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MR-FLOUR: Multi-robot Relative localization based on the Fusion of LiDAR, Odometry, and UWB Ranging MR-FLOUR：基于LiDAR， Odometry和UWB测距融合的多机器人相对定位

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-09-09 DOI: 10.1016/j.mechatronics.2025.103410

Muhammad Shalihan , Zhiqiang Cao , Billy Pik Lik Lau , Ran Liu , Chau Yuen , U-Xuan Tan

Accurate relative localization of multiple robots is crucial for efficient collaboration and teaming, where a prior map of the environment is often unavailable. In this context, proximal robot detection plays an important role in improving relative localization accuracy by providing essential spatial awareness. While LiDAR is a common choice for detecting nearby robots, it struggles to distinguish them from surrounding obstacles, especially in cluttered environments. To address this challenge, we introduce MR-FLOUR, which stands for Multiple-robot Relative localization based on the Fusion of LiDAR detection outcomes, Odometry, and UWB Ranging. The main innovation of our approach is the use of different sensors for proximal robot detection and the introduction of our LiDAR detection constraint for optimization. First, we propose an efficient method to integrate UWB ranging with LiDAR data for proximal robot detection. We cluster the LiDAR point cloud and apply circle-fitting on the clusters based on the expected radius of the robot to reject clusters that do not conform to the expected shape of the robot. Then match the UWB ranging with cluster distances to determine nearby robot positions. Next, we estimate the identified robot’s orientation from successive detections, with outliers filtered using short-term odometry data. Finally, through Pose Graph Optimization (PGO), we fuse odometry and UWB ranging constraints with our proposed LiDAR detection constraint, which not only accounts for the position and orientation estimations of the nearby robots but also incorporates the relative pose estimation between them. Our method improves the localization accuracy of traditional UWB localization by incorporating LiDAR detection constraints when in Line-Of-Sight (LOS). In Non-Line-Of-Sight (NLOS) conditions or when no nearby robot detections are available, it relies on UWB and odometry for localization. We validated the approach with three robots in three indoor environments, achieving up to 33.3% improvement in translation and 45.5% in rotation over traditional UWB localization.

多个机器人的精确相对定位对于有效的协作和团队合作至关重要，因为通常无法获得事先的环境地图。在这种情况下，近端机器人检测通过提供必要的空间感知，在提高相对定位精度方面发挥着重要作用。虽然激光雷达是检测附近机器人的常用选择，但它很难将它们与周围的障碍物区分开来，尤其是在混乱的环境中。为了应对这一挑战，我们引入了MR-FLOUR，即基于LiDAR检测结果、Odometry和UWB测距融合的多机器人相对定位。我们的方法的主要创新是使用不同的传感器进行近端机器人检测，并引入我们的激光雷达检测约束进行优化。首先，我们提出了一种有效的方法，将超宽带测距与激光雷达数据相结合，用于近端机器人检测。我们对LiDAR点云进行聚类，并根据机器人的期望半径对聚类进行圆拟合，以剔除不符合机器人期望形状的聚类。然后将超宽带测距与集群距离进行匹配，以确定附近机器人的位置。接下来，我们从连续的检测中估计识别机器人的方向，使用短期里程计数据过滤异常值。最后，通过姿态图优化（PGO），我们将距离测量和超宽带距离约束与我们提出的激光雷达检测约束融合在一起，不仅考虑了附近机器人的位置和方向估计，还结合了它们之间的相对姿态估计。该方法在视距（LOS）范围内结合激光雷达探测约束，提高了传统超宽带定位的精度。在非视距（NLOS）条件下或附近没有机器人检测时，它依靠超宽带和里程计进行定位。我们在三个室内环境中用三个机器人验证了该方法，与传统的UWB定位相比，平移速度提高了33.3%，旋转速度提高了45.5%。

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引用次数: 0

Soft paw sensor for tactile and force sensing in legged robots 用于有腿机器人触觉和力感的软爪传感器

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-09-03 DOI: 10.1016/j.mechatronics.2025.103407

Hugo A. Moreno , Luis A. Moreno , L.M. Valentín-Coronado , Gerardo Flores

The adaptability of legged robots to uneven terrain and their minimal ground impact have driven significant research advancements, establishing them as ideal solutions for complex and delicate environments. Tactile sensing and environmental perception are critical for enhancing robot performance, as they are essential for maintaining dynamic balance and achieving precise control. This paper presents a novel soft contact and force sensor designed for quadrupedal robot legs’ pads (end effectors). The innovative soft sensitive paw, made from flexible conductive membranes, simultaneously measures force and contact point position, enabling environmentally aware decision-making and supporting proprioceptive awareness. Experimental tests demonstrate its soft, spherical design provides excellent adaptability and grip on various terrains. Its sensing surface covers 83.3% of the sphere’s area, with a measurement error of only 0.14%. This capability allows the sensitive paw to detect ground contact as well as lateral and upper leg interactions, offering a robust and versatile tool for navigation and operation in complex environments. To validate its performance, the sensor was tested using custom-built test benches and subsequently mounted on the Lupoh quadruped robot, which was developed in our laboratory for further evaluation.

