Biomimetic Intelligence and Robotics最新文献

Improved PRM algorithm based on dynamic partitioning and adaptive sampling 基于动态划分和自适应采样的改进PRM算法

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2026-01-20 DOI: 10.1016/j.birob.2026.100283

Yifan Wang, Xiangrong Zhao, Gang Chen, Xianyuan Gao, Kaichao Chen

The Probabilistic Roadmap (PRM) algorithm has been widely employed in robotic manipulator path planning tasks due to its rapid exploration capabilities, particularly in high-dimensional configuration spaces with complex kinematic and environmental constraints. However, the efficiency of PRM is inherently constrained by the distribution of sampling points. In scenarios involving narrow passages, the sparsity of samples within such regions may significantly increase the likelihood of planning failure. In view of this, this paper proposes an improved PRM algorithm that is suitable for narrow channels with obstacles and can significantly improve the efficiency of path planning. First, a non-uniform partitioning strategy based on obstacle density is proposed to dynamically divide the sampling area to reduce the connection of redundant edges. Second, to address the sampling failure often encountered in narrow passages due to insufficient sample points, a weighted sampling adjustment strategy is proposed, which adaptively modifies the sampling density between narrow and open regions based on a comprehensive distance metric. Third, an adaptive variable step-size strategy is developed to dynamically adjust the connection steps between obstacle boundaries and open areas, further enhancing roadmap connectivity. By integrating the aforementioned strategies, the improved PRM algorithm proposed was applied in both two-dimensional and three-dimensional environments. The simulation results demonstrate that the method is capable of finding feasible paths in complex scenarios. Compared to the Lazy PRM and the OBPRM algorithms, the proposed approach achieves reductions of approximately 8.77% and 7.44% in path length and 9.00% and 5.74% in planning time, respectively. Finally, its effectiveness and superiority in robotic manipulator path planning were further validated through application to a 7-DOF manipulator.

概率路线图（Probabilistic Roadmap， PRM）算法由于其快速的探测能力，特别是在具有复杂运动和环境约束的高维构型空间中，被广泛应用于机器人机械手路径规划任务中。然而，PRM的效率受到采样点分布的固有约束。在涉及狭窄通道的场景中，这些区域内样本的稀疏性可能会显著增加规划失败的可能性。鉴于此，本文提出了一种改进的PRM算法，该算法适用于有障碍物的狭窄通道，可以显著提高路径规划的效率。首先，提出了一种基于障碍物密度的非均匀划分策略，对采样区域进行动态划分，减少冗余边的连接；其次，针对狭窄通道中采样点不足导致采样失败的问题，提出了一种加权采样调整策略，该策略基于综合距离度量自适应调整狭窄区域和开放区域之间的采样密度；第三，提出一种自适应变步长策略，动态调整障碍物边界与开放区域之间的连接步长，进一步增强路线图的连通性。通过综合上述策略，提出的改进PRM算法在二维和三维环境中都得到了应用。仿真结果表明，该方法能够在复杂场景下找到可行路径。与Lazy PRM和OBPRM算法相比，该方法的路径长度分别减少了8.77%和7.44%，规划时间分别减少了9.00%和5.74%。最后，通过对一个7自由度机械臂的应用，进一步验证了该方法在机械臂路径规划中的有效性和优越性。

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引用次数: 0

Investigation of efficient creeping locomotion for snake-like robots with compliant passive joints 柔性被动关节蛇形机器人高效爬行运动研究

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2026-01-20 DOI: 10.1016/j.birob.2026.100281

Yiming Cao , Longchuan Li , Zhenxuan Ma , Zaiyang Liu , Atsushi Kakogawa , Shugen Ma , Zhongkui Wang

Snake-like robots leverage their slender bodies to navigate confined spaces by coordinating the multiple actuated joints, which enable effective movement through constrained pathways. However, their high degrees of freedom in fully actuated systems engender significant challenges in reducing energy consumption. To address these challenges, this paper derives insights from the muscle functions of biological snakes and investigates the integration of compliance passive joints into snake-like robots, with the aim of enhancing locomotion efficiency. Passive joints, equipped with torsional springs, facilitate indirect actuation through energy storage and release. Under such background, we propose a dynamic model to investigate the influence of passive joints on locomotion performance. Simulations are utilized to analyze the effects of varying spring stiffness beyond experimental constraints. To facilitate systematic validation, a modular snake-like robot is designed. It allows flexible joint configurations, reassembly, and adjustable joint placements. Additionally, passive joint mechanism is refined to eliminate the requirements for motor gear reconfiguration, thereby improving experimental adaptability. The proposed model is evaluated through simulations and experiments to investigate the effects of joint stiffness on locomotion speed, while energy efficiency is analyzed experimentally. The results reveal that appropriate stiffness parameters significantly enhance motion efficiency. Moreover, the placement of passive joints plays a key role in the robot’s motion performance. Among all configurations, a compliant passive tail joint with an appropriate spring setup achieves the best performance. It increases motion speed by 26.8% and reduces energy consumption by 52.2%. These findings provide insights into the role of passive joints in snake-like robots, potentially contributing to future design improvements in locomotion efficiency and adaptability.

