Biomimetic Intelligence and Robotics最新文献

英文中文

DS-YOLO: A dense small object detection algorithm based on inverted bottleneck and multi-scale fusion network DS-YOLO：基于倒置瓶颈和多尺度融合网络的密集小目标检测算法

Biomimetic Intelligence and Robotics

Pub Date : 2024-10-26 DOI: 10.1016/j.birob.2024.100190

Hongyu Zhang , Guoliang Li , Dapeng Wan , Ziyue Wang , Jinshun Dong , Shoujun Lin , Lixia Deng , Haiying Liu

In the field of security, intelligent surveillance tasks often involve a large number of dense and small objects, with severe occlusion between them, making detection particularly challenging. To address this significant challenge, Dense and Small YOLO (DS-YOLO), a dense small object detection algorithm based on YOLOv8s, is proposed in this paper. Firstly, to enhance the dense small objects’ feature extraction capability of backbone network, the paper proposes a lightweight backbone. The improved C2fUIB is employed to create a lightweight model and expand the receptive field, enabling the capture of richer contextual information and reducing the impact of occlusion on detection accuracy. Secondly, to enhance the feature fusion capability of model, a multi-scale feature fusion network, Light-weight Full Scale PAFPN (LFS-PAFPN), combined with the DO-C2f module, is introduced. The new module successfully reduces the miss rate of dense small objects while ensuring the accuracy of detecting large objects. Finally, to minimize feature loss of dense objects during network transmission, a dynamic upsampling module, DySample, is implemented. DS-YOLO was trained and tested on the CrowdHuman and VisDrone2019 datasets, which contain a large number of densely populated pedestrians, vehicles and other objects. Experimental evaluations demonstrated that DS-YOLO has advantages in dense small object detection tasks. Compared with YOLOv8s, the Recall and [email protected] are increased by 4.9% and 4.2% on CrowdHuman dataset, 4.6% and 5% on VisDrone2019, respectively. Simultaneously, DS-YOLO does not introduce a substantial amount of computing overhead, maintaining low hardware requirements.

在安防领域，智能监控任务往往涉及大量密集的小型物体，而且这些物体之间存在严重的遮挡，因此检测工作尤其具有挑战性。针对这一重大挑战，本文提出了一种基于 YOLOv8s 的密集小物体检测算法--密集小物体 YOLO（Dense and Small YOLO，DS-YOLO）。首先，为了增强骨干网的密集小目标特征提取能力，本文提出了一种轻量级骨干网。采用改进的 C2fUIB 创建轻量级模型并扩大感受野，从而能够捕获更丰富的上下文信息，降低遮挡对检测精度的影响。其次，为增强模型的特征融合能力，引入了多尺度特征融合网络--轻量级全尺度 PAFPN（LFS-PAFPN），并与 DO-C2f 模块相结合。新模块成功降低了高密度小物体的漏检率，同时保证了大物体的检测精度。最后，为了最大限度地减少密集物体在网络传输过程中的特征损失，还实施了动态上采样模块 DySample。DS-YOLO 在 CrowdHuman 和 VisDrone2019 数据集上进行了训练和测试，这两个数据集包含大量密集的行人、车辆和其他物体。实验评估表明，DS-YOLO 在密集小物体检测任务中具有优势。与 YOLOv8s 相比，DS-YOLO 在 CrowdHuman 数据集上的 Recall 和 [email protected] 分别提高了 4.9% 和 4.2%，在 VisDrone2019 上分别提高了 4.6% 和 5%。同时，DS-YOLO 没有引入大量计算开销，保持了较低的硬件要求。

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引用次数: 0

Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms 通过生物启发和基于图的算法实现基于人类自主团队的安全意识导航

Biomimetic Intelligence and Robotics

Pub Date : 2024-10-18 DOI: 10.1016/j.birob.2024.100189

Timothy Sellers , Tingjun Lei , Chaomin Luo , Zhuming Bi , Gene Eu Jan

In the field of autonomous robots, achieving complete precision is challenging, underscoring the need for human intervention, particularly in ensuring safety. Human Autonomy Teaming (HAT) is crucial for promoting safe and efficient human–robot collaboration in dynamic indoor environments. This paper introduces a framework designed to address these precision gaps, enhancing safety and robotic interactions within such settings. Central to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram (GVD) with spatio-temporal graphs, effectively combining human feedback, environmental factors, and key waypoints. An integral component of this system is the improved Node Selection Algorithm (iNSA), which utilizes the revised Grey Wolf Optimization (rGWO) for better adaptability and performance. Furthermore, an obstacle tracking model is employed to provide predictive data, enhancing the efficiency of the system. Human insights play a critical role, from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental changes. Extensive simulation and comparison tests confirm the reliability and effectiveness of our proposed model, highlighting its unique advantages in the domain of HAT. This comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications.

