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Characteristic model-based control of aerial manipulator for improved interaction stability

Science China(Technological Sciences) 2025年第4期68卷 166-178页

作者： Bingkai XIU Zhan LI Yipeng YANG Huijun GAO Research Institute of Intelligent Control and Systems Harbin Institute of TechnologyHarbin150001China State Key Laboratory of Robotics and Systems(HIT) Harbin Institute of TechnologyHarbin150001China

Maintaining contact stability is crucial when the aerial manipulator interacts with the surrounding environment. In this paper, a novel output feedback framework based on a characteristic model is proposed to improve the contact stability of the aerial manipulator. First, only position measurements of the aerial manipulator are introduced to design the practical finite-time command filter-based force observer. Second, an attitude control architecture including characteristic modeling and controller design is presented. In the modeling part, input-output data is utilized to build the characteristic model with fewer parameters and a simpler structure than the traditional dynamic model. Different from conventional control methods, fewer feedback values,namely only angle information, are required for designing the controller in the controller part. In addition, the convergence of force estimation and the stability of the attitude control system are proved by the Lyapunov analysis. Numerical simulation comparisons are conducted to validate the effectiveness of the attitude controller and force observer. The comparative results demonstrate that the tracking error of x and θ channels decreases at least 10.62% and 10.53% under disturbances and the force estimation precision increases at least 45.19% in the different environmental stiffness. Finally, physical flight experiments are conducted to validate the effectiveness of the proposed framework by a self-built aerial manipulator platform.

关键词： aerial manipulator interaction stability contact force observer characteristic model

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Person Segmentation and Action Classification for Multi-Channel Hemisphere Field of View LiDAR Sensors

Person Segmentation and Action Classification for Multi-Chan...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Seliunina, Svetlana Otelepko, Artem Memmesheimer, Raphael Behnke, Sven University of Bonn Autonomous Intelligent Systems Group Computer Science Institute VI - Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics Germany

ISBN: (纸本)9798331531614

Robots need to perceive persons in their surroundings for safety and to interact with them. In this paper, we present a person segmentation and action classification approach that operates on 3D scans of hemisphere field of view LiDAR sensors. We recorded a data set with an Ouster OSDome-64 sensor consisting of scenes where persons perform three different actions and annotated it. We propose a method based on a MaskDINO model to detect and segment persons and to recognize their actions from combined spherical projected multi-channel representations of the LiDAR data with an additional positional encoding. Our approach demonstrates good performance for the person segmentation task and further performs well for the estimation of the person action states walking, waving, and sitting. An ablation study provides insights about the individual channel contributions for the person segmentation task. The trained models, code and dataset are made publicly available. © 2025 IEEE.

关键词： Signal encoding

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A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service robotics 23

A Comparison of Prompt Engineering Techniques for Task Plann...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Bode, Jonas Pätzold, Bastian Memmesheimer, Raphael Behnke, Sven Computer Science Institute Vi - Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Autonomous Intelligent Systems group Germany

ISBN: (纸本)9798350373578

Recent advances in Large Language Models (LLMs) have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason across a wide range of tasks and scenarios. Previous works have investigated various prompt engineering techniques for improving the performance of LLMs to accomplish tasks, while others have proposed methods that utilize LLMs to plan and execute tasks based on the available functionalities of a given robot platform. In this work, we consider both lines of research by comparing prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models. We make our code, including all prompts, available at https://***/AIS-Bonn/Prompt-Engineering. © 2024 IEEE.

关键词： Human robot interaction

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Multi-Objective Path Planning for Parallel Parking Based on NSGA-II Algorithm 2

Multi-Objective Path Planning for Parallel Parking Based on ...

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2nd International Conference on Mechatronics and Automation Technology, ICMAT 2023

作者： Tong, Fengshuo Zhang, Zheng Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology China

ISBN: (纸本)9781643684826

In order to improve the safety, parking efficiency and smoothness of the parallel parking of driverless cars, for the parking process needs to meet the path curve curvature is small, parking distance is short, the lane occupation is less, through the analysis of the characteristics of the parallel parking working conditions, put forward a NSGA-II algorithm based on the parking starting point of multi-objective optimization method. Firstly, based on the analysis of vehicle kinematics model, the quintic polynomial curve is used to construct the parking path, and the analytical function between the parking start point and various indexes is established;then, the NSGA-II algorithm is used to optimize the constructed multi-objective model, so as to obtain the optimal optional set of the parking start point;finally, the results of the simulation experiments show that: in order to satisfy the various objectives of the synergistic optimization as well as constraints, the proposed method of the design of the parking start point can achieve the parking path curve of less curvature and shorter parking distance. © 2024 The Authors.

