Biomimetic Intelligence and Robotics最新文献_第6页

Novel 3D instrument navigation in intracranial vascular surgery with multi-source image fusion and self-calibration 基于多源图像融合和自校准的颅内血管手术三维仪器导航

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-04-22 DOI: 10.1016/j.birob.2025.100233

Linsen Zhang , Shiqi Liu , Xiaoliang Xie , Xiaohu Zhou , Zengguang Hou , Xinkai Qu , Wenzheng Han , Meng Song , Xiyao Ma , Haining Zhao

In cerebrovascular interventional surgery, spatial position prediction navigation (SPPN) provides 3D spatial information of the vascular lumen, reducing the spatial dimension loss from digital subtraction angiography (DSA) and improving surgical precision. However, it is limited in its adaptability to complex vascular environments and prone to error accumulation. To address these issues, we propose spatial position prediction-based multimodal navigation (SPPMN), integrating minimal intraoperative X-ray images to enhance SPPN accuracy. In the first phase, a feature-weighted dynamic time warping (FDTW)-based branch matching algorithm is introduced for 3D topological positioning under non-registered conditions, with a dynamic location repositioning module for real-time corrections. In the second phase, an occlusion correction module, based on the elastic potential energy of the instrument tip, dynamically adjusts the tip’s angle to achieve low-projection occlusion control. Experimental validation using a high-precision electromagnetic tracking system (EMTS) on a 3D vascular model shows that the proposed method achieves an average 3D positioning accuracy of 9.36 mm in intracranial vascular regions, with a 78% reduction in radiation exposure, significantly enhancing both precision and safety in interventional surgeries.

在脑血管介入手术中，空间位置预测导航（SPPN）提供血管腔的三维空间信息，减少数字减影血管造影（DSA）带来的空间维度损失，提高手术精度。但其对复杂血管环境的适应能力有限，容易产生误差积累。为了解决这些问题，我们提出了基于空间位置预测的多模式导航（SPPMN），结合最小的术中x线图像来提高SPPN的准确性。在第一阶段，引入了一种基于特征加权动态时间翘曲（FDTW）的分支匹配算法，用于非配准条件下的三维拓扑定位，并采用动态位置重定位模块进行实时校正。第二阶段，遮挡校正模块基于仪器尖端的弹性势能，动态调整仪器尖端的角度，实现低投影遮挡控制。高精度电磁跟踪系统（EMTS）在三维血管模型上的实验验证表明，该方法在颅内血管区域的平均三维定位精度为9.36 mm，辐射暴露减少78%，显著提高了介入手术的精度和安全性。

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引用次数: 0

Soft objects grasping evaluation using a novel VCFN-YOLOv8 framework 基于VCFN-YOLOv8框架的软目标抓取评估

Biomimetic Intelligence and Robotics

Pub Date : 2025-04-11 DOI: 10.1016/j.birob.2025.100232

Guoshun Cui , Shiwei Su , Hanyu Gao , Kai Zhuo , Kun Yang , Hang Wu

Humans can quickly perform adaptive grasping of soft objects by using visual perception and judgment of the grasping angle, which helps prevent the objects from sliding or deforming excessively. However, this easy task remains a challenge for robots. The grasping states of soft objects can be categorized into four types: sliding, appropriate, excessive and extreme. Effective recognition of different states is crucial for achieving adaptive grasping of soft objects. To address this problem, a novel visual-curvature fusion network based on YOLOv8 (VCFN-YOLOv8) is proposed to evaluate the grasping state of various soft objects. In this framework, the robotic arm equipped with the wrist camera and the curvature sensor is established to perform generalization grasping and lifting experiments on 11 different objects. Meanwhile, the dataset is built for training and testing the proposed method. The results show a classification accuracy of 99.51% on four different grasping states. A series of grasping evaluation experiments is conducted based on the proposed framework, along with tests for the model’s generality. The experiment results demonstrate that VCFN-YOLOv8 is accurate and efficient in evaluating the grasping state of soft objects and shows a certain degree of generalization for non-soft objects. It can be widely applied in fields such as automatic control, adaptive grasping and surgical robot.

