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28 February 2026, Volume 31 Issue 1 Previous Issue   
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Intelligent Robots


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Cover and Table of Contents 2026, 31 (1):  0.  Abstract ( 0 )   PDF (45044KB) ( 0 )   Related Articles | Metrics

Intelligent Robots

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Development of Surgical Robot for CT-Guided Lung Biopsy Zhang Han, Zhang Guoliang, Feng Shengjie, Li Qingyun, Qu Jieming, Xie Le 2026, 31 (1):  1-11.  doi: 10.1007/s12204-025-2846-0 Abstract ( 0 )   PDF (1960KB) ( 0 )   Traditional lung biopsy procedures are complicated and time-consuming due to the lack of realtime imaging guidance, requiring physicians to frequently move between the operating room and computerized tomography (CT) imaging equipment. Robotics has been widely applied in medical surgeries, yet meeting the requirements for lung biopsy procedures with assured accuracy and safety remains a topic of research. This paper introduces a surgical robot for CT-guided lung biopsy. A kinematic analysis of the robot mechanism is conducted, and a master-slave control system tailored for this robot is developed. A force feedback algorithm is proposed to ensure the reliability and realism of the surgical process. Finally, the system’s feasibility is verified by the mechanism positioning accuracy experiment and the targeting accuracy experiment, and in vivo animal experiment is conducted to lay the foundation for clinical application. Related Articles | Metrics

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Graph Convolution Network with EEG-EMG Fusion for Upper Limb Motion Intention Recognition Zheng Luzhou, Zhao Changchen, Zhang Chao, Cheng Shichao, Zhang Jianhai 2026, 31 (1):  12-23.  doi: 10.1007/s12204-025-2856-y Abstract ( 3 )   PDF (877KB) ( 0 )   With the continuous advancement of sensors and algorithms, an increasing number of deep learning methods have been applied to fine-grained upper limb motion intention recognition using multimodal physiological signals. However, effectively and quantifiably integrating correlations between electroencephalogram (EEG) and electromyogram (EMG) signal channels as well as within EEG signal channels as a clue to improve performance remained challenging. In this paper, we proposed a novel framework that achieved accurate prediction of upper limb motion intentions via fusing EEG and EMG signals. Firstly, the raw input signals were fed into the feature extraction module, respectively, enabling feature decomposition in the channel dimension. Secondly, the graph convolution module with learnable edge weights was proposed to adaptively learn correlations between different modalities. Thirdly, we designed a self-attention graph pooling module that employed the self-attention mechanism to compute the attention score for each node as the basis for pooling. Compared with calculation methods using the mean or maximum value, this approach was more likely to retain nodes with stronger correlations to motor intentions. Finally, the prediction results were obtained through a classifier. We validated the effectiveness of our method on a publicly available multimodal upper limb dataset, achieving an accuracy of 93.17%. Related Articles | Metrics

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Leader-Follower Control Algorithm for Minimally Invasive Surgical Robot Li Mengwen, Lv Penghao, Liu Qiao, Dai Yu, Zhang Jianxun 2026, 31 (1):  24-35.  doi: 10.1007/s12204-025-2796-6 Abstract ( 3 )   PDF (1999KB) ( 0 )   A multi-degree-of-freedom heterogeneous remote operating system is built to improve the smoothness of minimally invasive surgical robot trajectories. In order to solve the shaking problem of follower arm caused by low leader-follower mapping frequency, a timed sampling polynomial interpolation method is proposed. The leaderfollower consistent motion mapping under the geodetic coordinate system is established by using the homogeneous transformation matrices. The leader-follower motion control algorithm based on position and pose separation is designed, which includes relative motion control and absolute pose control algorithms. Furthermore, the remapping auxiliary function for leader-follower control is added, allowing for motion control of heterogeneous leader-follower system in different work spaces. Finally, multiple sets of experiments are conducted to validate the effectiveness of the aforementioned algorithms. The stability of the system is demonstrated through experiments involving collar and collar-slide setups. Related Articles | Metrics

