the desired path in Cartesian space. Simulation and experimental results validate the effectiveness and practicability of the proposed methodology on the Sawyer robot manipulator.

在机器人机械手的轨迹优化过程中，由于轨迹参数的调整，生成的路径可能会偏离预定路径，如果特定任务的末端执行器路径在笛卡尔空间存在限制，这种现象就很危险。本文提出了一种基于帕累托前沿的方法来解决这一问题，该方法既考虑了机械臂的多目标优化，又考虑了末端执行器路径的质量。在蜣螂优化器的基础上，本研究提出了改进的非支配排序蜣螂优化器。本文用五次 B-样条曲线对机械手轨迹进行插值，实现了同时优化行走时间、能耗和平均挺举的多目标轨迹优化，并通过引入弗雷谢特距离的概念，提出了一种基于帕累托解集的轨迹选择策略，该策略可使末端执行器在笛卡尔空间中接近所需的路径。仿真和实验结果验证了所提方法在 Sawyer 机器人操纵器上的有效性和实用性。

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引用次数: 0