腿式机器人对不平坦地形的适应性及其对地面的最小影响推动了重大的研究进展，使其成为复杂和微妙环境的理想解决方案。触觉感知和环境感知是提高机器人性能的关键，因为它们对于保持动态平衡和实现精确控制至关重要。介绍了一种新型的四足机器人腿垫（末端执行器）软接触力传感器。创新的柔软敏感爪子，由柔性导电膜制成，同时测量力和接触点位置，使环境意识决策和支持本体感觉意识。实验测试表明，其柔软的球形设计在各种地形上具有良好的适应性和抓地力。其传感面覆盖球面面积的83.3%，测量误差仅为0.14%。这种能力使敏感的爪子能够探测地面接触以及侧向和上肢的相互作用，为复杂环境中的导航和操作提供了一个强大而通用的工具。为了验证其性能，使用定制的测试台对传感器进行了测试，随后安装在我们实验室开发的Lupoh四足机器人上进行进一步评估。

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引用次数: 0

Coordination control of multi-axle distributed drive vehicle with dynamically-triggered DYC intervention and KKT-based torque optimization distribution 动态触发DYC干预的多轴分布式驱动车辆协调控制及基于kkt的转矩优化分配

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-08-16 DOI: 10.1016/j.mechatronics.2025.103397

Lie Guo , Jiaqing Zhao , Longxin Guan , Jiahao Wang , Pingshu Ge , Linli Xu

Multi-axle distributed drive vehicles, characterized by over-actuation, internal dynamics, and nonlinear external disturbances, frequently encounter coordination challenges in lateral path tracking, yaw stability intervention, and longitudinal speed control. These issues can significantly degrade overall control performance, particularly under complex driving conditions. To address them, this paper proposes a coordinated control framework integrating path tracking, yaw stability intervention, longitudinal drive control, and optimal torque distribution. First, a robust path tracking controller based on a linear parameter-varying (LPV) dynamic model is designed and a longitudinal speed controller using a linear sliding mode approach are designed. Subsequently, a direct yaw-moment control (DYC) strategy based on nonsingular terminal sliding mode control (NTSMC) with nonlinear dynamic triggering is introduced to mitigate performance degradation induced by excessive interventions. Finally, an optimal torque distribution method based on the Karush–Kuhn–Tucker (KKT) conditions is developed to ensure the feasibility of the solutions. The effectiveness and superiority of the proposed coordination framework are validated through hardware-in-the-loop (HiL) experiments.

多轴分布式驱动车辆在横向路径跟踪、偏航稳定性干预和纵向速度控制等方面存在着过度驱动、内部动力学和非线性外部干扰等问题。这些问题会显著降低整体控制性能，特别是在复杂的驾驶条件下。为了解决这些问题，本文提出了一种集成路径跟踪、偏航稳定性干预、纵向驱动控制和最优转矩分配的协调控制框架。首先，设计了基于线性变参动态模型的鲁棒路径跟踪控制器和基于线性滑模方法的纵向速度控制器。随后，提出了一种基于非线性动态触发的非奇异终端滑模控制（NTSMC）的直接偏航力矩控制（DYC）策略，以减轻过度干预引起的性能下降。最后，提出了一种基于Karush-Kuhn-Tucker （KKT）条件的最优转矩分配方法，以保证解的可行性。通过硬件在环（HiL）实验验证了所提协调框架的有效性和优越性。

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引用次数: 0

Co-design of a wave energy converter through bi-conjugate impedance matching 双共轭阻抗匹配波能转换器的协同设计

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-08-16 DOI: 10.1016/j.mechatronics.2025.103395

Ryan G. Coe, Giorgio Bacelli, Daniel Gaebele, Alicia Keow, Dominic Forbush

As with other oscillatory power conversion systems, the design of wave energy converters can be understood as an impedance matching problem. By representing the wave energy converter as a multi-port network, two separate but related impedance matching conditions can be established. Satisfying these conditions maximizes power transfer to the load. In practice, these impedance matching conditions may be used to influence the design of the system (including the hull, power take-off, controller, mooring, etc.). To this end, this paper considers some example applications of wave energy converter design with the help of the impedance matching framework.