在自主机器人领域，实现完全精确具有挑战性，这就强调了人类干预的必要性，尤其是在确保安全方面。人类自主团队（HAT）对于促进动态室内环境中安全高效的人机协作至关重要。本文介绍了一个旨在解决这些精度差距的框架，以增强此类环境中的安全性和机器人互动。我们方法的核心是一个混合图系统，它将广义伏罗诺图（GVD）与时空图整合在一起，有效地结合了人类反馈、环境因素和关键航点。该系统的一个组成部分是改进的节点选择算法（iNSA），该算法采用了经修订的灰狼优化算法（rGWO），具有更好的适应性和性能。此外，还采用了障碍物跟踪模型来提供预测数据，从而提高了系统的效率。从提供初始环境数据和确定关键航点，到在意外挑战或动态环境变化时进行干预，人类的洞察力发挥着至关重要的作用。广泛的模拟和对比测试证实了我们所建议的模型的可靠性和有效性，凸显了其在 HAT 领域的独特优势。这种全面的方法确保了系统在现实世界的复杂应用中始终保持稳健性和响应性。

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引用次数: 0

Design of FDM-printable tendon-driven continuum robots using a serial S-shaped backbone structure 采用串联s型骨架结构的fdm可打印肌腱驱动连续机器人的设计

Biomimetic Intelligence and Robotics

Pub Date : 2024-10-18 DOI: 10.1016/j.birob.2024.100188

Kaidi Zhu, Tim C. Lueth, Yilun Sun

Tendon-driven continuum robots (TDCR) are widely used in various engineering disciplines due to their exceptional flexibility and dexterity. However, their complex structure often leads to significant manufacturing costs and lengthy prototyping cycles. To cope with this problem, we propose a fused-deposition-modeling-printable (FDM-printable) TDCR structure design using a serial S-shaped backbone, which enables planar bending motion with minimized plastic deformation. A kinematic model for the proposed TDCR structure based on the pseudo-rigid-body model (PRBM) approach is developed. Experimental results have revealed that the proposed kinematic model can effectively predict the bending motion under certain tendon forces. In addition, analyses of mechanical hysteresis and factors influencing bending stiffness are conducted. Finally, A three-finger gripper is fabricated to demonstrate a possible application of the proposed TDCR structure.

肌腱驱动连续体机器人（TDCR）由于其优异的灵活性和灵巧性被广泛应用于各种工程学科。然而，它们复杂的结构往往导致巨大的制造成本和漫长的原型周期。为了解决这一问题，我们提出了一种FDM-printable (FDM-printable) TDCR结构设计，该结构采用串联s形骨干，可以在最小塑性变形的情况下进行平面弯曲运动。基于拟刚体模型（PRBM）方法建立了TDCR结构的运动学模型。实验结果表明，所提出的运动学模型能够有效地预测在一定肌腱力作用下的弯曲运动。此外，还对弯曲刚度的影响因素和机械滞回进行了分析。最后，制作了一个三指夹持器来演示所提出的TDCR结构的可能应用。

引用次数: 0

Editorial for the special issue on bio-inspired robotic dexterity intelligence 为生物启发机器人灵巧智能特刊撰写社论

Biomimetic Intelligence and Robotics

Pub Date : 2024-10-16 DOI: 10.1016/j.birob.2024.100186

Qiang Li, Shuo Wang, Cong Wang, Jihong Zhu

引用次数: 0

Leveraging large language models for comprehensive locomotion control in humanoid robots design 在仿人机器人设计中利用大型语言模型实现综合运动控制

