Robotics and Autonomous Systems最新文献_第2页

A critical review and systematic design approach for innovative upper-limb rehabilitation devices 对创新型上肢康复设备的严格审查和系统设计方法

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2024-10-21 DOI: 10.1016/j.robot.2024.104835

Michele Perrelli, Francesco Lago, Salvatore Garofalo, Luigi Bruno, Domenico Mundo, Giuseppe Carbone

This paper conducts a thorough literature review and assessment of prevailing upper-limb rehabilitation devices, scrutinizing their strengths and limitations. The focus of this work is mainly on soft exosuit devices but some rigid and hybrid exoskeleton devices are also discussed as a comparative mean. Subsequently, this manuscript delineates explicit design guidelines with the intent of fostering a systematic approach toward innovation in the realm of upper-limb rehabilitation technology. Through an examination of current concepts and technological paradigms, this study seeks to contribute nuanced insights aimed at optimizing both efficacy and user experience in rehabilitation device design. The culmination of this critical analysis results in the proposal of a systematic design procedure to inform and influence the trajectory of specific user-tailored innovations within the domain of upper-limb rehabilitation devices.The proposed approach enables the identification of features and weaknesses in existing devices, facilitating also the design of innovative solutions for unsolved issues in the field of wearable robotics. A design example is presented to clarify the proposed design procedure.

本文对现有的上肢康复设备进行了全面的文献综述和评估，仔细研究了这些设备的优势和局限性。这项工作的重点主要是软质外骨骼装置，但也讨论了一些硬质和混合外骨骼装置，作为一种比较手段。随后，本手稿制定了明确的设计指南，旨在促进上肢康复技术领域的系统性创新。通过对当前概念和技术范式的研究，本研究力图提出细致入微的见解，旨在优化康复设备设计的功效和用户体验。这一批判性分析的最终结果是提出了一个系统化的设计程序，以指导和影响上肢康复设备领域中特定用户定制创新的发展轨迹。所提出的方法能够识别现有设备的特点和弱点，也有助于为可穿戴机器人领域中尚未解决的问题设计创新解决方案。本文通过一个设计实例来阐明所提出的设计程序。

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引用次数: 0

Differential kinematics of a single-point-suspended manipulator with center-of-mass shift compensation 具有质量中心偏移补偿功能的单点悬挂式机械手的差分运动学原理

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2024-10-19 DOI: 10.1016/j.robot.2024.104820

Zhifeng Huang , Runqiao Zhou , Ruiyuan Huang , Jun Ota

The single cable-suspended manipulator is suitable for special occasions such as aerial operation tasks of unmanned aerial vehicles (UAVs) and deep-well search and rescue. However, due to the lack of complete constraints at the base, the manipulator will have errors in end position due to the center-of-mass offset during the motion. In this paper, the model of CoM shifting is established, and the Jacobian matrix is improved based on this model, to realize the differential kinematics solution for the single cable-suspended manipulator. In addition, by introducing the constraint of CoM shift in the Jacobian matrix, it makes it possible to synchronize the planning of the motion of the end and the center of mass. This can effectively avoid the wobbling of the manipulator in the presence of elasticity or instability at the suspension point. Both simulation and prototype experiments effectively verify the effectiveness of the proposed method. Using the method of this paper, the average error of the trajectories in the z-axis and x-axis can be reduced from 27.0 ± 2.6 mm to 5.6 ± 3.4 mm, and 43.0 ± 64.2 mm to 3.3 ± 4.8 mm, respectively.