与其他振荡功率转换系统一样，波能转换器的设计可以理解为阻抗匹配问题。通过将波能转换器表示为一个多端口网络，可以建立两个独立但相关的阻抗匹配条件。满足这些条件可以最大限度地向负载传输功率。在实际应用中，这些阻抗匹配条件可能会影响系统的设计（包括船体、动力输出、控制器、系泊等）。为此，本文考虑了利用阻抗匹配框架设计波能转换器的一些应用实例。

引用次数: 0

Compensating hysteresis and mechanical misalignment in piezo-stepper actuators 压电步进执行器的补偿滞后和机械错位

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-08-13 DOI: 10.1016/j.mechatronics.2025.103394

Max van Meer , Tim van Meijel , Emile van Halsema , Edwin Verschueren , Gert Witvoet , Tom Oomen

Piezo-stepper actuators enable accurate positioning through the sequential contraction and expansion of piezoelectric elements, generating a walking motion. The aim of this paper is to reduce velocity ripples caused by parasitic effects, due to hysteresis in the piezoelectric material and mechanical misalignments, through suitable feedforward control. The presented approach involves the integration of a rate-dependent hysteresis model with a position-dependent feedforward learning scheme to compensate for these effects. Experimental results show that this approach leads to a significant reduction in the velocity ripples, even when the target velocity is changed. These results enable the use of piezo-stepper actuators in applications requiring high positioning accuracy and stiffness over a long stroke, without requiring expensive position sensors for high-gain feedback.

压电步进驱动器通过压电元件的连续收缩和膨胀来实现精确定位，从而产生行走运动。本文的目的是通过适当的前馈控制来减少由压电材料的滞后和机械错位引起的寄生效应引起的速度波动。所提出的方法包括将速率相关的滞后模型与位置相关的前馈学习方案相结合，以补偿这些影响。实验结果表明，即使目标速度发生变化，该方法也能显著减小速度波动。这些结果使得在需要长行程的高定位精度和刚度的应用中使用压电步进致动器，而不需要昂贵的高增益反馈位置传感器。

引用次数: 0

Learning-driven sensorless interaction force estimation for low-cost robot arm with limited dynamic features 基于学习驱动的低成本机械臂无传感器交互力估计

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-08-11 DOI: 10.1016/j.mechatronics.2025.103396

Jiaoyang Lu , Xianta Jiang , Ting Zou

Precise measurement of the interaction force between the robot and its environment benefits the decision-making processes in various robotic applications. Compared with sensor-based methods, sensorless approaches are commonly preferred due to their versatility and cost-effectiveness. This paper introduces a learning-based method that leverages the state-of-the-art transformer to accurately estimate the interaction force. In contrast to other estimation methods relying on accurate robot dynamic parameters, state information or image features, a notable innovation of our work is the utilization of the limited set of features. The elaborate feature set only includes the joint angle, velocity, and driven torque, with the omission of joint acceleration—a basic robot state typically employed in other research. This configuration expands the feasibility of the presented approach to low-cost robots which are solely equipped with encoders in each joint, and to scenarios where the collection of clear and unobstructed visual features are challenging. Another distinctive feature of our work is that both soft and stiff objects during interaction are considered. Results from the experiment demonstrate that, in comparison to previous image-based methods, our framework achieves an equivalent or even superior level of accuracy across a broader spectrum of environments. Additionally, due to the elimination of joint acceleration from the feature set, the proposed framework sacrifices a small degree of accuracy compared with some non-image-based methods to broaden its applicability.

精确测量机器人与环境之间的相互作用力有助于机器人在各种应用中的决策过程。与基于传感器的方法相比，无传感器方法由于其通用性和成本效益而普遍受到青睐。本文介绍了一种基于学习的方法，利用最先进的变压器来准确估计相互作用力。与其他依赖于精确的机器人动态参数、状态信息或图像特征的估计方法相比，我们的工作的一个显著创新是利用了有限的特征集。精心设计的特征集只包括关节角度、速度和驱动扭矩，而忽略了关节加速度——这是其他研究中通常采用的机器人基本状态。这种结构将所提出的方法的可行性扩展到每个关节仅配备编码器的低成本机器人，以及具有挑战性的清晰和无障碍视觉特征的收集场景。我们工作的另一个显著特点是在相互作用过程中同时考虑了软物体和硬物体。实验结果表明，与以前基于图像的方法相比，我们的框架在更广泛的环境中实现了同等甚至更高的精度水平。此外，由于从特征集中消除了关节加速度，与一些非基于图像的方法相比，所提出的框架牺牲了一定程度的精度，以扩大其适用性。