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Input Mapping-Based Model Predictive Control with Event-Triggered Adaptive Strategy for Rigid-Soft Hybrid Manipulator Li Guolin，Chen Tong，He Shaoying，Yin Debin 2026, 31 (1):  36-47.  doi: 10.1007/s12204-026-2902-4 Abstract ( 3 )   PDF (1769KB) ( 0 )   Uncertain loads of the rigid-soft hybrid manipulator directly affect working configurations, which will alter the system model parameters, and thereby degrade control accuracy and efficiency. This paper introduces an event-triggered adaptive model predictive control strategy, which integrates with a data-driven approach to control hybrid robots with a cable-driven soft component. In the presence of model uncertainty and mismatch, adaptive identification is employed to improve the nominal model within the controller. Meanwhile, an event-triggered scheme is utilized to reduce redundant identification frequency and improve computing efficiency. Furthermore, an online data-driven method, called input mapping, uses the relationship between the historical input and output data to compensate for the minor model error in the controller via linear combination. The optimization problem is efficiently solved by designing the attenuation coefficient in an infinite-domain situation. Comparative simulation and experimental results demonstrate that the proposed method achieves improved accuracy and faster convergence speed. Related Articles | Metrics

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Acceleration Optimization-Based Speed Planning Method for High-Precision Longitudinal Control of Wheeled Robots Wang Longshenga，Yuan Weib，Zhuang Hanyangc，Wang Chunxianga，Yang Minga 2026, 31 (1):  48-58.  doi: 10.1007/s12204-025-2836-2 Abstract ( 1 )   PDF (1218KB) ( 0 )   In recent years, wheeled robots have been widely used in the field of logistics automation. In realworld application, the inertia of wheeled robots is not fully considered in traditional speed planning methods, and the longitudinal error of wheeled robots reaching the target area is too large to accurately complete subsequent operations, especially for large-loaded wheeled robots like autonomous forklifts. In order to deal with the above problem, this paper proposes an acceleration-awarded speed planning method based on acceleration optimization aimed at making wheeled robots reach the target area smoothly and accurately. This method first introduces acceleration information into speed planning based on dynamic constraints, and then models speed planning as an optimization problem to smooth speed changes. Experimental verification shows that the longitudinal error of wheeled robots using this method is significantly reduced, and the smoothness of speed is improved. Related Articles | Metrics

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Misaligned Parallel-Chamber Soft Pneumatic Network Actuator for Multi-Mode Gripping Zhang Dong，Liu Sheng，Shi Mengyao，Cai Yu，Wang Dazhong 2026, 31 (1):  59-70.  doi: 10.1007/s12204-025-2865-x Abstract ( 2 )   PDF (5755KB) ( 0 )   Grasping is a fundamental way that creatures interact with their environments and is also a key field of interest in soft robotics. Pneumatic network actuators are particularly promising candidates for the soft robotics community. However, most soft pneumatic grippers based on pneumatic network actuators can only realize a single grasping mode, with poor adaptability and dexterity in grasping objects. Therefore, a new misaligned parallelchamber soft pneumatic actuator was presented, which mainly consists of soft chambers embedded in an elastomer structure. Then, theoretical models describing the bending deformation and three-dimensional trajectory curve of the proposed actuator were developed using the segmental constant curvature assumption and incorporating the Yeoh model. In addition, finite element simulations of the bending deformation were performed and verified experimentally. Finally, three soft grippers with different gripping modes, namely, winding, enveloping, and pinching, were designed, and gripping experiments were conducted. The results show that the designed soft gripper demonstrates good adaptability and flexibility to the target object, expanding the application range of pneumatic soft grippers in the field of picking objects. The proposed soft actuator provides a potential method for the design of a multifunctional soft gripper. Related Articles | Metrics

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Planning and Control for Robot-Assisted Feeding System Towards the Disabled Dai Feifan，Pei Zijun，Wang Pu，Chen Weidong 2026, 31 (1):  71-81.  doi: 10.1007/s12204-024-2779-z Abstract ( 1 )   PDF (3596KB) ( 0 )   Eating is an essential activity for the disabled with upper limb impairments, and therefore numerous feeding robots are born. However, safety and reliability are two basic elements to build trust in assistive robots. Thus, planning and control methods for a safe and reliable robot-assisted feeding system are developed. Firstly, the feeding task is expanded to include both pre-meal preparation and eating. The feeding task is then divided into five subtasks including door opening, bowl grasping and transferring, utensil fetching, food skewering, and food transferring. Meanwhile, the system is built from five levels, i.e., user interface, task planning, motion planning, control, and perception. Secondly, the feeding task is decomposed into a series of motion primitives based on a motion-centric taxonomy. Then a set of states utilizing those primitives is constructed and then a finite state machine is employed as the task manager which can regulate the workflow during the feeding task. Thirdly, a safety-oriented motion planner, a food item selector, an admittance controller, and a collision detector are depicted. Finally, experiments in the laboratory and further in a rehabilitation hospital with stroke patients are conducted. The experimental results indicate that the system is safe and reliable. Related Articles | Metrics