关键词： Multiobjective optimization

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SMUG Planner: A Safe Multi-Goal Planner for Mobile Robots in Challenging Environments

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IEEE robotics and Automation Letters 2023年第11期8卷 7170-7177页

作者： Chen, Changan Frey, Jonas Arm, Philip Hutter, Marco Robotics Systems Lab ETH Zurich Zurich8092 Switzerland Max Planck Institute for Intelligent Systems Stuttgart70569 Germany

Robotic exploration or monitoring missions require mobile robots to autonomously and safely navigate between multiple target locations in potentially challenging environments. Currently, this type of multi-goal mission often relies on humans designing a set of actions for the robot to follow in the form of a path or waypoints. In this letter, we consider the multi-goal problem of visiting a set of pre-defined targets, each of which could be visited from multiple potential locations. To increase autonomy in these missions, we propose a safe multi-goal (SMUG) planner that generates an optimal motion path to visit those targets. To increase safety and efficiency, we propose a hierarchical state validity checking scheme, which leverages robot-specific traversability learned in simulation. We use LazyPRM∗ with an informed sampler to accelerate collision-free path generation. Our iterative dynamic programming algorithm enables the planner to generate a path visiting more than ten targets within seconds. Moreover, the proposed hierarchical state validity checking scheme reduces the planning time by 30% compared to pure volumetric collision checking and increases safety by avoiding high-risk regions. We deploy the SMUG planner on the quadruped robot ANYmal and show its capability to guide the robot in multi-goal missions fully autonomously on rough terrain. © 2023 IEEE.

关键词： Motion planning

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SDSLP-YOLO: An end-to-end model for simultaneously detecting strawberry and locating picking point

SDSLP-YOLO: An end-to-end model for simultaneously detecting...

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2024 China Automation Congress, CAC 2024

作者： Du, Xueyi Huang, Wenhui Min, Huasong Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350368604

Accurately identifying strawberries and locating picking points are prerequisites for harvesting robots. To address the issues of multiple detections, missed detections, and false detections, as well as inaccurate picking point localization caused by large differences in fruit shape, complex backgrounds, and occlusions during the picking process, this paper proposes a method based on the YOLOv8-pose model. First, a dual-path feature extraction backbone network (LGFBNet) that combines convolution and Transformer is proposed. This module enhances the richness and expressiveness of features by coupling global and local features, effectively solving the issue of multiple detections caused by large differences in fruit shape. Secondly, CBAM is introduced into the neck of the network, allowing the network to focus more on the distinguishing features between the strawberries and the background, effectively addressing the missed and false detections caused by complex backgrounds. Finally, the NWD-CIoU weighted loss function is introduced, where the scale-balancing property of NWD compensates for the sensitivity of CIoU to positional shifts, reducing the additional missed detection rate caused by severe occlusions. Experimental results show that the proposed method achieves an average precision (mAP-obj) of 88.3% for strawberry detection and an average precision (mAP-kp) of 74.2% for keypoint detection, which are 1.7% and 3% higher than YOLOv8-pose, respectively. In summary, this method has significant theoretical and practical implications for tasks such as strawberry recognition and picking point localization in complex environments. © 2024 IEEE.

关键词： key point detection picking point positioning strawberry identification yolov8-pose

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YOLOv5-LF: An optimized YOLOv5 algorithm for real-time automatic tracking

YOLOv5-LF: An optimized YOLOv5 algorithm for real-time autom...

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2024 China Automation Congress, CAC 2024

作者： Luo, Jicheng Cheng, Lei Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350368604

Recent research has concentrated on enhancing robot visual tracking in dynamic settings. To boost real-time tracking capabilities, we introduce an optimized robot visual tracking control system that employs an advanced lightweight YOLOv5 algorithm. This system integrates the LCNet-a lightweight CPU-based Convolutional Neural Network-and a PConv-driven FasterNet module, both of which significantly enhance the detection of object features. Our experimental setup, which uses an industrial camera to acquire target pose data, demonstrates that after implementing the optimized model, the system efficiently detects and tracks moving objects in real-time from video streams. The gimbal’s positioning is dynamically adjusted in response to the moving target’s image displacement. The YOLOv5-LF network model, quantified with OpenVINO, has demonstrated a detection accuracy of 95.23% and a recognition rate of 90 frames per second (FPS) on Intel’s NUC. This not only ensures seamless real-time system management but also enhances the precision of object detection. The refined detection algorithm notably outperforms the original network in terms of visual tracking efficacy on our experimental platform. © 2024 IEEE.