人类通过视觉感知和对抓取角度的判断，可以快速地对柔软物体进行自适应抓取，从而防止物体过度滑动或变形。然而，这项简单的任务对机器人来说仍然是一个挑战。软性物体的抓取状态可分为滑动、适度、过度和极端四种。对不同状态的有效识别是实现软物体自适应抓取的关键。针对这一问题，提出了一种基于YOLOv8的视觉曲率融合网络（VCFN-YOLOv8）来评估各种软物体的抓取状态。在该框架下，建立了配备腕部相机和曲率传感器的机械臂，对11个不同的物体进行泛化抓取和提升实验。同时，建立数据集用于训练和测试所提出的方法。结果表明，在四种不同抓取状态下，分类准确率达到99.51%。基于所提出的框架进行了一系列抓取评价实验，并对模型的通用性进行了测试。实验结果表明，VCFN-YOLOv8对软性物体的抓取状态评价准确、高效，对非软性物体具有一定的泛化能力。可广泛应用于自动控制、自适应抓取、手术机器人等领域。

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引用次数: 0

Hierarchical reinforcement learning for enhancing stability and adaptability of hexapod robots in complex terrains 层次强化学习提高六足机器人在复杂地形中的稳定性和适应性

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-03-27 DOI: 10.1016/j.birob.2025.100231

Shichang Huang , Zhihan Xiao , Minhua Zheng , Wen Shi

In the field of hexapod robot control, the application of central pattern generators (CPG) and deep reinforcement learning (DRL) is becoming increasingly common. Compared to traditional control methods that rely on dynamic models, both the CPG and the end-to-end DRL approaches significantly simplify the complexity of designing control models. However, relying solely on DRL for control also has its drawbacks, such as slow convergence speed and low exploration efficiency. Moreover, although the CPG can produce rhythmic gaits, its control strategy is relatively singular, limiting the robot’s ability to adapt to complex terrains. To overcome these limitations, this study proposes a three-layer DRL control architecture. The high-level reinforcement learning controller is responsible for learning the parameters of the middle-level CPG and the low-level mapping functions, while the middle and low level controllers coordinate the joint movements within and between legs. By integrating the learning capabilities of DRL with the gait generation characteristics of CPG, this method significantly enhances the stability and adaptability of hexapod robots in complex terrains. Experimental results show that, compared to pure DRL approaches, this method significantly improves learning efficiency and control performance, when dealing with complex terrains, it considerably enhances the robot’s stability and adaptability compared to pure CPG control.

在六足机器人控制领域，中心模式发生器（CPG）和深度强化学习（DRL）的应用越来越普遍。与依赖于动态模型的传统控制方法相比，CPG和端到端DRL方法都大大简化了控制模型设计的复杂性。但是，单纯依靠DRL进行控制也存在收敛速度慢、勘探效率低等缺点。此外，尽管CPG可以产生有节奏的步态，但其控制策略相对单一，限制了机器人适应复杂地形的能力。为了克服这些限制，本研究提出了一个三层DRL控制体系结构。高级强化学习控制器负责学习中级CPG的参数和低级映射函数，中低级控制器协调腿内和腿间的关节运动。该方法将DRL的学习能力与CPG的步态生成特性相结合，显著提高了六足机器人在复杂地形中的稳定性和适应性。实验结果表明，与纯DRL方法相比，该方法显著提高了学习效率和控制性能，在处理复杂地形时，与纯CPG控制相比，该方法显著增强了机器人的稳定性和自适应能力。

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引用次数: 0

Editorial for the special issue on Advanced Technology in Autonomous Robots and Swarm Intelligence “自主机器人和群体智能中的先进技术”特刊社论

Biomimetic Intelligence and Robotics

Pub Date : 2025-03-18 DOI: 10.1016/j.birob.2025.100230

Weinan Chen, Tao Zhang, Jiyu Cheng, Yangming Lee, Yisheng Guan

引用次数: 0

A novel magnetoelastic torque sensor with planar spiral coil probes for humanoid robot joints 一种基于平面螺旋线圈探头的新型人形机器人关节磁弹性扭矩传感器

Biomimetic Intelligence and Robotics

Pub Date : 2025-03-17 DOI: 10.1016/j.birob.2025.100229

Zijian Zhang , Zitao Wang , Ming Shao , Yangyang Dong , Fenglei Ni

Humanoid robot joints require real-time torque detection to provide accurate force feedback information for the control system. To meet the measurement requirements and realize the miniaturization of the sensor, a torque sensor based on the magnetoelastic effect is developed, utilizing planar spiral coils as detection probes. In this work, a planar spiral coil mutual inductance calculation model is established to solve the mutual inductance coefficient, and the mechanical structure and circuit design of the sensor are completed. Finally, a torque loading platform is built to perform calibration experiments, and the hysteresis model is improved to compensate for the hysteresis phenomenon. The calibration results indicate that the sensor shows excellent loaded nonlinearity of 3.08%F.S., unloaded nonlinearity of 2.71%F.S., loaded repeatability of 2.48%F.S., unloaded repeatability of 1.89%F.S. and hysteresis of 1.9%F.S., at a compact probe size of 13.8

\times 9.9 \times

1.8 mm.