单缆悬挂式机械手适用于无人驾驶飞行器（UAV）的空中作业任务和深井搜救等特殊场合。然而，由于底座缺乏完整约束，机械手在运动过程中会因质量中心偏移而产生末端位置误差。本文首先建立了重心偏移模型，并在此基础上改进了雅各布矩阵，实现了单缆悬挂机械手的微分运动学求解。此外，通过在雅各布矩阵中引入 CoM 移位的约束条件，可以实现末端和质心运动的同步规划。这可以有效避免机械手在悬挂点存在弹性或不稳定的情况下发生摆动。模拟和原型实验都有效地验证了所提方法的有效性。利用本文的方法，Z 轴和 X 轴的平均轨迹误差可分别从 27.0 ± 2.6 mm 减小到 5.6 ± 3.4 mm 和 43.0 ± 64.2 mm 减小到 3.3 ± 4.8 mm。

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引用次数: 0

Bidirectional artificial potential field-based ant colony optimization for robot path planning 基于人工势场的双向蚁群优化机器人路径规划

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2024-10-19 DOI: 10.1016/j.robot.2024.104834

Bo Fu , Yuming Chen , Yi Quan , Xilin Zhou , Chaoshun Li

Ant colony optimization (ACO) is a common approach for addressing mobile robot path planning problems. However, it still encounters some challenges including slow convergence speed, susceptibility to local optima, and a tendency to falling into traps. We propose a bidirectional artificial potential field-based ant colony optimization (BAPFACO) algorithm to solve these issues. First, the bidirectional artificial potential field is introduced to initialize the grid environment model and restrict direction selection to jump out of the trap. Second, an adaptive heuristic function is presented to strengthen directionality of the algorithm and reduce the turning times. Third, a pseudo-random state transition rule based on potential difference between starting and ending nodes is developed to accelerate convergence speed. Finally, an improved pheromone update strategy incorporating pheromone diffusion mechanism and elite ants update strategy is proposed to help getting out of local optima. To demonstrate the advantages of BAPFACO, the validation of the performance in six different complexity environments and comparative experiments with other conventional search algorithms and ACO variants are conducted. The results of experiment show that compared to various ACO variants, BAPFACO have advantages in terms of reducing the turning times, shortening path length, improving convergence speed and avoiding ant loss. In complex environments, compared to IHMACO, the average path length enhancement percentage (PLE) of BAPFACO is 20.98%, the average iterations enhancement percentage (IE) of BAPFACO is 20.00% and the average turning times enhancement percentage (TE) of BAPFACO is 49.43%. These results firmly demonstrate the efficiency and practicality of the BAPFACO algorithm for mobile robot in path planning.

蚁群优化（ACO）是解决移动机器人路径规划问题的常用方法。然而，它仍然面临一些挑战，包括收敛速度慢、容易出现局部最优以及容易陷入陷阱。我们提出了一种基于双向人工势场的蚁群优化算法（BAPFACO）来解决这些问题。首先，引入双向人工势场来初始化网格环境模型，并限制方向选择以跳出陷阱。其次，提出了一种自适应启发式函数，以加强算法的方向性并减少转弯时间。第三，基于起点和终点节点之间的电位差开发了一种伪随机状态转换规则，以加快收敛速度。最后，提出了一种改进的信息素更新策略，该策略结合了信息素扩散机制和精英蚂蚁更新策略，有助于摆脱局部最优状态。为了证明 BAPFACO 的优势，我们在六种不同的复杂性环境中进行了性能验证，并与其他传统搜索算法和 ACO 变体进行了对比实验。实验结果表明，与各种 ACO 变体相比，BAPFACO 在减少转弯时间、缩短路径长度、提高收敛速度和避免蚂蚁损失方面具有优势。在复杂环境下，与 IHMACO 相比，BAPFACO 的平均路径长度提高率（PLE）为 20.98%，平均迭代次数提高率（IE）为 20.00%，平均转弯次数提高率（TE）为 49.43%。这些结果充分证明了 BAPFACO 算法在移动机器人路径规划中的高效性和实用性。

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引用次数: 0

Inspection robot GPS outages localization based on error Kalman filter and deep learning 基于误差卡尔曼滤波和深度学习的巡检机器人 GPS 故障定位

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2024-10-18 DOI: 10.1016/j.robot.2024.104824