Biomimetic Intelligence and Robotics

Pub Date : 2024-10-16 DOI: 10.1016/j.birob.2024.100187

Shilong Sun , Chiyao Li , Zida Zhao , Haodong Huang , Wenfu Xu

This paper investigates the utilization of large language models (LLMs) for the comprehensive control of humanoid robot locomotion. Traditional reinforcement learning (RL) approaches for robot locomotion are resource-intensive and rely heavily on manually designed reward functions. To address these challenges, we propose a method that employs LLMs as the primary designer to handle key aspects of locomotion control, such as trajectory planning, inverse kinematics solving, and reward function design. By using user-provided prompts, LLMs generate and optimize code, reducing the need for manual intervention. Our approach was validated through simulations in Unity, demonstrating that LLMs can achieve human-level performance in humanoid robot control. The results indicate that LLMs can simplify and enhance the development of advanced locomotion control systems for humanoid robots.

本文研究了利用大型语言模型（LLM）对仿人机器人运动进行综合控制的问题。用于机器人运动的传统强化学习（RL）方法是资源密集型的，并且严重依赖人工设计的奖励函数。为了应对这些挑战，我们提出了一种方法，利用 LLM 作为主要设计器来处理运动控制的关键环节，如轨迹规划、逆运动学求解和奖励函数设计。通过使用用户提供的提示，LLM 生成并优化代码，从而减少了人工干预的需要。我们的方法通过在 Unity 中的仿真进行了验证，证明 LLM 可以在仿人机器人控制中实现人类水平的性能。结果表明，LLM 可以简化和增强仿人机器人高级运动控制系统的开发。

引用次数: 0

LQR-based control strategy for improving human–robot companionship and natural obstacle avoidance 基于 LQR 的控制策略，改善人机陪伴和自然避障能力

Biomimetic Intelligence and Robotics

Pub Date : 2024-10-04 DOI: 10.1016/j.birob.2024.100185

Zefan Su , Hanchen Yao , Jianwei Peng , Zhelin Liao , Zengwei Wang , Hui Yu , Houde Dai , Tim C. Lueth

In the dynamic and unstructured environment of human–robot symbiosis, companion robots require natural human–robot interaction and autonomous intelligence through multimodal information fusion to achieve effective collaboration. Nevertheless, the control precision and coordination of the accompanying actions are not satisfactory in practical applications. This is primarily attributed to the difficulties in the motion coordination between the accompanying target and the mobile robot. This paper proposes a companion control strategy based on the Linear Quadratic Regulator (LQR) to enhance the coordination and precision of robot companion tasks. This method enables the robot to adapt to sudden changes in the companion target’s motion. Besides, the robot could smoothly avoid obstacles during the companion process. Firstly, a human–robot companion interaction model based on nonholonomic constraints is developed to determine the relative position and orientation between the robot and the companion target. Then, an LQR-based companion controller incorporating behavioral dynamics is introduced to simultaneously avoid obstacles and track the companion target’s direction and velocity. Finally, various simulations and real-world human–robot companion experiments are conducted to regulate the relative position, orientation, and velocity between the target object and the robot platform. Experimental results demonstrate the superiority of this approach over conventional control algorithms in terms of control distance and directional errors throughout system operation. The proposed LQR-based control strategy ensures coordinated and consistent motion with target persons in social companion scenarios.

在动态和非结构化的人机共生环境中，陪伴机器人需要自然的人机交互，并通过多模态信息融合实现自主智能，从而实现有效协作。然而，在实际应用中，伴随行动的控制精度和协调性并不令人满意。这主要是由于伴随目标与移动机器人之间的运动协调存在困难。本文提出了一种基于线性二次调节器（LQR）的陪伴控制策略，以提高机器人陪伴任务的协调性和精确性。这种方法能使机器人适应伴随目标运动的突然变化。此外，机器人还能在陪伴过程中顺利避开障碍物。首先，建立了一个基于非人体工学约束的人机交互模型，以确定机器人与陪伴目标之间的相对位置和方向。然后，引入基于 LQR 的伴行控制器，其中包含行为动力学，以同时避开障碍物并跟踪伴行目标的方向和速度。最后，通过各种模拟和真实世界的人机陪伴实验来调节目标物体与机器人平台之间的相对位置、方向和速度。实验结果表明，这种方法在整个系统运行过程中的控制距离和方向误差方面优于传统控制算法。所提出的基于 LQR 的控制策略可确保在社交陪伴场景中与目标人物协调一致地运动。

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引用次数: 0

Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks 利用 Kolmogorov-Arnold 网络建立柔性 EHD 泵的预测模型