人形机器人关节需要实时扭矩检测，为控制系统提供准确的力反馈信息。为满足测量要求，实现传感器的小型化，研制了一种基于磁弹性效应的扭矩传感器，采用平面螺旋线圈作为检测探头。本文建立了平面螺旋线圈互感计算模型，求解了互感系数，完成了传感器的机械结构和电路设计。最后，搭建了扭矩加载平台进行标定实验，并对滞回模型进行了改进以补偿滞回现象。标定结果表明，该传感器具有良好的负载非线性（3.08%F.S）。，卸载非线性为2.71%F.S。加载重复性为2.48%F.S。，卸载重复性为1.89%F.S。迟滞率为1.9%F.S。，探头尺寸紧凑，为13.8×9.9×1.8 mm。

引用次数: 0

View planning for visual detection coverage tasks of large airplane upper surface using UAVs 基于无人机的大型飞机上表面目视探测覆盖任务视角规划

Biomimetic Intelligence and Robotics

Pub Date : 2025-02-26 DOI: 10.1016/j.birob.2025.100228

Zhun Huang

In order to enhance the efficiency of visual inspection and effectively carry out 3D visual coverage tasks, this paper focuses on the 3D view planning problem concerning the visual coverage of an airplane’s surface using unmanned aerial vehicles (UAV). Our objective is to attain a sufficiently high coverage rate with the least number of viewpoints. The contributions of this work are enumerated as follows. Firstly, the 3D model of the target aircraft is spatially extended in accordance with the depth range of the camera mounted on the drone, thereby confining the sampling range of 3D viewpoints. Next, a candidate set of viewpoints is generated through random sampling and the probabilistic potential field technique. Subsequently, we propose a novel hyper-heuristic algorithm. In this algorithm, a genetic algorithm serves as a high-level heuristic strategy, in tandem with multiple low-level heuristic operators devised for combinatorial optimization. This not only augments the capacity to seek the global optimal solution but also expedites the convergence rate, aiming to ascertain the optimal subset of viewpoints. Moreover, we devise a new fitness function for appraising candidate solution vectors in the set covering problem (SCP), strengthening the evolutionary guidance for genetic algorithms. Eventually, experimental findings on the simulated and real airplanes corroborate the efficacy of the proposed method, i.e., it markedly diminishes the requisite number of viewpoints and augments inspection efficiency.

为了提高目视检测效率，有效地完成三维目视覆盖任务，本文重点研究了无人机对飞机表面目视覆盖的三维视图规划问题。我们的目标是用最少的视点获得足够高的覆盖率。这项工作的贡献列举如下。首先，根据安装在无人机上的摄像机的深度范围对目标飞行器的三维模型进行空间扩展，从而限制了三维视点的采样范围。然后，通过随机抽样和概率势场技术生成候选视点集。随后，我们提出了一种新的超启发式算法。在该算法中，遗传算法作为一个高级启发式策略，串联多个低级启发式算子设计用于组合优化。这不仅增强了算法寻求全局最优解的能力，而且加快了算法的收敛速度，旨在确定视点的最优子集。此外，我们设计了一个新的适应度函数来评价集合覆盖问题（SCP）中的候选解向量，加强了对遗传算法的进化指导。最后，在模拟和真实飞机上的实验结果证实了该方法的有效性，即显著减少了所需视点的数量，提高了检测效率。

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引用次数: 0

An improved path planning and tracking control method for planetary exploration rovers with traversable tolerance 具有可穿越公差的行星探测车的改进路径规划和跟踪控制方法