Yansheng Li, Haoyang Yu, Lingli Xiao, Yiyang Yuan

In urban environments, inspection robots face complex terrain and variable motion states, posing high demands on their positioning systems. Although the integration of Micro-Electro-Mechanical Systems Inertial Navigation Systems (MEMS-INS) with the Global Positioning System (GPS) provides continuous positioning information, high buildings and tunnels in cities can block GPS signals, leading to signal interruptions and increased positioning errors. During GPS outages, MEMS-INS gradually accumulates errors, severely affecting positioning accuracy. To address this issue, this paper proposes an adaptive error state Kalman Filter (AESKF), which employs an adaptive mechanism to eliminate the noise impact of MEMS-INS and reduce reliance on the process model. Additionally, a deep learning framework based on the Self-Attention mechanism of the Transformer and a custom loss function Long Short-Term Memory (LSTM) module is proposed to predict position increments of the inspection robot. Combining AESKF with Transformer-LSTM achieves optimized positioning accuracy of the inspection robot during GPS outages in dynamic urban environments. Simulation and practical experimental results demonstrate that the combination of AESKF and Transformer-LSTM significantly improves positioning accuracy. Compared to other mature methods, the Root Mean Square Error (RMSE) of positioning is reduced by up to 83.64 % in the north direction and 89.56 % in the east direction. When the GPS signal interruption lasts for 10 s and 60 s, the maximum position error standard deviation (STD) is 0.1186 m and 1.0417 m, respectively.

在城市环境中，巡检机器人要面对复杂的地形和多变的运动状态，这对其定位系统提出了很高的要求。虽然微机电系统惯性导航系统（MEMS-INS）与全球定位系统（GPS）的集成可提供连续的定位信息，但城市中的高楼和隧道会阻挡 GPS 信号，导致信号中断，增加定位误差。在 GPS 中断期间，MEMS-INS 会逐渐积累误差，严重影响定位精度。为解决这一问题，本文提出了自适应误差状态卡尔曼滤波器（AESKF），它采用自适应机制来消除 MEMS-INS 的噪声影响，减少对过程模型的依赖。此外，本文还提出了一个基于变压器自保持机制和自定义损失函数长短期记忆（LSTM）模块的深度学习框架，用于预测检测机器人的位置增量。将 AESKF 与 Transformer-LSTM 相结合，在动态城市环境中 GPS 中断期间实现了检测机器人定位精度的优化。仿真和实际实验结果表明，AESKF 与 Transformer-LSTM 的结合显著提高了定位精度。与其他成熟方法相比，定位的均方根误差（RMSE）在北向降低了 83.64%，在东向降低了 89.56%。当 GPS 信号中断 10 秒和 60 秒时，最大定位误差标准偏差（STD）分别为 0.1186 米和 1.0417 米。

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引用次数: 0

Design of exoskeleton brain-like intelligent trajectory prediction model based on echo state network 基于回波状态网络的外骨骼类脑智能轨迹预测模型设计

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2024-10-17 DOI: 10.1016/j.robot.2024.104836

Wendong Wang , Chenyang Wang , Xiaoqing Yuan , Songyun Xie , Jinming Liu

Traditional rehabilitation training methods face significant challenges, such as low repeatability and a shortage of skilled physicians. Exoskeleton robots have been recognized by rehabilitation experts as valuable tools in addressing these issues. However, current auxiliary training devices suffer from limited human-computer interaction capabilities, single-mode training, and basic passive functionalities. To enhance the effectiveness of rehabilitation training, particularly in predicting human movement trajectories, this study presents a brain-like intelligent trajectory prediction model. This model, inspired by bionics, follows the physiological structure and control mechanisms of the human brain to improve human-robot cooperative control in rehabilitation exoskeletons. Utilizing an Echo State Network (ESN), the model establishes a computational framework that mirrors the motor neuron activity of the cerebellum, brainstem, and spinal cord. In conjunction with the Spiking Cerebellar Model Network (SCMN), a brain-like trajectory prediction model was developed that incorporates pulsatile neurons, simulating the transmission and synaptic processes observed in biological neural networks. This approach enhances computational efficiency and physiological interpretability, addressing the limitations of existing neural network models. Experimental results demonstrate that the proposed brain-like control model effectively predicts the movement trajectories of upper limb rehabilitation exoskeletons, offering a novel theoretical and practical framework for bionic control in rehabilitation robotics.