蛇形机器人利用它们细长的身体，通过协调多个驱动关节来导航狭窄的空间，从而在受限的路径上有效地移动。然而，它们在全驱动系统中的高度自由度在降低能耗方面带来了重大挑战。为了解决这些挑战，本文从生物蛇的肌肉功能中获得见解，并研究将顺应性被动关节集成到蛇形机器人中，以提高运动效率。被动式关节，配备扭力弹簧，通过能量储存和释放促进间接驱动。在此背景下，我们提出了一个动态模型来研究被动关节对运动性能的影响。利用仿真分析了超出实验约束的不同弹簧刚度的影响。为了便于系统验证，设计了模块化蛇形机器人。它允许灵活的关节配置，重组和可调的关节位置。此外，对被动关节机构进行了改进，消除了对电机齿轮重构的要求，提高了实验适应性。通过仿真和实验验证了关节刚度对运动速度的影响，并对能量效率进行了实验分析。结果表明，适当的刚度参数可显著提高运动效率。此外，被动关节的位置对机器人的运动性能起着至关重要的作用。在所有配置中，具有适当弹簧设置的顺从被动尾接头达到最佳性能。运动速度提高26.8%，能耗降低52.2%。这些发现提供了被动关节在蛇形机器人中的作用的见解，可能有助于未来在运动效率和适应性方面的设计改进。

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引用次数: 0

A vision-based humanoid compliant skill transfer framework: Application to robotic cutting tasks 基于视觉的仿人柔顺技能转移框架：在机器人切割任务中的应用

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2026-01-17 DOI: 10.1016/j.birob.2026.100280

Zhaohong Mai , Chao Zeng , Ning Wang , Chenguang Yang

Autonomously completing a contact-rich task for multiple manipulation objects remains a challenging problem for robots. To achieve this goal, learning from demonstration has emerged as an efficient method for transferring human-like skills to robots. Existing works primarily focus on trajectory or impedance learning to design force-impedance controllers for specific tasks, which require precise force sensing. However, visual perception plays a critical role in enabling humans to perform dexterous manipulation. To bridge the gap between vision and learning in the control loop, this work proposes a vision-based humanoid compliant skill transfer (VHCST) framework. Considering the lack of vision-impedance mapping, a hybrid tree is introduced as a planning bridge to encode skill parameters across multiple objects. To simplify skill transfer, an observation-wearable demonstration method is employed to capture the position and stiffness of human’s arm. The decoupled learning model incorporates the geometric properties of stiffness ellipsoids, which reside on Riemannian manifolds. Finally, the proposed approach is validated through robotic cutting experiments involving multiple objects. Comparative experimental results demonstrate the effectiveness of the proposed framework.

对于机器人来说，自主完成多操作对象的富接触任务仍然是一个具有挑战性的问题。为了实现这一目标，从示范中学习已经成为一种将类人技能转移给机器人的有效方法。现有的工作主要集中在轨迹或阻抗学习，以设计特定任务的力阻抗控制器，这需要精确的力传感。然而，视觉感知在使人类能够进行灵巧的操作中起着关键作用。为了在控制回路中弥合视觉和学习之间的差距，本工作提出了一个基于视觉的类人柔顺技能转移（VHCST）框架。考虑到视觉-阻抗映射的不足，引入混合树作为规划桥梁，跨多个目标对技能参数进行编码。为了简化技能转移，采用观察-穿戴演示方法捕捉人体手臂的位置和刚度。解耦学习模型结合了黎曼流形上的刚性椭球体的几何特性。最后，通过多目标机器人切割实验验证了该方法的有效性。对比实验结果证明了该框架的有效性。

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引用次数: 0

End-to-end replay-based trajectory planning for autonomous vehicles under multi-weather scenarios 基于端到端回放的多天气自动驾驶车辆轨迹规划