Biomimetic Intelligence and Robotics

Pub Date : 2025-02-15 DOI: 10.1016/j.birob.2025.100219

Haojie Zhang, Feng Jiang, Qing Li

In order to ensure the safety and efficiency of planetary exploration rovers, path planning and tracking control of a planetary rover are expected to consider factors such as complex 3D terrain features, the motion constraints of the rover, traversability, etc. An improved path planning and tracking control method is proposed for planetary exploration rovers on rough terrain in this paper. Firstly, the kinematic model of the planetary rover is established. A 3D motion primitives library adapted to various terrains and the rover’s orientations is generated. The state expansion process and heuristic function of the A* algorithm are improved using the motion primitives and terrain features. Global path is generated by improved A*-based algorithm that satisfies the planetary rover’s kinematic constraints and the 3D terrain restrictions. Subsequently, an optional arc path set is designed based on the traversable capabilities of the planetary rover. Each arc path corresponds to a specific motion that determines the linear and angular velocities of the planetary rover. The optimal path is selected through the multi-objective evaluation function. The planetary rover is driven to accurately track the global path by sending optimal commands that corresponds to the optimal path for real-time obstacle avoidance. Finally, the path planning and tracking control method is effectively validated during a given mission through two simulation tests. The experiment results show that the improved A*-based algorithm reduces planning time by 30.05% and generates smoother paths than the classic A* algorithm. The multi-objective arc-based method improves the rover’s motion efficiency, ensuring safer and quicker mission completion along the global path.

为了保证行星探测车的安全、高效运行，行星探测车的路径规划与跟踪控制需要考虑复杂的三维地形特征、探测车自身的运动约束、可穿越性等因素。提出了一种改进的行星探测车在崎岖地形上的路径规划与跟踪控制方法。首先，建立了行星漫游车的运动学模型。生成了适应不同地形和探测车方向的三维运动原语库。利用运动原语和地形特征，改进了A*算法的状态展开过程和启发式函数。采用改进的A*算法生成全局路径，满足行星漫游车的运动学约束和三维地形约束。然后，根据行星漫游车的可穿越能力，设计了可选的圆弧路径集。每个弧线路径对应于一个特定的运动，决定了行星探测器的线速度和角速度。通过多目标评价函数选择最优路径。通过发送与实时避障最优路径相对应的最优指令，驱动行星漫游者精确跟踪全局路径。最后，通过两次仿真试验，在给定任务中有效验证了路径规划和跟踪控制方法。实验结果表明，改进的基于A*的算法比经典的A*算法减少了30.05%的规划时间，生成的路径更平滑。基于多目标弧线的方法提高了火星车的运动效率，确保火星车在全局路径上更安全、更快地完成任务。

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引用次数: 0

Bathing assistive devices and robots for the elderly 老年人沐浴辅助设备和机器人

Biomimetic Intelligence and Robotics

Pub Date : 2025-02-14 DOI: 10.1016/j.birob.2025.100218

Jian Li , Yadong Mo , Shijie Jiang , Lifang Ma , Ying Zhang , Shimin Wei

The issue of aging population has become a severe problem that restricts global development. Thus, the development of bathing robots for the elderly is of great significance for the national strategy of actively addressing population aging. However, there is a lack of systematic review and analysis for the elderly bathing aids and robots, and the trend of the future development is also unclear. Therefore, by reviewing the relevant literature, this paper systematically analyzes the technical characteristics and usage scenarios of the lying, sitting and auxiliary posture, based on the bathing methods, bathing modes, and post bath care, which can clarify the current research status of bathing aids and robots for the elderly. Meanwhile, from the perspectives of the structural design, motion control and information intelligence, the key technologies and existing problems of bathing aids and robots are elaborated, and the relevant technical system is sorted out. Finally, based on the future of technological elderly care and the elderly bathing needs, the development trend of elderly bathing aids and robots is prospected, and the reference and suggestions for its research and development is provided, which has positive research significance.

人口老龄化问题已成为制约全球发展的严重问题。因此，开发老年人沐浴机器人对于积极应对人口老龄化的国家战略具有重要意义。然而，对于老年人助浴器和机器人缺乏系统的回顾和分析，未来的发展趋势也不明朗。因此，本文通过查阅相关文献，系统分析了基于沐浴方式、沐浴模式、沐浴后护理的躺姿、坐姿和辅助姿势的技术特点和使用场景，可以明确老年人沐浴辅助工具和机器人的研究现状。同时，从结构设计、运动控制和信息智能的角度，阐述了助浴器和机器人的关键技术和存在的问题，梳理了相关的技术体系。最后，基于未来的科技养老和老年人洗浴需求，对老年人洗浴辅助设备和机器人的发展趋势进行了展望，并为其研发提供了参考和建议，具有积极的研究意义。

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引用次数: 0

Automatic analysis of alarm embedded with large language model in police robot 警用机器人嵌入式大语言模型报警自动分析