关键词： Convolutional neural networks

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DNGF-LS: A Dynamic Non-Ground Points Filtering LiDAR SLAM Based on Sparse 4DCNN

DNGF-LS: A Dynamic Non-Ground Points Filtering LiDAR SLAM Ba...

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2024 China Automation Congress, CAC 2024

作者： Chen, Tingtian Wang, Qi Min, Huasong Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350368604

Achieving precise localization and high-quality static map construction in unfamiliar environments is fundamental for mobile robots. However, in environments like urban roads, dynamic objects such as pedestrians and vehicles can cause LiDAR SLAM odometry drift, leaving ghostings in the constructed map, which can lead to the failure of subsequent tasks such as path planning based on prior maps. Therefore, performing dynamic points filtering on point clouds is crucial for mobile robots, this paper introduces a novel Dynamic Non-Ground points Filtering LiDAR SLAM(DNGF-LS). Firstly, employing a Coarse-to-Fine Concentric Zone Model(C-F-CZM) to represent point clouds and perform ground segmentation addresses the sparsity issue caused by distant points in ground plane fitting, yielding accurate non-ground point clouds. Subsequently, converting the current frame’s previous ten frames of point clouds to 4D point clouds with the same viewpoint, utilizing sparse voxel grids to represent 4D point clouds, and inputting them into a sparse 4D Convolutional Neural Network(CNN) for dynamic points filtering. Finally, retaining static points for pose estimation and mapping using LiDAR Odometry And Mapping(LOAM). Experimental results on highly dynamic datasets demonstrate that DNGF-LS can construct high-quality static maps and achieve superior localization accuracy compared to LIO-SEGMOT and SUMA++. © 2024 IEEE.

关键词： Mapping

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A Fruit Detection Model in Complex Environments Based on Attention Mechanism and Feature Fusion

A Fruit Detection Model in Complex Environments Based on Att...

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2024 China Automation Congress, CAC 2024

作者： Nan, Qian Huang, Wenhui Min, Huasong Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350368604

In complex orchard environments, harvesting robots face significant challenges in identifying fruits accurately due to fruits of various sizes, varying degrees of sunlight intensity, and occlusion. To resolve this issue, we propose a fruit detection model in this paper, which is called AMMFF-YOLO and based on the YOLOv8s architecture. Firstly, a small target detection layer is introduced to enhance the model’s ability to identify small-sized fruits. Secondly, a light-adaptive module is proposed, which increases spatial dimension information without reducing the channel dimension, enriching the model’s perception ability under different sunlight intensities. This module is named C2fCS, which stands for C2f module based on channel and spatial (CS) attention mechanisms. Furthermore, a context information enhancement module (CIEM) is proposed, which employs dilated convolutions and skip connections to fuse feature information from multiple scales, enlarges the receptive field, and improves the model’s ability to recognize target fruits and backgrounds under occlusion. Lastly, the Inner-MPDIoU Loss is used as a bounding box regression loss function to improve the model’s localization ability and detection accuracy. In the detection experiments of strawberries, apples, and citrus in complex orchard environments, the detection accuracy of the proposed model is higher than the detection accuracy of other state-of-art models 0.4% ∼ 8.8%. The experimental results validate the effectiveness of AMMFF-YOLO in fruit detection under complex orchard environments. © 2024 IEEE.

关键词： Orchards

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SESGMS: A Welding Seam Extraction Method Based on Spherical Gaussian Mixture Sampling

SESGMS: A Welding Seam Extraction Method Based on Spherical ...

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2024 China Automation Congress, CAC 2024

作者： Wu, Chuxiong Zhou, Haotian Min, Huasong Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350368604

Accurate seam extraction is crucial for optimizing welding robot performance. Current 3D weld detectors often struggle with geometric approximation challenges and fail to adapt effectively across different weld types, leading to missed or redundant detections that require meticulous threshold adjustments. This paper proposes a weld seam detection method for accurate and robust extraction of weld seam points. Firstly, a neighborhood-weighted spherical projection algorithm is proposed to enhance local feature distinctions within point clouds, improving recognition accuracy of weld seam points. Secondly, a novel Local Reference Frame (LRF) algorithm based on spherical projection is designed to ensure rotational isotropy and facilitate feature comparability across diverse point cloud orientations. In LFR, rotational Spherical Gaussian (SG) closed-form is utilized for hybrid sampling, aiming to achieve efficient, stable, and isotropic 2D transformations of point cloud features. Finally, angle-independent feature vectors are generated by kernel density estimation with a nearest neighbor algorithm to assess symmetry, improving the extraction accuracy of weld. Experimental results on three types of workpieces and seven angular weld models demonstrate that our method excels in adaptive weld seam extraction, particularly in accurately identifying weld seam points. © 2024 IEEE.

关键词： Spheres

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