Biomimetic Intelligence and Robotics

Xiaoming Wang

Hongliu Yu

Søren Kold

Ole Rahbek

Shaoping Bai

Wearable sensors for activity monitoring and motion control: A review

可穿戴传感器用于活动监测和运动控制：综述

In this study, the aerodynamic performance of flapping wings using a parallel motion was investigated and compared with the insect-like "fan-sweep" motion, and the effect of adding a slit to the wings was analyzed. First, numerical simulations were performed to analyze the wing aerodynamics of two flapping motions with equivalent stroke amplitudes over a range of pitching angles based on computational fluid dynamics (CFD). The simulation results indicated that flapping wings with a rapid and short parallel motion achieved better lift and efficiency than those of the fan-sweep motion while maintaining the same aerodynamic characteristics regarding stall delay and leading-edge vortices. For a parallel motion with a pitching angle of 25° and 100 mm stroke amplitude, the wings generated an average lift of 8.4 gf with a lift-to-drag ratio of 1.06, respectively, which were 1.8% and 26% greater than those of the fan-sweep motion with a corresponding 96° stroke amplitude. This situation was reversed when the pitching angle and stroke amplitude were increased to 45° and 144° for the fan-sweep motion, which was equivalent to the parallel motion with a 150 mm stroke amplitude. The slit effect in the parallel motion was also evaluated, and the CFD results indicated that a slit width of 1 mm (1/50 wing chord) increased the lift of the wing by approximately 27% in the case of the 150 mm stroke amplitude. Further, the slit width slightly influenced the lift and aerodynamic efficiency.

Zenggang Zhu

Jingtai Zhao

Yuanyuan He

Shijun Guo

Si Chen

Bing Ji

Aerodynamic analysis of insect-like flapping wings in fan-sweep and parallel motions with the slit effect

昆虫样扑翼在扇形和平行运动中的气动分析及裂隙效应

The fusion of large language models and robotic systems has introduced a transformative paradigm in human–robot interaction, offering unparalleled capabilities in natural language understanding and task execution. This review paper offers a comprehensive analysis of this nascent but rapidly evolving domain, spotlighting the recent advances of Large Language Models (LLMs) in enhancing their structures and performances, particularly in terms of multimodal input handling, high-level reasoning, and plan generation. Moreover, it probes the current methodologies that integrate LLMs into robotic systems for complex task completion, from traditional probabilistic models to the utilization of value functions and metrics for optimal decision-making. Despite these advancements, the paper also reveals the formidable challenges that confront the field, such as contextual understanding, data privacy and ethical considerations. To our best knowledge, this is the first study to comprehensively analyze the advances and considerations of LLMs in Human–Robot Interaction (HRI) based on recent progress, which provides potential avenues for further research.

Ceng Zhang

Junxin Chen

Jiatong Li

Yanhong Peng

Zebing Mao

Large language models for human–robot interaction: A review

用于人机交互的大型语言模型：综述

Shilong Sun

Chiyao Li

Zida Zhao

Haodong Huang

Wenfu Xu

Leveraging large language models for comprehensive locomotion control in humanoid robots design

利用大型语言模型实现人形机器人设计的全面运动控制

Hugo Alcaraz-Herrera

Michail-Antisthenis Tsompanas

Igor Balaz

Andrew Adamatzky

Using neuroevolution for designing soft medical devices

利用神经进化设计软性医疗设备

Tao Zhang

Jiawei Dong

Qianqian Chen

Xiongqian Wu

Shuqi Wang

Yisheng Guan

Review of bioinspired aquatic jumping robots

生物启发式水生跳跃机器人综述

Weinan Chen

Wenzheng Chi

Sehua Ji

Hanjing Ye

Jie Liu

Yunjie Jia

Jiajie Yu

Jiyu Cheng

A survey of autonomous robots and multi-robot navigation: Perception, planning and collaboration

自主机器人与多机器人导航综述：感知、规划与协作

Teng Bin

Hanming Yan

Ning Wang

Milutin N. Nikolić

Jianming Yao

Tianwei Zhang

A survey on the visual perception of humanoid robot

人形机器人视觉感知调查

Chuang-Chuang Li

Xu-Bin Lin

Zhao-Yang Liao

Hong-Min Wu

Zhi-Hao Xu

Xue-Feng Zhou

A fast registration method for multi-view point clouds with low overlap in robotic measurement

一种用于机器人测量中低重叠度多视点云的快速配准方法

Publication Information	Publisher: Elsevier B.V.，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2021，Proportion of original research papers: ，Self Citation Rate:， Gold OA Rate:
Journal Fees	$1800Details
Average review cycle	8 weeks

Biomimetic Intelligence and Robotics

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Wearable sensors for activity monitoring and motion control: A review

Aerodynamic analysis of insect-like flapping wings in fan-sweep and parallel motions with the slit effect

Large language models for human–robot interaction: A review

Leveraging large language models for comprehensive locomotion control in humanoid robots design

Using neuroevolution for designing soft medical devices

Using neuroevolution for designing soft medical devices

Review of bioinspired aquatic jumping robots

A survey of autonomous robots and multi-robot navigation: Perception, planning and collaboration

A survey on the visual perception of humanoid robot

A fast registration method for multi-view point clouds with low overlap in robotic measurement