Biomimetic Intelligence and Robotics

Pub Date : 2025-02-14 DOI: 10.1016/j.birob.2025.100220

Zirui Liu, Haichun Sun, Deyu Yuan

Police robots are used to assist police officers in performing tasks in complex environments, so as to improve the efficiency of law enforcement, ensure the safety of police officers and maintain social stability. With the rapid development of science and technology, police robots are widely used in the field of public security, such as alarm reception, patrol, explosive disposal, reconnaissance and so on. However, police robots still have the problem of analysis deviation in the process of receiving the alarm, which leads to the low efficiency of police dispatch. This study aims to enhance the police alarm automatic analysis ability of the police robots to assist in the dispatch of police. In this paper, we propose a novel method (FSTC-LLM) for sample augmentation based on large language model and noise reduction. The experimental evaluations are carried out on the alarm data set and the THUC News data set. The results show that the proposed FSTC-LLM has excellent performance in few shot text augmentation tasks, and can assist police robots to complete the task of automatic analysis of alarm with high quality, which is of great significance to enhance public security.

警用机器人用于协助警务人员在复杂环境下执行任务，从而提高执法效率，保障警务人员的安全，维护社会稳定。随着科学技术的飞速发展，警用机器人被广泛应用于公安领域，如报警接收、巡逻、排爆、侦察等。然而，警察机器人在接收报警过程中仍然存在分析偏差的问题，导致警察调度效率低下。本研究旨在增强警用机器人的警情报警自动分析能力，以辅助警力调度。本文提出了一种基于大语言模型和降噪的样本增强方法（FSTC-LLM）。对报警数据集和清华大学新闻数据集进行了实验评估。结果表明，本文提出的FSTC-LLM在少数镜头文本增强任务中具有优异的性能，能够辅助警用机器人高质量地完成报警自动分析任务，对增强公共安全具有重要意义。

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引用次数: 0

Human-in-the-loop transfer learning in collision avoidance of autonomous robots 自主机器人避碰中的人在环迁移学习

Biomimetic Intelligence and Robotics

Pub Date : 2025-01-28 DOI: 10.1016/j.birob.2025.100215

Minako Oriyama , Pitoyo Hartono , Hideyuki Sawada

Neural networks have demonstrated exceptional performance across a range of applications. Yet, their training often demands substantial time and data resources, presenting a challenge for autonomous robots operating in real-world environments where real-time learning is difficult. To mitigate this constraint, we propose a novel human-in-the-loop framework that harnesses human expertise to mitigate the learning challenges of autonomous robots. Our approach centers on directly incorporating human knowledge and insights into the robot’s learning pipeline. The proposed framework incorporates a mechanism for autonomous learning from the environment via reinforcement learning, utilizing a pre-trained model that encapsulates human knowledge as its foundation. By integrating human-provided knowledge and evaluation, we aim to bridge the division between human intuition and machine learning capabilities. Through a series of collision avoidance experiments, we validated that incorporating human knowledge significantly improves both learning efficiency and generalization capabilities. This collaborative learning paradigm enables robots to utilize human common sense and domain-specific expertise, resulting in faster convergence and better performance in complex environments. This research contributes to the development of more efficient and adaptable autonomous robots and seeks to analyze how humans can effectively participate in robot learning and the effects of such participation, illuminating the intricate interplay between human cognition and artificial intelligence.

神经网络在一系列应用中表现出卓越的性能。然而，他们的训练往往需要大量的时间和数据资源，这对在现实环境中运行的自主机器人提出了挑战，因为实时学习是困难的。为了减轻这一限制，我们提出了一个新的人在环框架，利用人类的专业知识来减轻自主机器人的学习挑战。我们的方法是直接将人类的知识和见解融入机器人的学习管道。提出的框架结合了一种通过强化学习从环境中自主学习的机制，利用封装人类知识的预训练模型作为其基础。通过整合人类提供的知识和评估，我们的目标是弥合人类直觉和机器学习能力之间的鸿沟。通过一系列的避碰实验，我们验证了将人类知识结合在一起可以显著提高学习效率和泛化能力。这种协作学习模式使机器人能够利用人类的常识和特定领域的专业知识，从而在复杂环境中实现更快的收敛和更好的性能。本研究有助于开发更高效、适应性更强的自主机器人，并试图分析人类如何有效地参与机器人学习以及这种参与的影响，阐明人类认知与人工智能之间复杂的相互作用。

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引用次数: 0