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Hybrid Learning Model for Cross-Device Fault Detection of Industrial Robot Joints Xiao Lei，Zhao Hailong，Wu Xun，Wang Jun，Zhou Qihong 2026, 31 (1):  82-98.  doi: 10.1007/s12204-025-2843-3 Abstract ( 0 )   PDF (1207KB) ( 0 )   Industrial robots, widely employed to boost production efficiency, encounter escalating risks of joint faults as their service time lengthens. However, end-effector motion anomalies may stem from faults in the endeffector itself or from motion propagation in other joints. Moreover, the scarcity of fault samples for detection poses significant challenges. Install extra accelerometers for more precise fault diagnosis might increase the system’s complexity and costs. To tackle these challenges, this study leverages the ease of data acquisition to analyze current data from multi-joint industrial robots. A hybrid learning method is proposed for cross-device fault detection to identify the defective joint. This method integrates features from deep networks and spectral analysis to harness knowledge from both other robots and the target robot. An unsupervised model is used to assess the status of the joints based on the fused features. The proposed method’s effectiveness is validated through ablation studies and method comparisons. Results demonstrate that it accurately detects the abnormal joints without misjudgment. Related Articles | Metrics

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Collision Detection for Vacuum Wafer Transfer Robot Fang Xingyu，Wei Xianming，Sun Jintao，Xu Linsen 2026, 31 (1):  99-105.  doi: 10.1007/s12204-026-2899-8 Abstract ( 1 )   PDF (1711KB) ( 0 )   In response to safety concerns caused by wafer transmission robot collisions in confined vacuum chambers, which can lead to wafer breakage and production line contamination, a collision detection and response method is proposed, leveraging the robot’s dynamics model and adjustable acceleration thresholds. The structure and motion characteristics of the robot were first analyzed. Subsequently, its kinematic and dynamic models were established and validated via simulation. To reduce noise interference, the dynamics model was computed using a reference trajectory from the host planner. Cross-correlation analysis was used to identify phase differences between the planned and encoder feedback trajectories, enabling phase compensation. For collision threshold settings, an acceleration-based adjustment scheme was developed, taking into account potential collision risk levels. Experimental tests on the vacuum robot verified the effectiveness of the proposed method. Related Articles | Metrics

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Design of Composite Two-Channel Disturbance Estimation Adaptive Controller for Backlash-Like Hysteretic Nonlinear Systems Liu Qunpo，Li Jiakun，Fei Shumin，Bo Xuhui，Naohiko Hanajima 2026, 31 (1):  106-116.  doi: 10.1007/s12204-025-2827-3 Abstract ( 0 )   PDF (621KB) ( 0 )   For nonlinear systems with backlash-like hysteresis characteristics and external disturbance, a composite two-channel disturbance estimation adaptive controller is proposed to improve the trajectory tracking accuracy of the system. The unmodeled hysteresis and external disturbances are treated as lumped uncertainties, which are approximated by radial basis neural network and disturbance estimator respectively. These approximations are then linearly fused to form the compensation term for the lumped uncertainty. The second order linear filter is employed to estimate multiple differential terms, which are integrated into the controller design and dynamic system state updates, thereby reducing computational complexity. A weighted fusion mechanism is implemented for the two channels, and the adaptive update rate for each channel is determined based on the deviation between the lumped uncertainty reference value and the output of each channel. To address the challenges posed by the discontinuity of deviation and maintain system stability, the first-order low-pass filter is applied to smooth the deviation, enhancing system robustness. A trajectory tracking simulation of a single-input single-output nonlinear system is conducted to compare the performance of the proposed controller with baseline controllers, demonstrating the effectiveness of the composite two-channel disturbance estimation adaptive controller. Related Articles | Metrics