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2026-01-14 DOI: 10.1016/j.birob.2026.100275

Jinjun Dun , Yuenan Zhao , Xiaoyu Xu , Zhenguo Chen , Hui Xie

Autonomous driving systems face challenges from perception degradation and kinematic coupling in adverse weather. This paper introduces an end-to-end trajectory prediction framework integrating multi-weather continual learning with kinematic constraint optimization. Traditional weather-specific models suffer from fragmented experience and catastrophic forgetting, impacting control in low-visibility, high-curvature scenarios. We propose a multi-weather adaptive replay mechanism (MWARM) with entropy-weighted sampling for cross-weather knowledge transfer, paired with a bird’s eye view (BEV)-based perception-planning architecture using multi-objective model predictive control (MO-MPC) to adjust weights based on real-time curvature and weather data. Evaluated in CARLA with a multi-weather dataset, the framework provides a robust solution for complex conditions.

在恶劣天气条件下，自动驾驶系统面临感知退化和运动耦合的挑战。介绍了一种结合多天气连续学习和运动约束优化的端到端弹道预测框架。传统的特定天气模型存在经验碎片化和灾难性遗忘的问题，影响了在低能见度、高曲率情况下的控制。我们提出了一种基于熵加权采样的多天气自适应重播机制（MWARM），用于跨天气知识转移，并结合基于鸟瞰（BEV）的感知规划架构，使用多目标模型预测控制（MO-MPC）根据实时曲率和天气数据调整权重。在CARLA中使用多天气数据集进行评估，该框架为复杂条件提供了强大的解决方案。

引用次数: 0

Optimization-based automated generation of 1-DOF multi-section continuum robots with predefined end-effector poses 基于优化的末端执行器位姿1自由度多截面连续体机器人自动生成

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2026-01-14 DOI: 10.1016/j.birob.2026.100276

Jiake Fu, Zengwei Wang, Felix Pancheri, Tim C. Lueth, Yilun Sun

Continuum robots have been widely utilized in various fields, such as medical surgery, industrial manufacturing, and aerospace, due to their flexibility and compliance. However, their high structural compliance also presents significant challenges in achieving precise control. Although many existing continuum robots feature multiple degrees-of-freedom (DOFs) and complex control systems, such sophistication is often unnecessary for simple, repetitive, and task-specific applications where task-specific structures are more efficient. To address this issue, this paper proposes a parametric optimization-based automated design framework to generate structural models for multi-section 1-DOF flexure-joint-based continuum robots capable of achieving any two predefined end-effector poses. The proposed methodology employs a constant curvature assumption to simulate the bending characteristics of the continuum robot. MATLAB is used to optimize and solve the structural parameters, followed by the generation of 3D-printable models using the Solid Geometry Library Toolbox. Experimental results demonstrate that, under certain geometric boundary conditions for structural parameters, the robot’s end-effector can reach any two predefined poses with high accuracy. This approach significantly reduces the structural and control complexity of task-specific continuum robots, lowers manufacturing costs, and expands their range of applications.

连续体机器人由于其灵活性和顺应性被广泛应用于医疗外科、工业制造和航空航天等各个领域。然而，它们的高结构顺应性也提出了实现精确控制的重大挑战。尽管许多现有的连续体机器人具有多个自由度（dof）和复杂的控制系统，但对于简单、重复和特定任务的应用来说，这种复杂性通常是不必要的，因为特定任务的结构更有效。为了解决这一问题，本文提出了一种基于参数优化的自动化设计框架，用于生成能够实现任意两个预定义末端执行器姿态的多截面1自由度柔性关节连续体机器人的结构模型。该方法采用常曲率假设来模拟连续体机器人的弯曲特性。利用MATLAB对结构参数进行优化求解，利用Solid Geometry Library Toolbox生成3d打印模型。实验结果表明，在一定的结构参数几何边界条件下，机器人末端执行器可以高精度地达到任意两个预定位姿。这种方法大大降低了特定任务连续体机器人的结构和控制复杂性，降低了制造成本，扩大了它们的应用范围。

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引用次数: 0

A feedforward tendon-elongation compensator for tendon-sheath mechanisms with arbitrary and time-varying transmission routes in three-dimensional space 一种适用于三维空间中具有任意时变传输路径的肌腱鞘机构的前馈肌腱伸长补偿器

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2026-01-13 DOI: 10.1016/j.birob.2026.100278

