【标题】Intelligent control for accurate fast response and minimum energy of motion for industrial robotic manipulator
【参考中译】智能控制，实现工业机器人机械手的准确快速响应和最小运动能量
【类型】 期刊
【关键词】 robot manipulators;;optimisation;;LQR;;linear quadratic regulator;;DOF;;degrees-of-freedom;;energy consumption;;neural network
【参考中译】 机器人机械手;;优化;;LQR;;线性二次调节器;;自由度;;能源消耗;;神经网络
【作者】 Shaar, Areej; Ghaeb, Jasim A.Shaar, Areej; Ghaeb, Jasim A.
【摘要】 This work proposes an approach to optimise the performance of a six degrees-of-freedom (6-DOF) robotic manipulator. The focus is on achieving a balance between three key objectives: rapid response speed, minimal positioning error, and reduced energy consumption during movement. The methodology employs a two-phase approach. First, a kinematic model is established using the Denavit-Hartenberg convention. Subsequently, a grey wolf optimiser (GWO) identifies optimal joint configurations for diverse target locations within the workspace. These optimal configurations serve as training data for a forward neural network (FNN) model, enabling it to predict optimal joint angles for future tasks. The proposed method demonstrates exceptional capability in precisely positioning the manipulator at desired locations within a short timeframe (0.01 sec average) while maintaining high accuracy (0.0056 mean square error (MSE) average) and achieving significant energy savings (70% average reduction). This approach presents a promising solution for enhancing the overall performance of 6-DOF robotic manipulators.
【参考中译】 这项工作提出了一种优化六自由度（6-DOF）机器人机械手性能的方法。重点是实现三个关键目标之间的平衡：快速响应速度、最小定位误差以及减少运动期间的能源消耗。该方法采用两阶段方法。首先，使用德纳维特-哈滕伯格惯例建立运动学模型。随后，灰狼优化器（GWO 0）识别工作空间内不同目标位置的最佳关节配置。这些最佳配置充当前向神经网络（FNN）模型的训练数据，使其能够预测未来任务的最佳关节角度。所提出的方法表现出了在短时间内（平均0.01秒）将机械手精确定位在所需位置的卓越能力，同时保持高准确性（平均0.0056均方误差（SSE）并实现显着节能（平均减少70%）。这种方法为提高6自由度机器人机械手的整体性能提供了一种有希望的解决方案。
【来源】 International Journal of Automation and Control 2025, vol.19, no.3
【入库时间】 2025/9/1
【标题】Synergistic effects of robot performance on human-robot mutual assistance systems in manufacturing
【参考中译】制造业中机器人性能对人-机器人互助系统的协同效应
【关键词】 Digital twin;;digital human;;human-robot collaboration;;mutual assistance;;manufacturing production system
【参考中译】 数字孪生;;数字人;;人机协作;;互助;;制造生产系统
【作者】 Shirakura, Naoki; Maruyama, Tsubasa; Makihara, Koshi; Ueshiba, Toshio; Itadera, Shunki; Endo, Yuki; Tada, Mitsunori; Domae, YukiyasuShirakura, Naoki; Maruyama, Tsubasa; Makihara, Koshi; Ueshiba, Toshio; Itadera, Shunki; Endo, Yuki; Tada, Mitsunori; Domae, Yukiyasu
【摘要】 In countries experiencing a growing shortage of human resources, collaboration with robots is indispensable for leveraging a diverse workforce and maintaining productivity. This study proposes a manufacturing system that simultaneously enhances overall productivity and reduces human workload through mutual assistance between humans and robots. By considering the characteristics of both humans and robots, task allocation was performed to balance the trade-off between productivity and workload. Furthermore, this study introduces a method for improving robot performance to maximize the benefit of mutual assistance. The proposed approach utilizes a digital twin, including humans, for a real-time evaluation of the human workload. Moreover, a novel grasp strategy for a magnetic gripper was proposed to improve the robot's picking success rate, thereby enhancing the effectiveness of the mutual assistance. Through a practical example of supply tasks in a real factory setting, we showcased the mutual assistance between a human worker and a mobile manipulator, confirming the potential of the proposed system. Our analysis of the human workload indicated that improvements in robot performance significantly increased the effectiveness of mutual assistance. The results demonstrated that productivity can be enhanced by up to 19% while simultaneously reducing the human workload by up to 15%.