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SDA-Loc: A Semantic-Driven Alignment Algorithm for Cross-Modal Localization in Point Cloud Maps Ceng Yuxuan，Zhao Wentao，Chen Yongtao，Xiao Peng，Wang Jingchuan，Guo Rui 2026, 31 (1):  117-129.  doi: 10.1007/s12204-025-2841-5 Abstract ( 1 )   PDF (1852KB) ( 0 )   Cross-modal localization, utilizing only cameras and prior light detection and ranging (LiDAR) point cloud maps, achieves high localization accuracy at a low cost. The integration of semantic information can significantly enhance the accuracy at the cost of heavy computational load on optimization and huge semantic annotation on LiDAR point cloud maps. In this paper, we propose the SDA-Loc, a semantic cross-modal localization system that solely relies on visual semantic information, making our approach more streamlined compared to existing methods. We design a semantic-driven alignment algorithm that leverages visual semantic labels to perform different types of iterative closest point, allowing the system to better exploit the structural information represented by object semantics, thereby achieving accurate localization without the additional burden of point cloud annotation. Coupled with a designed dynamic error rejection mechanism, our approach effectively achieves a balance between accuracy and speed. The experiments conducted on the KITTI dataset demonstrate the competitive localization performance of our approach. Moreover, the experiment on outdoor campus dataset confirms that the proposed system can effectively mitigate the drift in visual localization under challenging lighting conditions, and proves the robustness of SDA-Loc when using poor LiDAR point cloud maps. The runtime analysis also shows that SDA-Loc strikes an excellent balance between localization accuracy and computational efficiency. Related Articles | Metrics

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Hybrid Topological Map Fusion Based on Memory Sphere Peng Chengyu，Chen Baifan，Li Siyu，Jin Yuxuan，Wan Jiadong，Fu Yuesi 2026, 31 (1):  130-142.  doi: 10.1007/s12204-025-2824-6 Abstract ( 0 )   PDF (2631KB) ( 0 )   A topological map with the spatial relationship is an inescapable object in the research of map fusion, as it is a priori knowledge for planning path. However, there are some difficulties in topological map fusion in a dynamic environment. Therefore, this paper proposes a fusion method for the hybrid topological map based on the memory sphere. A hybrid topological map is composed of occupancy grid maps and the topological structure. The hybrid map fusion can rely on rich features in occupancy grid maps. By analyzing the process of recalling scene, a memory sphere is designed to store the features and the semantic label extracted from occupancy grid maps. Then the core is the matching of the memory sphere, which is divided into two parts, fast retrieval and fine matching. We verify the effectiveness of our method in simulation and real environments, demonstrating that our method has a great performance in the dynamic environment. Related Articles | Metrics

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ListPose: Lightweight and Implicit Spatial-Temporal Modeling with TokenPose for Video-Based Pose Estimation Wu Zhiyang，Zhang Zhicheng，Dang Yonghao，Yin Jianqin，Tang Jin 2026, 31 (1):  143-153.  doi: 10.1007/s12204-025-2815-7 Abstract ( 1 )   PDF (919KB) ( 0 )   Video pose estimation has gained significant attention in the field of deep learning. Compared to traditional image-based pose estimation methods, video pose estimation leverages inter-frame relationships and temporal cues to provide more accurate and robust results. However, handling pose estimation in video still faces challenges in terms of modeling frames’ dependency and considering real-world applications’ latency. To address these issues, we propose a lightweight video pose estimation model based on the Transformer architecture. First, we discard the heavy pose-initialization module and retain only a lightweight frame encoder to simplify the model. Second, we introduce a novel residual token initialization module to model frame dependencies and implicitly capture the spatial-temporal correlations between adjacent frames. Additionally, we employ TokenPose as the feature extractor, which leverages self-attention mechanisms to implicitly model the spatial relationships between keypoints and effectively reduces model parameters and computational complexity. We evaluate our method on the Penn Action dataset and Sub-JHMDB dataset, two commonly used benchmarks for video pose estimation. The results demonstrate that our approach achieves comparable performance while significantly reducing the number of model parameters and computational complexity. Related Articles | Metrics