Qian Gao , Jiaqi Li

Tendon-sheath mechanisms (TSMs) are widely used for position transmission in robotic systems that require compactness and adaptability to complex environments. However, friction-induced tendon-elongation disrupts the alignment between input and output positions, preventing the robotic end-effector from accurately following motion commands. Since tendon-elongation depends on the configuration of the transmission route, resolving position transmission misalignment in TSMs becomes even more challenging. Building upon the tendon-elongation compensator developed in the author’s recent work, this study presents a technical note aiming to align the actual output position with the desired position. The improved compensator operates without relying on any distal sensory feedback, thereby preserving the compactness of the system. Notably, it is applicable to TSMs with arbitrary and time-varying transmission routes in three-dimensional (3-D) space, fulfilling the adaptability requirement. Preliminary experimental results demonstrate the potential of the presented technique, achieving 96.44%–97.56% accuracy in distal position tracking. By tackling a long-standing challenge in TSM research, this study lays a technical foundation for future advancements in the field.

腱鞘机构（TSMs）被广泛用于要求紧凑和适应复杂环境的机器人系统的位置传递。然而，摩擦引起的肌腱伸长破坏了输入和输出位置之间的对齐，阻止了机器人末端执行器准确地遵循运动命令。由于肌腱伸长取决于传输路径的配置，因此在tsm中解决位置传输错位变得更加具有挑战性。在作者最近的工作中开发的肌腱伸长率补偿器的基础上，本研究提出了一个技术说明，旨在将实际输出位置与期望位置对齐。改进的补偿器不依赖于任何远端感官反馈，从而保持了系统的紧凑性。值得注意的是，该方法适用于三维空间中具有任意时变传输路径的tsm，满足了自适应性要求。初步的实验结果证明了该技术的潜力，远端位置跟踪精度达到96.44% ~ 97.56%。通过解决TSM研究中一个长期存在的挑战，本研究为该领域的未来发展奠定了技术基础。

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引用次数: 0

Enhanced reasoning and task planning for surgical autonomy using multi-modal large language models with gradual learning 使用渐进学习的多模态大语言模型增强手术自主性的推理和任务规划

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2026-01-13 DOI: 10.1016/j.birob.2026.100277

Sadra Zargarzadeh , Jemima Okanlawon , Maryam Mirzaei , Mahan Mohammadi , Mahdi Tavakoli

Large language models (LLMs) have been widely adopted in robotic applications in recent years, but their ability in task planning of long-horizon and complex tasks remains a challenge. In this work, we present a gradual learning method to address this challenge and explore its usability in surgical training tasks that require high levels of reasoning, such as peg transfer and the sliding puzzle task. Experiments were conducted using the da Vinci Research Kit (dVRK), with environment feedback initiating follow-up prompts for the LLM when necessary, as well as in a simulation environment. Results showed that for complex tasks, the gradual learning method outperformed the direct approach in the LLM’s task and motion planning, requiring fewer follow-up prompts and leading to higher success rates with faster execution. This suggests that for complex pseudo-surgical tasks, it is more efficient to have the LLM solve simpler versions of the task while incrementally increasing complexity, rather than tackling the full complex task at once. The approach shows promise for enhancing robot-assisted surgery where tasks are complex, long-horizon, and demand high-reasoning abilities.

近年来，大型语言模型（llm）在机器人应用中得到了广泛的应用，但其在长视界和复杂任务的任务规划能力仍然是一个挑战。在这项工作中，我们提出了一种渐进学习方法来解决这一挑战，并探索其在需要高水平推理的外科训练任务中的可用性，例如peg转移和滑动拼图任务。实验使用达芬奇研究工具包（dVRK）进行，环境反馈在必要时启动LLM的后续提示，以及在模拟环境中进行。结果表明，对于复杂任务，渐进式学习方法在LLM的任务和运动规划方面优于直接学习方法，需要的后续提示更少，执行速度更快，成功率更高。这表明，对于复杂的伪手术任务，让LLM在逐步增加复杂性的同时解决任务的更简单版本会更有效，而不是一次处理整个复杂的任务。在任务复杂、视野长远、需要高度推理能力的领域，这种方法有望增强机器人辅助手术的能力。

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引用次数: 0

Large language model-based task planning for service robots: A review 基于大语言模型的服务机器人任务规划研究综述

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2026-01-10 DOI: 10.1016/j.birob.2026.100274