Biomimetic Intelligence and Robotics

Pub Date : 2024-09-17 DOI: 10.1016/j.birob.2024.100184

Yanhong Peng , Yuxin Wang , Fangchao Hu , Miao He , Zebing Mao , Xia Huang , Jun Ding

We present a novel approach to predicting the pressure and flow rate of flexible electrohydrodynamic pumps using the Kolmogorov–Arnold Network. Inspired by the Kolmogorov–Arnold representation theorem, KAN replaces fixed activation functions with learnable spline-based activation functions, enabling it to approximate complex nonlinear functions more effectively than traditional models like Multi-Layer Perceptron and Random Forest. We evaluated KAN on a dataset of flexible EHD pump parameters and compared its performance against RF, and MLP models. KAN achieved superior predictive accuracy, with Mean Squared Errors of 12.186 and 0.012 for pressure and flow rate predictions, respectively. The symbolic formulas extracted from KAN provided insights into the nonlinear relationships between input parameters and pump performance. These findings demonstrate that KAN offers exceptional accuracy and interpretability, making it a promising alternative for predictive modeling in electrohydrodynamic pumping.

我们提出了一种利用 Kolmogorov-Arnold 网络预测柔性电动流体动力泵压力和流量的新方法。受科尔莫哥洛夫-阿诺德表示定理的启发，KAN 用可学习的基于样条的激活函数取代了固定的激活函数，使其能够比多层感知器和随机森林等传统模型更有效地逼近复杂的非线性函数。我们在灵活的 EHD 泵参数数据集上对 KAN 进行了评估，并将其性能与 RF 和 MLP 模型进行了比较。KAN 的预测准确度更高，压力和流量预测的平均平方误差分别为 12.186 和 0.012。从 KAN 中提取的符号公式有助于深入了解输入参数与泵性能之间的非线性关系。这些研究结果表明，KAN 具有卓越的准确性和可解释性，是电液动力泵预测建模的理想选择。

引用次数: 0

Human–robot collaborative handling of curtain walls using dynamic motion primitives and real-time human intention recognition 利用动态运动基元和实时人类意图识别实现幕墙的人机协作操控

Biomimetic Intelligence and Robotics

Pub Date : 2024-09-10 DOI: 10.1016/j.birob.2024.100183

Fengming Li , Huayan Sun , Enguang Liu , Fuxin Du

Human–robot collaboration fully leverages the strengths of both humans and robots, which is crucial for handling large, heavy objects at construction sites. To address the challenges of human–machine cooperation in handling large-scale, heavy objects — specifically building curtain walls — a human–robot collaboration system was designed based on the concept of “human–centered with machine support”. This system allows the handling of curtain walls according to different human intentions. First, a robot trajectory learning and generalization model based on dynamic motion primitives was developed. The operator’s motion intent was then characterized by their speed, force, and torque, with the force impulse introduced to define the operator’s intentions for acceleration and deceleration. Finally, a collaborative experiment was conducted on an experimental platform to validate the robot’s understanding of human handling intentions and to verify its ability to handle curtain wall. Collaboration between humans and robots ensured a smooth and labor-saving handling process.

人机协作能充分发挥人类和机器人的优势，这对于在建筑工地搬运大型重物至关重要。为了解决人机合作搬运大型重型物体（特别是建筑幕墙）所面临的挑战，我们设计了一套基于 "以人为本、机器支持 "理念的人机协作系统。该系统可根据人的不同意图搬运幕墙。首先，开发了基于动态运动基元的机器人轨迹学习和泛化模型。然后，通过速度、力和扭矩来描述操作员的运动意图，并引入力冲来定义操作员的加速和减速意图。最后，在实验平台上进行了协作实验，以验证机器人对人类操控意图的理解，并验证其操控幕墙的能力。人类与机器人之间的协作确保了平稳、省力的搬运过程。

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引用次数: 0

Design of a spider-inspired wheeled compliant leg for search mobile robots 为搜索移动机器人设计受蜘蛛启发的轮式顺应腿