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BEV-Fused Imitation and Reinforcement Learning for Autonomous Driving Planning Xia Jie，Wu Xiaodong，Xu Min 2026, 31 (1):  154-166.  doi: 10.1007/s12204-025-2851-3 Abstract ( 0 )   PDF (919KB) ( 0 )   End-to-end autonomous driving technology breaks the constraints of traditional modular pipeline approaches by integrating perception, prediction, and planning within a single framework, achieving global optimization. Current end-to-end frameworks typically rely on deep learning planning, which requires extensive offline data for training. Deep reinforcement learning (DRL) algorithms are also popular, as they allow agents to adapt to environmental changes through reward functions. However, these frameworks cannot implement backpropagation with the perception module. Each approach has its strengths and weaknesses. This paper combines both frameworks by developing a bird’s eye view (BEV) feature extraction network to capture key traffic flow information, creating an end-to-end DRL planning framework based on BEV features. This shift transforms the technology from data-driven to behavior-driven. To improve training speed and quality, we propose an advanced imitation learning algorithm, validated through simulations in the CARLA simulator. Experimental results show that our approach outperforms other frameworks, enhancing the agent’s safety and efficiency. Related Articles | Metrics

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Haptic-Aided Navigation Vehicle: Enhancing Obstacle Detection in Blind Spots and Transparent Object Scenarios Li Mingwang，Li Xinde，Zhang Zhentong，Wang Zeyu，Zhao Haoming 2026, 31 (1):  167-175.  doi: 10.1007/s12204-025-2807-7 Abstract ( 2 )   PDF (1218KB) ( 0 )   As autonomous mobile robots are increasingly deployed in complex environments, traditional vision sensors and LiDAR encounter considerable limitations, particularly in detecting obstacles in blind spots or transparent objects. To address the issue of blind spots, we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle (HANV). This system integrates haptic sensors and LiDAR to deliver comprehensive perception, significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors. To tackle the challenge of transparent obstacles, which are often undetected by both vision and LiDAR sensors, we employ a fusion of haptic sensors and LiDAR. The haptic system provides physical contact feedback, ensuring reliable detection of transparent obstacles such as glass, while LiDAR offers long-range sensing capabilities. This combination enables HANV to navigate effectively in environments with transparent obstacles, overcoming the limitations of traditional sensing systems. Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles. HANV achieves high success rates, minimal collisions, and efficient obstacle avoidance, particularly excelling in complex, confined spaces where conventional systems prove inadequate. These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments. Related Articles | Metrics

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Vision-Based Detection for Aerial Intruders in Airport Flight Areas Niu Guochen，Lü Zhihao 2026, 31 (1):  176-186.  doi: 10.1007/s12204-026-2900-6 Abstract ( 2 )   PDF (1776KB) ( 0 )   To address the technical challenge of achieving real-time and accurate detection of aerial intruders such as birds and drones in airport flight areas, where targets are extremely small, have complex and variable trajectories, suffer from strong background noise, and require long-distance detection, a tri-module fusion airspace detection network (ACE-AirDETR) based on the real-time detection Transformer (RT-DETR) framework is proposed in this paper. Performance is enhanced through three core modules. The cross-scale edge information enhancement module strengthens target contour details, generates highly discriminative features, and significantly alleviates the decline in detection accuracy caused by motion blur of small targets. The efficient additive attention module optimizes computational efficiency and improve the model’s real-time performance and deployability. The context-guided spatial feature reconstruction feature pyramid network module enhances the feature expression capability of small targets under complex backgrounds and effectively reduces the false detection and missed detection rates. To verify the effectiveness of the proposed method in specific scenarios, a self-built airplane-birddrone dataset for airspace intruders in airport-like environments is constructed. Experimental results show that compared with the RT-DETR algorithm, ACE-AirDETR improves the AP50 and AP50:95 metrics by 3.2 and 1.5 percentage points respectively, increases the frame rate by 11.8%, and reduces the computational complexity and parameter count by 20.7% and 27.3% respectively, achieving a coordinated optimization of detection accuracy, speed, and model lightweight. Related Articles | Metrics

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Cooperative Pursuit of Unmanned Surface Vehicles Using Multi-Agent Reinforcement Learning Qu Xingru，Li Chu，Jiang Yuze，Long Feifei，Zhang Rubo 2026, 31 (1):  187-194.  doi: 10.1007/s12204-025-2816-6 Abstract ( 0 )   PDF (1169KB) ( 0 )   This paper is concerned with the cooperative pursuit of unmanned surface vehicles (USVs) against the dynamic escaping target using multi-agent reinforcement learning. The Markov game process is established for pursuit-evasion, and the success criteria for cooperative capture of USVs are given by using distance and angle constraints. By virtue of the centralized training and decentralized execution framework as well as the long short-term memory network, cooperative pursuit training is conducted using the multi-agent soft actor-critic reinforcement learning, which can optimize capture performance of USVs against the escaping target. Besides, to avoid the occurrence of lazy capturer and increase the capture success rate, a multi-stage reward guidance method is developed, where the training process can be optimized according to the current states of both sides, effectively guiding vehicle to achieve the capture task from easy to difficult. Simulations are provided to illustrate the effectiveness of the proposed reinforcement learning method for cooperative pursuit of USVs. Related Articles | Metrics