【参考中译】 在人力资源日益短缺的国家，与机器人的合作对于利用多元化的劳动力和保持生产力是不可或缺的。这项研究提出了一种制造系统，通过人类和机器人之间的互助，同时提高整体生产力并减少人类工作量。通过考虑人类和机器人的特点，进行任务分配，以平衡生产力和工作量之间的权衡。此外，本研究还介绍了一种提高机器人性能的方法，以最大限度地提高互助的效益。所提出的方法利用包括人类在内的数字双胞胎来实时评估人类工作量。此外，提出了一种新颖的磁性抓手抓取策略，以提高机器人的拾取成功率，从而增强互助的有效性。通过真实工厂环境中供应任务的实际示例，我们展示了人类工人和移动机械手之间的互助，证实了拟议系统的潜力。我们对人类工作量的分析表明，机器人性能的提高显着提高了互助的有效性。结果表明，生产力可以提高高达19%，同时减少高达15%的人力工作量。
【来源】 Advanced Robotics 2025, vol.39, no.4
【标题】An open extended reality platform supporting dynamic robot paths for studying human-robot collaboration in manufacturing
【参考中译】支持动态机器人路径的开放延展实境平台，用于研究制造业中的人机协作
【关键词】 Industrial cobot;;Human robot collaboration;;Extended Reality;;Dynamic collision avoidance;;Safety
【参考中译】 工业协作机器人;;人类机器人协作;;延展实境;;动态碰撞避免;;安全
【作者】 Angelidis, Antonios; Plevritakis, Emmanuel; Vosniakos, George-Christopher; Matsas, EliasAngelidis, Antonios; Plevritakis, Emmanuel; Vosniakos, George-Christopher; Matsas, Elias
【摘要】 Human-robot collaboration (HRC) in manufacturing allows advantageous distribution of tasks, e.g. exploiting robot accuracy and human dexterity, safety being of paramount importance. Safety is mostly linked to avoiding collisions between the human and the robot but the pertinent measures adopted should prolong task duration as little as possible. In order to test such measures in HRC pertinent algorithms need to be applied, which is made possible without jeopardising human safety only in an Extended Reality environment. In order to implement path planning algorithms and human-robot interaction rules freely the environment must be open. In this work, the development of such an environment is presented and demonstrated by example of laying up carbon fibre fabric sheets in a mould. An existing open platform was substantially extended by embedding robot control functionality concerning motion, path and trajectory planning emphasizing static and dynamic obstacle detection, interactive input and manipulation and real-time path planning, whereas trajectory planning focused on ensuring acceptability of joint motion solutions using inverse kinematics. Two different real-time path planning methods are embedded in the environment as representative examples. The first one is the established 'Rapidly exploring Random Tree' (RRT) algorithm followed by path optimization. The second one is 'Machine-Learned Path Planning' (MLPP) a prototype machine learning model trained using linear regression with Gaussian noise based on safe path planning data generated by users. The evaluation criteria of these methods were the number and severity of collisions as well as the total completion time of the manufacturing task. In the particular case examined, the machine learning technique proved much faster than RRT but not as safe, despite its potential. However, the openness of the XR platform enables testing of any other strategy supporting HRC in manufacturing before it is actually transcribed to the real robot controller.
【参考中译】 制造业中的人机协作（HRC）允许任务的有利分配，例如利用机器人的准确性和人类的灵活性，安全性至关重要。安全性主要与避免人类和机器人之间的碰撞有关，但采取的相关措施应尽可能少地延长任务持续时间。为了在HRC中测试此类措施，需要应用相关算法，只有在延展实境环境中才能在不危及人类安全的情况下实现这一目标。为了自由地实施路径规划算法和人机交互规则，环境必须是开放的。在这项工作中，这种环境的发展，并展示了铺设碳纤维织物片在模具中的例子。现有的开放式平台通过嵌入机器人控制功能进行了实质性扩展，这些功能涉及运动、路径和轨迹规划，强调静态和动态障碍物检测、交互式输入和操纵以及实时路径规划，而轨迹规划则侧重于确保使用逆运动学的关节运动解决方案的可接受性。两种不同的实时路径规划方法嵌入在环境中作为代表性的例子。第一个是建立“快速探索随机树”（RRT）算法，然后进行路径优化。第二个是“机器学习路径规划”（MLPP），这是一种基于用户生成的安全路径规划数据，使用带有高斯噪声的线性回归训练的原型机器学习模型。这些方法的评价标准是碰撞的数量和严重程度以及制造任务的总完成时间。在研究的特定案例中，机器学习技术被证明比RRT快得多，但尽管有潜力，却不那么安全。然而，XR平台的开放性使得能够在实际转录到真正的机器人控制器之前测试制造中支持HRC的任何其他策略。
【来源】 The International Journal of Advanced Manufacturing Technology 2025, vol.138, no.1