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sensor fusion and decentralized control in autonomous robotic systems

The proceedings contain 30 papers. The topics discussed include: a collaboration strategy for autonomous, highly specialized agents;an extended Kalman filter for frequent local and infrequent global sensor data fusion;analyzing the multiple-target-agent scenario using optimal assignment algorithms;target specification error optimal camera configuration for vision-based robotics;a vision-guided, semi-autonomous system applied to a robotic coating application;a unique sensor fusion system for coordinate measuring machine tasks;multiple target tracking across reliable multicast wireless networks;grid-based framework for sensorial data integration in mobile robots;a modular planning/control architecture for the semi-autonomous control of telerobots in a hazardous environment;underwater robot society doing internal inspection and leak monitoring of water systems;and dynamic Bayes net approach to multimodal sensor fusion.

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Enhanced Force Estimation and autonomous Force Tuning Through fusion of Tactile sensor and Quasi-Direct Drive Gripper

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IEEE sensorS JOURNAL 2025年第3期25卷 5319-5326页

作者： De Clercq, Thomas Ostyn, Frederik Crevecoeur, Guillaume Univ Ghent Dept Electromech Syst & Met Engn B-9000 Ghent Belgium

robotic manipulation relies on accurate force sensing and control mechanisms to ensure efficient and safe operation. This article presents a novel approach to enhance robotic manipulation capabilities by integrating a barometric tactile sensor with a quasi-direct drive gripper (QDDG). The synergy between these technologies addresses inherent limitations: QDDGs struggle with high current requirements and potential overheating, while tactile sensors often have restricted force ranges and sampling rates. By fusing data from these systems using an adaptive sensor fusion model, this study improves force estimation accuracy, extends force range capability, and boosts sampling rates up to 100 Hz. The integrated approach not only enhances grasping efficiency but also enables autonomous adjustment of grip force levels to prevent slippage. Experimental results demonstrate significant improvements in force estimation accuracy over individual sensor outputs, validating the effectiveness of the proposed integration for advanced robotic manipulation tasks. Furthermore, based on this sensor fusion framework, a slip detection and control mechanism was developed capable of grasping and lifting a diverse array of objects with high success-rates (>90%).

关键词： Force Tactile sensors sensors Robot sensing systems sensor fusion Grippers Robots Accuracy Grasping Gears Multiple-sensor systems sensor data fusion smart sensor-actuators

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Design of a Cost-Effective Ultrasound Force sensor and Force control System for robotic Extra-Body Ultrasound Imaging

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sensorS 2025年第2期25卷 468-468页

作者： Zheng, Yixuan Ning, Hongyuan Rangarajan, Eason Merali, Aban Geale, Adam Lindenroth, Lukas Xu, Zhouyang Wang, Weizhao Kruse, Philipp Morris, Steven Ye, Liang Fu, Xinyi Rhode, Kawal Housden, Richard James Kings Coll London Sch Biomed Engn & Imaging Sci London SE1 7EH England Southern Univ Sci & Technol Sch Med Shenzhen 518055 Peoples R China

Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering more consistent, precise, and faster scans, potentially reducing human error and healthcare costs. Effective force control is crucial in robotic ultrasound scanning to ensure consistent image quality and patient safety. However, existing robotic ultrasound systems rely heavily on expensive commercial force sensors or the integrated sensors of commercial robotic arms, limiting their accessibility. To address these challenges, we developed a cost-effective, lightweight, 3D-printed force sensor and a hybrid position-force control strategy tailored for robotic ultrasound scanning. The system integrates patient-to-robot registration, automated scanning path planning, and multi-sensor data fusion, allowing the robot to autonomously locate the patient, target the region of interest, and maintain optimal contact force during scanning. Validation was conducted using an ultrasound-compatible abdominal aortic aneurysm (AAA) phantom created from patient CT data and healthy volunteer testing. For the volunteer testing, during a 1-min scan, 65% of the forces were within the good image range. Both volunteers reported no discomfort or pain during the whole procedure. These results demonstrate the potential of the system to provide safe, precise, and autonomous robotic ultrasound imaging in real-world conditions.