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Optimization of Three-Degree-of-Freedom Biomimetic Pectoral Fin Propulsion Law Li Bin，Li Zonggang，Li Haoyu，Du Yajiang 2026, 31 (1):  195-208.  doi: 10.1007/s12204-024-2579-5 Abstract ( 0 )   PDF (3692KB) ( 0 )   To optimize the movement of the three-degree-of-freedom (3-DOF) pectoral fins, a 3-DOF model of the dolphin-like pectoral fins was established, and the effects of different parameters of the pectoral fins on their propelling performance were simulated using computational fluid dynamics (CFD) technology. Using CFD simulation data as a training set and a multi-layer perceptron (MLP) neural network as a prediction model, the average thrust and lift of the pectoral fin motion under different motion cycles, rowing amplitudes, flapping amplitudes, and feathering amplitudes were predicted and modeled. A multi-objective genetic algorithm was used to obtain the optimal parameter values for maximum thrust and minimum absolute lift, and the optimal motion law for 3-DOF motion was brought. The results showed that the optimal propulsion performance was achieved at a period of 1 s, a rowing amplitude of 36 ◦ , a flapping amplitude of 18 ◦ , and a feathering amplitude of 56 ◦ . Finally, the force and displacement of the robotic fish were collected through indoor pool experiments and compared with the simulation results, indicating that the simulation results are of considerable reliability. The research results have specific guiding significance for the design of the pectoral fins of biomimetic robotic fish. Related Articles | Metrics

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Synthetic Data-Driven Multi-Task Framework for UAV Detection and Classification Zhang Jingkai，Li Xinde，Wei Wangzichao，Wang Ziyao，Ma Ke 2026, 31 (1):  209-220.  doi: 10.1007/s12204-026-2903-3 Abstract ( 1 )   PDF (1626KB) ( 0 )   With the rapid development of unmanned aerial vehicle (UAV) technology, there is an increasingly urgent demand for intelligent detection, identification, and performance parameter inference techniques. However, existing UAV datasets face severe challenges including high acquisition costs, labor-intensive annotation, and data scarcity, and lack methods for directly predicting functional performance from single images. This paper proposes a UAV analysis framework based on synthetic data generation and multi-task deep learning. We construct an adaptive dataset generation system based on Unreal Engine 5, incorporating UAV size classification, adaptive distance adjustment algorithms, and enhanced 3D-to-2D coordinate transformation techniques for automated high-quality synthetic data generation. We design a multi-task collaborative learning network integrating visual information, distance information, and uncertainty quantification modules, supporting UAV detection, parameter prediction, functional classification, and size prediction. Experimental results demonstrate high similarity between synthetic and real data (Fr´echet inception distance is 21.05), with parameter prediction achieving mean absolute percentage errors of 29.1% and 27.0% for maximum speed and altitude respectively, and military-civilian classification accuracy reaching 62.5%. This method provides a low-cost, efficient solution for intelligent UAV analysis with considerable practical value. Related Articles | Metrics

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Review: Development of Micro-Scale Planetary Surface Exploration Robots Dong Kaijie，Li Ziqi，Gao Mingxing，Zhang Jianhua，Li Duanling 2026, 31 (1):  221-240.  doi: 10.1007/s12204-025-2857-x Abstract ( 0 )   PDF (3892KB) ( 0 )   As humanity’s exploration of the universe continues to advance, micro-scale planetary surface exploration robots have emerged as indispensable tools for space science. This paper provides a comprehensive review of the development of exploration robots both domestically and internationally. The key structural characteristics and applications of various representative robots are systematically analyzed, with a focus on their technological features and performance. Additionally, the paper highlights recent research advancements in novel exploration robots, addressing innovations in design and functionality. Finally, the future development trends and prospects of micro-scale planetary surface exploration robots are discussed, underscoring their potential to push space exploration to new frontiers. This research plays a significant role in promoting the development of China’s space industry. Related Articles | Metrics