传统的康复训练方法面临着巨大的挑战，例如可重复性低和技术熟练的医生短缺。外骨骼机器人已被康复专家视为解决这些问题的重要工具。然而，目前的辅助训练设备存在人机交互能力有限、训练模式单一和基本无源功能等问题。为了提高康复训练的效果，尤其是在预测人体运动轨迹方面，本研究提出了一种类脑智能轨迹预测模型。该模型受仿生学启发，遵循人脑的生理结构和控制机制，以改善康复外骨骼中的人机协同控制。该模型利用回声状态网络（ESN），建立了一个反映小脑、脑干和脊髓运动神经元活动的计算框架。结合尖峰小脑模型网络 (SCMN)，我们开发了一个类脑轨迹预测模型，其中包含脉冲神经元，模拟生物神经网络中的传输和突触过程。这种方法提高了计算效率和生理学可解释性，解决了现有神经网络模型的局限性。实验结果表明，所提出的类脑控制模型能有效预测上肢康复外骨骼的运动轨迹，为康复机器人的仿生控制提供了新的理论和实践框架。

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引用次数: 0

Vibration suppression of redundantly controlled cable-driven parallel robots 冗余控制电缆驱动并联机器人的振动抑制

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2024-10-17 DOI: 10.1016/j.robot.2024.104838

Xiaotong Zhao, Jingli Du, KunPeng Zhao

Cable-driven parallel robots (CDPRs) use flexible cables to connect the end-effector to a fixed base, which is prioritized for large workspace and fast operation speed, but the presence of flexible cables creates a challenge for high-precision control of CDPRs. The mass and elasticity of the cables need to be considered to model the CDPRs in a large workspace more accurately. In this paper, the dynamics of the CDPRs are modeled using the finite element method. In order to more accurately predict the simulation results of the discrete-time model at the actual control frequency, the hierarchical model predictive control (H-MPC) algorithm is proposed with an internal mapping module for mapping control signals and an external prediction module for predictive control. In the control process, we designed a physics-informed neural network (PINN) to predict the state of end-cable elements. Under the same hardware conditions, the H-MPC algorithm effectively reduces the vibration of the end-effector during operation compared to the model predictive control (MPC) algorithm. Our proposed algorithm is validated under various trajectories, and the results show that the H-MPC algorithm can mitigate the vibration condition of the end-effector. We provide new solutions and ideas for the research in high precision control and vibration control of CDPRs. Our H-MPC algorithms are also easier to deploy in industrial controls.

缆索驱动并联机器人（CDPRs）使用柔性缆索将末端执行器连接到固定基座，优先考虑工作空间大和运行速度快的特点，但柔性缆索的存在给 CDPRs 的高精度控制带来了挑战。为了更精确地模拟大工作空间中的 CDPR，需要考虑电缆的质量和弹性。本文使用有限元法对 CDPR 的动态进行建模。为了更准确地预测离散时间模型在实际控制频率下的仿真结果，我们提出了分层模型预测控制（H-MPC）算法，该算法具有用于映射控制信号的内部映射模块和用于预测控制的外部预测模块。在控制过程中，我们设计了一个物理信息神经网络（PINN）来预测端缆元件的状态。在相同的硬件条件下，与模型预测控制（MPC）算法相比，H-MPC 算法能有效减少末端执行器在运行过程中的振动。我们提出的算法在各种轨迹下进行了验证，结果表明 H-MPC 算法可以减轻末端执行器的振动状况。我们为 CDPR 的高精度控制和振动控制研究提供了新的解决方案和思路。我们的 H-MPC 算法也更容易部署到工业控制中。

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引用次数: 0

Management of a fleet of autonomous underwater gliders for area coverage: From simulation to real-life experimentation 管理用于区域覆盖的自主水下滑翔机舰队：从模拟到实际实验

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2024-10-15 DOI: 10.1016/j.robot.2024.104825