Shaohan Bian , Ying Zhang , Guohui Tian , Zhiqiang Miao , Edmond Q. Wu , Simon X. Yang , Changchun Hua

With the rapid advancement of large language models (LLMs) and robotics, service robots are increasingly becoming an integral part of daily life, offering a wide range of services in complex environments. To deliver these services intelligently and efficiently, robust and accurate task planning capabilities are essential. This paper presents a comprehensive overview of the integration of LLMs into service robotics, with a particular focus on their role in enhancing robotic task planning. First, the development and foundational techniques of LLMs, including pre-training, fine-tuning, retrieval-augmented generation (RAG), and prompt engineering, are reviewed. We then explore the application of LLMs as the cognitive core—“brain”—of service robots, discussing how LLMs contribute to improved autonomy and decision-making. Furthermore, recent advancements in LLM-driven task planning across various input modalities are analyzed, including text, visual, audio, and multimodal inputs. Finally, we summarize key challenges and limitations in current research and propose future directions to advance the task planning capabilities of service robots in complex, unstructured domestic environments. This review aims to serve as a valuable reference for researchers and practitioners in the fields of artificial intelligence and robotics.

随着大型语言模型（llm）和机器人技术的快速发展，服务机器人越来越成为日常生活中不可或缺的一部分，在复杂的环境中提供广泛的服务。为了智能而高效地交付这些服务，强大而准确的任务规划能力是必不可少的。本文全面概述了llm与服务机器人的集成，特别关注它们在增强机器人任务规划方面的作用。首先，回顾了法学硕士的发展和基础技术，包括预训练、微调、检索增强生成（RAG）和提示工程。然后，我们探讨了法学硕士作为服务机器人的认知核心——“大脑”的应用，讨论了法学硕士如何有助于提高自主性和决策能力。此外，分析了llm驱动的任务规划在各种输入模式中的最新进展，包括文本、视觉、音频和多模式输入。最后，我们总结了当前研究中的关键挑战和局限性，并提出了未来发展方向，以提高服务机器人在复杂、非结构化家庭环境中的任务规划能力。本文旨在为人工智能和机器人领域的研究人员和从业人员提供有价值的参考。

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引用次数: 0

HDCAR: A 3D-2D registration network for abdominal aortic vessels based on CTA vessel models and DSA images HDCAR：基于CTA血管模型和DSA图像的腹主动脉血管3D-2D配准网络

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-12-31 DOI: 10.1016/j.birob.2025.100272

Bo Zhang , Shiqi Liu , Xiaoliang Xie , Xiaohu Zhou , Zengguang Hou , Meng Song , Xiyao Ma , Kang Li , Zhichao Lai , Bao Liu

Multimodal image registration is a crucial prerequisite for the automation and intelligence of interventional surgical medical robots. In endovascular aneurysm repair, due to limitations in imaging principles and hemodynamic effects, single-frame DSA images often fail to provide a complete representation of the vascular structure. This is particularly true for blood vessels that run parallel to the X-ray beam, as they are difficult to visualize in the DSA images. To address this issue, this study proposes an abdominal aortic vessel registration network, HDCAR, based on preoperative CTA 3D vascular models and intraoperative DSA images, aiming to enhance vascular completeness and spatial consistency in intraoperative imaging. The HDCAR network integrates multiple optimization modules to improve registration accuracy and robustness. First, the K-Sample module is employed to filter DSA images, enhancing the uniformity of intra-vascular structures and improving contrast between vessels and surrounding tissues. Second, depth information is incorporated to strengthen cross-dimensional spatial feature fusion, thereby optimizing the alignment between preoperative 3D models and intraoperative 2D images. Additionally, the network utilizes a dual-rectangular-window-based cross-attention mechanism and the RankC module to enhance both global contextual relationships and local feature representations. The ASPP module is further employed to extract multi-scale feature information, improving the model’s ability to capture vascular structures. Finally, a two-stage hybrid loss function is applied to optimize network parameters, ensuring precise and stable image registration. Experimental results demonstrate that the HDCAR network achieves high-precision vascular registration across multi-modal images, significantly improving the completeness and accuracy of intraoperative vascular imaging. This provides more precise imaging support for endovascular aneurysm repair procedures and holds great potential for clinical applications.