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引用次数: 0

Multiple model switched repetitive control for tremor suppression 多模型切换重复控制抑制震颤

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-08-05 DOI: 10.1016/j.mechatronics.2025.103392

Tingze Fang, Christopher T. Freeman

Tremor is a condition that impacts millions of people globally, and is characterised by a periodic limb movement that impedes voluntary motion. Recent studies have shown that functional electrical stimulation (FES) can help reduce tremor by artificially stimulating opposing muscles, thereby decreasing the oscillation’s amplitude. Various control methods have been proposed for this purpose, but repetitive control (RC) has shown the most promise with potential to completely suppress the tremor. While several RC approaches have demonstrated suppression rates of up to 90%, they heavily rely on an accurate model of the underlying dynamics, and their effectiveness declines steeply due to factors like muscle fatigue, spasticity, and modelling inaccuracies.

This paper introduces a multiple model switched repetitive control (MMSRC) framework that addresses the limitations of existing RC approaches. It guarantees high performance tremor suppression provided the true dynamics belong to an uncertainty set specified by the designer. This enables it to adapt to time-varying physiological changes, as well as changes in the placement of the FES electrodes. Moreover, once an uncertainty set has been established, it removes the need for subsequent model identification. This is an important step towards home-based tremor suppression where model identification and expert tuning are not possible. Experimental validation is performed with four participants, showing that MMSRC effectively suppresses tremor even in the presence of severe modelling uncertainty and fatigue, unlike conventional RC methods which often become unstable under these conditions.

震颤是一种影响全球数百万人的疾病，其特征是周期性肢体运动，阻碍了自主运动。最近的研究表明，功能性电刺激（FES）可以通过人工刺激对侧肌肉来减少震颤，从而降低振荡的幅度。为此提出了各种控制方法，但重复控制（RC）显示出最有希望完全抑制震颤的潜力。虽然一些RC方法已经证明抑制率高达90%，但它们严重依赖于潜在动力学的准确模型，并且由于肌肉疲劳、痉挛和建模不准确等因素，它们的有效性急剧下降。本文介绍了一个多模型切换重复控制（MMSRC）框架，解决了现有RC方法的局限性。它保证高性能的震颤抑制提供了真正的动态属于一个不确定的设计者指定的集合。这使得它能够适应时变的生理变化，以及FES电极位置的变化。此外，一旦建立了不确定性集，就不需要后续的模型识别。这是迈向基于家庭的震颤抑制的重要一步，其中模型识别和专家调谐是不可能的。实验验证与四名参与者进行，表明MMSRC有效地抑制震颤，即使存在严重的建模不确定性和疲劳，不像传统的RC方法，往往在这些条件下变得不稳定。

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引用次数: 0

Optimization of crutch-free walking for a powered exoskeleton considering human adaptation 考虑人类适应性的动力外骨骼无拐杖行走优化

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-08-05 DOI: 10.1016/j.mechatronics.2025.103389

Jongwon Kim , Abhiraj Singh , Jimin Youn , Hyeongjun Kim , Jeongsu Park , Jinsu Park , Kyoungchul Kong

In crutch-free walking with powered exoskeletons, pilots instinctively engage their upper body to adapt its motion and maintain balance, especially in the absence of lower-limb sensory feedback or external stabilizing aids. These self-balancing efforts, often involving significant head and trunk movement, not only increase physical and cognitive load but also reduce the overall usability of the exoskeleton. This study proposes a human-adaptation-in-the-loop optimization method that minimizes the need for voluntary upper-body adjustments, particularly head movement. This approach aims to enable crutch-free walking by minimizing the pilot’s voluntary balancing, achieved through the iterative optimization of ankle joint trajectory based on the modeling of the pilot’s head movements and the center of pressure (COP). As a result, the proposed human-adaptation-in-the-loop optimization minimized the instability caused by the pilot’s adaptation motion that is not reflected within the human–robot integrated system, enabling continuous walking for people with spinal cord injury (SCI) at a speed of 0.24 m/s without the use of crutches. This demonstrates an effective solution for achieving natural, crutch-free walking in a powered exoskeleton.