Aurélien Merci , Cédric Anthierens , Nadège Thirion-Moreau , Yann Le Page

This article deals with underwater gliders whether there are operated in a fleet or individually. They constitute the most affordable and energy-saving autonomous observation/data acquisition platform, making long-duration ocean exploration missions possible. In this article, theoretical researches are led to solve the path planning problem of multi-point exploration missions of this type of vehicle. We focus on the area coverage type missions i.e. all points of a given area must be visited only once. We suggest a new path planning method for area coverage i.e. the fleet of glider is sized and the optimized glider trajectories are calculated according to selected criteria (mission duration, energy consumption or traveled distance). Our proposed approach combines weighted graph theory with our underwater glider simulator whose main interest is to be capable of integrating time-varying 3D environmental data (4D). Our method is tested in simulation and then in a dynamic real-life context (Mediterranean Sea) on Alseamar’s SeaExplorer autonomous underwater gliders. Finally, a comparison with the expertise of a glider pilot and a more conventional approach, exploiting only the distance between the waypoints in the operation area, confirms the relevance and effectiveness of the suggested method. The experimental mission demonstrates the interest and benefits of the approach and the ease of operational implementation in an industrial context.

本文论述的是水下滑翔机，无论是以船队形式运行还是单独运行。它们是最经济、最节能的自主观测/数据采集平台，使长时间海洋探测任务成为可能。本文通过理论研究来解决这类飞行器多点探测任务的路径规划问题。我们将重点放在区域覆盖型任务上，即必须只访问一次给定区域的所有点。我们提出了一种新的区域覆盖路径规划方法，即根据选定的标准（任务持续时间、能耗或飞行距离）确定滑翔机队的规模并计算优化的滑翔机轨迹。我们提出的方法将加权图理论与我们的水下滑翔机模拟器相结合，该模拟器的主要功能是整合时变三维环境数据（4D）。我们的方法首先在模拟环境中进行了测试，然后在 Alseamar 公司的 SeaExplorer 自主水下滑翔机的动态真实环境（地中海）中进行了测试。最后，通过与滑翔机驾驶员的专业知识和仅利用作业区航点间距离的传统方法进行比较，证实了所建议方法的实用性和有效性。实验任务证明了该方法的意义和优点，以及在工业环境中操作实施的便利性。

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引用次数: 0

Social robot navigation through constrained optimization: A comprehensive study of uncertainty-based objectives and constraints in the simulated and real world 通过约束优化实现社交机器人导航：对模拟和真实世界中基于不确定性的目标和约束条件的综合研究

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2024-10-11 DOI: 10.1016/j.robot.2024.104830

Timur Akhtyamov , Aleksandr Kashirin , Aleksey Postnikov , Ivan Sosin , Gonzalo Ferrer

This paper provides an empirical evaluation, in both simulation and real scenarios, of the social navigation problem when considering human motion prediction and its stochastic effects. To this end, we study several different optimization criteria and constraints related to the uncertainty of predicting pedestrians’ motion, embedded into the Model Predictive Control (MPC) scheme.

The main research question of this work is the following: what are the most important uncertainty-based criteria for the social MPC both in simulated and real-world environments? In order to achieve a solid answer to this question, we extend the results previously obtained from our work (Akhtyamov et al., 2023) in the simulated environments and provide a real-world setting that mimics similar conditions, for a fair comparison of the qualitative and quantitative results.

The main conclusions supported by both of the evaluation environments are the advantages of using adaptive constraints as a clear undisputed enhancement and the problems raised when considering uncertainty-aware criteria. We hope this paper is of interest to the community for deciding and designing uncertainty-aware approaches for social robot navigation.