Biomimetic Intelligence and Robotics

Pub Date : 2024-09-06 DOI: 10.1016/j.birob.2024.100182

Yilin Wang, Felix Pancheri, Tim C. Lueth, Yilun Sun

Earthquake and other disasters nowadays still threat people’s lives and property due to their destructiveness and unpredictability. The past decades have seen the booming development of search and rescue robots due to their potential for increasing rescue capacity as well as reducing personnel safety risk at disaster sites. In this work, we propose a spider-inspired wheeled compliant leg to further improve the environmental adaptability of search mobile robots. Different from the traditional fully-actuated method with independent motor joint control, this leg employs an under-actuated compliant mechanism design with overall semi-tendon-driven control, which enables the passive and active terrain adaptation, system simplification and lightweight of the realized search robot. We have generalized the theoretical model and design methodology for this type of compliant leg, and implement it in a parametric program to improve the design efficiency. In addition, preliminary load capacity and leg-lifting experiments are carried out on a one-leg prototype to evaluate its mechanical performance. A four-legged robot platform is also fabricated for the locomotion tests. The preliminary experimental results have verified the feasibility of the proposed design methodology, and also show possibilities for improvements. In future work, structural optimization and stronger actuation elements should be introduced to further improve the mechanical performance of the fabricated wheeled leg mechanism and robot platform.

如今，地震和其他灾害因其破坏性和不可预测性，仍然威胁着人们的生命和财产安全。在过去的几十年里，搜救机器人得到了蓬勃发展，因为它不仅能提高救援能力，还能降低灾害现场的人员安全风险。在这项工作中，我们提出了一种受蜘蛛启发的轮式顺应腿，以进一步提高搜索移动机器人的环境适应能力。与传统的独立电机关节控制的全驱动方式不同，该腿采用了整体半腱驱动控制的欠驱动顺应机构设计，实现了搜索机器人的被动和主动地形适应、系统简化和轻量化。我们归纳了这种顺应式支腿的理论模型和设计方法，并将其实现在参数化程序中，以提高设计效率。此外，我们还在单腿原型上进行了初步的负载能力和抬腿实验，以评估其机械性能。此外，还制作了一个四足机器人平台，用于运动测试。初步实验结果验证了拟议设计方法的可行性，同时也显示了改进的可能性。在今后的工作中，应引入结构优化和更强的执行元件，以进一步提高制造的轮腿机构和机器人平台的机械性能。

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引用次数: 0

A bionic robotic ankle driven by the multiple pneumatic muscle actuators 由多个气动肌肉致动器驱动的仿生机器人脚踝

Biomimetic Intelligence and Robotics

Pub Date : 2024-08-12 DOI: 10.1016/j.birob.2024.100176

Delei Fang, Fangyuan Ren, Jianwei Wang, Pan Li, Lin Cao, Junxia Zhang

The traditional pneumatic muscle robot joint has weak load capacity and low control precision. This paper proposes a bionic robotic ankle driven by multiple pneumatic muscle actuators. The structural design of the bionic robotic ankle and the drive mechanism that imitates human muscle recruitment are introduced. A dynamic model of the ankle and a static model of the pneumatic muscle actuator are established to analyze the driving characteristics. The multi-muscle recruiting strategy and load matching control method are optimized, and the output characteristics are simulated, including the robotic ankle driven by a single pneumatic muscle actuator, the robotic ankle driven by dual pneumatic muscle actuators, and the bionic ankle driven by multiple pneumatic muscle actuators. A prototype and testing platform are developed, and experimental research is carried out to validate the theoretical analysis and simulation. The results show that the bionic robotic ankle driven by multiple pneumatic muscle actuators can match varied loads, effectively reducing angle error and increasing output force.

传统的气动肌肉机器人关节负载能力弱、控制精度低。本文提出了一种由多个气动肌肉致动器驱动的仿生机器人踝关节。本文介绍了仿生机器人踝关节的结构设计和模仿人体肌肉运动的驱动机制。建立了踝关节的动态模型和气动肌肉致动器的静态模型，以分析其驱动特性。优化了多肌肉招募策略和负载匹配控制方法，模拟了单气动肌肉致动器驱动的机器人踝关节、双气动肌肉致动器驱动的机器人踝关节和多气动肌肉致动器驱动的仿生踝关节的输出特性。研究人员开发了原型和测试平台，并开展了实验研究，以验证理论分析和模拟结果。结果表明，由多个气动肌肉致动器驱动的仿生机器人踝关节可以匹配不同的负载，有效减少角度误差并增加输出力。

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