在使用动力外骨骼的无拐杖行走中，飞行员本能地使用上半身来适应运动并保持平衡，特别是在没有下肢感官反馈或外部稳定辅助的情况下。这些自我平衡的努力，通常涉及显著的头部和躯干运动，不仅增加了身体和认知负荷，而且降低了外骨骼的整体可用性。本研究提出了一种人类适应循环优化方法，该方法可以最大限度地减少对上半身自愿调整的需要，特别是头部运动。该方法旨在通过基于飞行员头部运动和压力中心（COP）建模的踝关节轨迹迭代优化，最大限度地减少飞行员的自主平衡，从而实现无拐杖行走。因此，本文提出的人类自适应环内优化最小化了飞行员自适应运动在人-机器人集成系统中没有反映出来的不稳定性，使脊髓损伤（SCI）患者能够在不使用拐杖的情况下以0.24 m/s的速度连续行走。这展示了一种有效的解决方案，可以在动力外骨骼中实现自然的、无拐杖的行走。

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引用次数: 0

Research on space proximity pursuit-evasion interception decision-making based on deep reinforcement learning 基于深度强化学习的空间接近追逃拦截决策研究

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-08-05 DOI: 10.1016/j.mechatronics.2025.103387

Cheng Huang, Quanli Zeng, Jiazhong Xu

Aiming at the one-to-one pursuit-evasion problem in space, to successfully intercept the close-range evader with arbitrary counter-maneuver under relative motion between pursuer and evader at a close given range, this paper proposes a decision-making method for close-range pursuit-evasion interception based on Distributed Distributional Deep Determined Policy Gradient (D4PG). An improved nearest neighbor algorithm exploration mechanism including random constant and logarithmic constant is adopted, which reduces the learning burden of the algorithm and improves its convergence stability. A target network containing three value networks is constructed, and the loss function is calculated by selecting a value network with the minimum variance of probability distribution in the three networks, which enables the more accurate estimation of the Q-functions, and the operation speed and efficiency of the algorithm are effectively improved. Four typical escaping scenarios of arbitrary counter-maneuvering are performed as experimental verification to the simulation, and the results show the effectiveness and superiority of the proposed decision-making method for space proximity pursuit-evasion interception.

针对空间中一对一的追逃问题，为了在给定近距离内，在追逃相对运动条件下成功拦截具有任意反机动的近距离躲避机，提出了一种基于分布式深度确定策略梯度（D4PG）的近距离追逃拦截决策方法。采用改进的包含随机常数和对数常数的最近邻算法探索机制，减少了算法的学习负担，提高了算法的收敛稳定性。构造了包含三个值网络的目标网络，通过选择三个网络中概率分布方差最小的值网络来计算损失函数，使得q函数的估计更加准确，有效地提高了算法的运算速度和效率。通过四种典型的任意反机动逃离场景对仿真进行了实验验证，结果表明了所提决策方法在空间近距离追逃拦截中的有效性和优越性。

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引用次数: 0

A novel pose control framework and its implementation for robot manipulators following constrained spatial paths 基于约束空间路径的机器人姿态控制框架及其实现

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-07-31 DOI: 10.1016/j.mechatronics.2025.103390

Yalun Wen, Prabhakar R. Pagilla

This paper develops a novel pose control framework for robot manipulators traversing a given spatial curve with constant speed. The key to this framework is the use of a Rotation Minimizing Frame (RMF) for path generation and control, enhancing motion stability for paths with significant curvature and inflection points, and reducing kinematic twist. Using the governing equations based on the RMF, we first develop the reference velocity and acceleration along the path that is consistent with the RMF. Employing tools from differential geometry, we derive a path following position control law by projecting the robot translation states onto the RMF. From an analytical description of the relative orientation error kinematics, we derive a stabilizing orientation controller by utilizing the Modified Rodrigues Parameters to avoid the unwinding problem. The proposed framework is applicable to both torque-controlled and velocity-controlled robots, and we provide results from real-time experiments on both types of robots to verify the effectiveness and advantages of the proposed approach.

本文提出了一种新的机器人姿态控制框架，用于机器人以等速穿越给定的空间曲线。该框架的关键是使用旋转最小化框架（RMF）进行路径生成和控制，增强具有显著曲率和拐点的路径的运动稳定性，并减少运动学扭曲。利用基于RMF的控制方程，首先推导出与RMF一致的参考速度和参考加速度。利用微分几何工具，通过将机器人的平移状态投影到RMF上，推导出路径跟随位置控制律。从相对姿态误差运动学的分析描述出发，利用修正罗德里格斯参数推导出一种稳定姿态控制器，以避免解卷问题。所提出的框架适用于转矩控制和速度控制的机器人，并提供了两种类型机器人的实时实验结果，以验证所提出方法的有效性和优势。

引用次数: 0