本文通过模拟和真实场景，对考虑到人类运动预测及其随机影响的社会导航问题进行了实证评估。为此，我们研究了与行人运动预测不确定性相关的几种不同的优化标准和约束条件，并将其嵌入到模型预测控制（MPC）方案中。这项工作的主要研究问题如下：在模拟和真实环境中，社会 MPC 最重要的基于不确定性的标准是什么？为了获得这一问题的可靠答案，我们扩展了之前在模拟环境中获得的结果（Akhtyamov 等人，2023 年），并提供了模拟类似条件的真实世界环境，以便对定性和定量结果进行公平比较。两个评估环境所支持的主要结论是，使用自适应约束作为明确的无可争议的增强功能具有优势，而在考虑不确定性感知标准时则会出现问题。我们希望这篇论文对社会各界决定和设计社会机器人导航的不确定性感知方法有所帮助。

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引用次数: 0

Rebar-tying Robot based on machine vision and coverage path planning 基于机器视觉和覆盖路径规划的钢筋绑扎机器人

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2024-10-11 DOI: 10.1016/j.robot.2024.104826

Xinyan Tan , Lingxuan Xiong , Weimin Zhang , Zhengqing Zuo , Xiaohai He , Yi Xu , Fangxing Li

Automation technology can replace manual work in the traditional construction industry, improve quality and efficiency, and reduce costs. This paper developed a rebar-tying robot for tying the intersection of rebars. It proposed a Hough transform multi-segment fitting method to detect the intersections of rebars in real-time acquired RGB-D images. To cover the surface of the rebar net as much as possible under the condition of limited camera FOV, this paper designed a coverage path planning method to plan the path of the photo positions and the detected intersections of rebars efficiently and orderly. The experimental results show that the robot achieved an accuracy rate of 99.4 % in intersection detection, the detection error is within 2.8 mm, the single tying time is 1.85 s, and the average tying time is 5.5 s, which is faster than most robots. The robot realizes the task of automatically tying the intersection of rebars, which is robust and efficient, without duplication or omission.

自动化技术可以取代传统建筑行业的人工作业，提高质量和效率，降低成本。本文开发了一种钢筋绑扎机器人，用于绑扎钢筋的交叉点。它提出了一种 Hough 变换多段拟合方法，用于检测实时获取的 RGB-D 图像中的钢筋交叉点。为了在有限的相机 FOV 条件下尽可能多地覆盖钢筋网表面，本文设计了一种覆盖路径规划方法，以高效、有序地规划照片位置和检测到的钢筋交叉点的路径。实验结果表明，机器人的交叉点检测准确率达到 99.4%，检测误差在 2.8 mm 以内，单次绑扎时间为 1.85 s，平均绑扎时间为 5.5 s，比大多数机器人都要快。该机器人实现了钢筋交叉点的自动绑扎任务，稳健高效，无重复或遗漏。

引用次数: 0

Human leading or following preferences: Effects on human perception of the robot and the human–robot collaboration 人类的领导或跟随偏好：对人类感知机器人和人机协作的影响

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2024-10-10 DOI: 10.1016/j.robot.2024.104821

Ali Noormohammadi-Asl, Kevin Fan, Stephen L. Smith, Kerstin Dautenhahn

Achieving effective and seamless human–robot collaboration requires two key outcomes: enhanced team performance and fostering a positive human perception of both the robot and the collaboration. This paper investigates the capability of the proposed task planning framework to realize these objectives by integrating human leading/following preferences and performance into its task allocation and scheduling processes. We designed a collaborative scenario wherein the robot autonomously collaborates with participants. The outcomes of the user study indicate that the proactive task planning framework successfully attains the aforementioned goals. We also explore the impact of participants’ leadership and followership styles on their collaboration. The results reveal intriguing relationships between these factors which warrant further investigation in future studies.

实现有效、无缝的人机协作需要两个关键结果：提高团队绩效和促进人类对机器人和协作的积极认知。本文通过将人类的领导/跟随偏好和表现整合到任务分配和调度流程中，研究了拟议任务规划框架实现这些目标的能力。我们设计了一个协作场景，其中机器人自主与参与者协作。用户研究结果表明，主动任务规划框架成功实现了上述目标。我们还探讨了参与者的领导风格和追随风格对协作的影响。研究结果揭示了这些因素之间有趣的关系，值得在今后的研究中进一步探讨。

引用次数: 0