多模态图像配准是介入手术医疗机器人实现自动化和智能化的重要前提。在血管内动脉瘤修复中，由于成像原理和血流动力学效果的限制，单帧DSA图像往往不能完整地反映血管结构。对于平行于x射线束的血管尤其如此，因为它们很难在DSA图像中可视化。针对这一问题，本研究提出基于术前CTA三维血管模型和术中DSA图像的腹主动脉血管配准网络HDCAR，旨在增强术中成像血管的完整性和空间一致性。HDCAR网络集成了多个优化模块，提高了配准精度和鲁棒性。首先，利用K-Sample模块对DSA图像进行滤波，增强血管内结构的均匀性，提高血管与周围组织的对比度。其次，结合深度信息加强跨维空间特征融合，优化术前三维模型与术中二维图像的对齐。此外，该网络利用基于双矩形窗口的交叉注意机制和RankC模块来增强全局上下文关系和局部特征表示。进一步利用ASPP模块提取多尺度特征信息，提高模型捕获血管结构的能力。最后，采用两级混合损失函数对网络参数进行优化，保证了图像配准的精度和稳定性。实验结果表明，HDCAR网络实现了跨多模态图像的高精度血管配准，显著提高了术中血管成像的完整性和准确性。这为血管内动脉瘤修复手术提供了更精确的成像支持，具有很大的临床应用潜力。

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引用次数: 0

Acoustic-enhanced local bearing estimation using low-cost microphones for Micro Air Vehicle swarms 基于低成本麦克风的微型飞行器群声学增强局部方位估计

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-09-10 DOI: 10.1016/j.birob.2025.100264

Aohua Li, Ye Zhou, Weijie Kuang, Hann Woei Ho

Micro Air Vehicle (MAV) swarms are often constrained by limited onboard processing capabilities and payload capacity, restricting the use of sophisticated localization systems. Lightweight ultra-wideband (UWB) ranging techniques are commonly used to estimate inter-vehicle distances, but they do not provide local bearing information—essential for precise relative positioning. Inspired by bat echolocation in low-visibility environments, we propose an acoustic-enhanced method for local bearing estimation designed for low-cost MAVs. Our approach leverages ambient acoustic signals naturally emitted by a target MAV in flight, combined with UWB distance measurements. The acoustic data is processed using the Frequency-Sliding Generalized Cross-Correlation (FS-GCC) method, enhanced with our analytical formulation that compensates for inter-channel switching delays in asynchronous, high-frequency sampling. This enables accurate Time Difference of Arrival (TDOA) estimation, even with compact microphone arrays. These TDOA values, along with known microphone geometry and UWB data, are integrated into our geometric model to estimate the bearing of the target MAV. We validate our approach in a controlled indoor hall across two experimental scenarios: static-bearing estimation, where the target MAV hovers at predefined angular positions (0°, ±30°, ±45°, ±60°), and dynamic-bearing estimation, where it flies across angles at varying velocities. The results show that our method yields reliable TDOA measurements compared to classical and machine learning baselines, and produces accurate bearing estimates in both static and dynamic settings. This demonstrates the feasibility of our low-cost acoustic-enhanced solution for local bearing estimation in MAV swarms, supporting improved relative navigation and decentralized perception in GPS-denied or visually degraded environments.

微型飞行器（MAV）群通常受到有限的机载处理能力和有效载荷能力的限制，限制了复杂定位系统的使用。轻型超宽带（UWB）测距技术通常用于估计车辆间距离，但它们不能提供精确相对定位所必需的局部方位信息。受蝙蝠在低能见度环境下回声定位的启发，我们提出了一种针对低成本mav的声学增强局部方位估计方法。我们的方法利用目标飞行器在飞行中自然发出的环境声信号，结合超宽带距离测量。声学数据使用频率滑动广义互相关（FS-GCC）方法进行处理，并通过我们的分析公式进行增强，该公式可以补偿异步高频采样中的信道间切换延迟。这使得准确的到达时间差（TDOA）估计，即使是紧凑的麦克风阵列。这些TDOA值，以及已知的麦克风几何形状和超宽带数据，被整合到我们的几何模型中，以估计目标MAV的方位。我们在一个受控的室内大厅中通过两种实验场景验证了我们的方法：静态方位估计，目标MAV在预定义的角度位置（0°，±30°，±45°，±60°）悬停，以及动态方位估计，它以不同的速度飞过角度。结果表明，与经典和机器学习基线相比，我们的方法产生了可靠的TDOA测量值，并且在静态和动态设置下都产生了准确的轴承估计。这证明了我们的低成本声学增强解决方案在MAV群中进行局部方位估计的可行性，支持在gps拒绝或视觉退化的环境中改进的相对导航和分散感知。

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引用次数: 0