关键词： robot-assisted ultrasound imaging force control for robotic systems force sensor design medical robotics multi-sensor fusion ultrasound-compatible phantom design

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iKalibr: Unified Targetless Spatiotemporal Calibration for Resilient Integrated Inertial systems

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IEEE TRANSACTIONS ON roboticS 2025年 41卷 1618-1638页

作者： Chen, Shuolong Li, Xingxing Li, Shengyu Zhou, Yuxuan Yang, Xiaoteng Wuhan Univ Sch Geodesy & Geomat SGG Wuhan 430070 Peoples R China

The integrated inertial system, typically integrating an IMU and an exteroceptive sensor, such as radar, light detection and ranging (LiDAR), and camera, has been widely accepted and applied in modern robotic applications for ego-motion estimation, motion control, or autonomous exploration. To improve system accuracy, robustness, and further usability, both multiple and various sensors are generally resiliently integrated, which benefits the system performance regarding failure tolerance, perception capability, and environment compatibility. For such systems, accurate and consistent spatiotemporal calibration is required to maintain a unique spatiotemporal framework for multisensor fusion. Considering that most existing calibration methods first, are generally oriented to specific integrated inertial systems, second, often focus on spatial-only determination, and third, usually require artificial targets, lacking convenience and usability, we propose iKalibr: a unified targetless spatiotemporal calibration framework for resilient integrated inertial systems, which overcomes the above issues, and enables both accurate and consistent calibration. Altogether four commonly employed sensors are supported in iKalibr currently, namely, IMU, radar, LiDAR, and camera. The proposed method starts with a rigorous and efficient dynamic initialization, where all parameters in the estimator would be accurately recovered. Subsequently, several continuous-time batch optimizations are conducted to refine the initialized parameters toward better states. Sufficient real-world experiments were conducted to verify the feasibility and evaluate the calibration performance of iKalibr. The results demonstrate that iKalibr can achieve accurate resilient spatiotemporal calibration.

关键词： Calibration Spatiotemporal phenomena Robot sensing systems Cameras Accuracy Usability Laser radar Vectors Robot vision systems Optimization Continuous-time batch optimization multisensor fusion resilient integration spatiotemporal calibration

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Exact particle flow Daum-Huang filters for mobile robot localization in occupancy grid maps

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COMPLEX & INTELLIGENT systems 2025年第4期11卷 1-15页

作者： Csuzdi, Domonkos Becsi, Tamas Gaspar, Peter Toro, Oliver Budapest Univ Technol & Econ Fac Transportat Engn & Vehicle Engn Dept Control Transportat & Vehicle Syst Muegyetem Rkp 3 H-1111 Budapest Hungary Inst Comp Sci & Control Syst & Control Lab Kende U 13 H-1111 Budapest Hungary

In this paper, we present a novel localization algorithm for mobile robots navigating in complex planar environments, a critical capability for various real-world applications such as autonomous driving, robotic assistance, and industrial automation. Although traditional methods such as particle filters and extended Kalman filters have been widely used, there is still room for assessing the capabilities of modern filtering techniques for this task. Building on a recent localization method that employs a chamfer distance-based observation model, derived from an implicit measurement equation, we explore its potential further by incorporating exact particle flow Daum-Huang filters to achieve superior accuracy. Recent advancements have spotlighted Daum-Huang filters as formidable contenders, outshining both the extended Kalman filters and traditional particle filters in various scenarios. We introduce two new Daum-Huang-based localization algorithms and assess their tracking performance through comprehensive simulations and real-world trials. Our algorithms are benchmarked against various methods, including the widely acclaimed Adaptive Monte-Carlo Localization algorithm. Overall, our algorithm demonstrates superior performance compared to the baseline models in simulations and exhibits competitive performance in the evaluated real-world application.

关键词： Localization Wheeled robot sensor fusion Nonlinear filter

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Adaptive polynomial predictive filter for sensor data estimation and prediction in interference environment

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IET control THEORY AND APPLICATIONS 2025年第1期19卷

作者： Sivaraman, Dileep Pillai, Branesh M. Ongwattanakul, Songpol Suthakorn, Jackrit Mahidol Univ Fac Engn Ctr Biomed & Robot Technol BART Lab Dept Biomed Engn Nakhon Pathom 73170 Thailand

This study introduces an adaptive polynomial predictive filter (APPF) to address the issues of inconsistent and interrupted sensor data in estimation and prediction tasks. The APPF utilizes a polynomial prediction model to estimate time-variant/time-invariant sensor data through polynomial extrapolation. A key feature of the APPF is the autonomous determination of an accurate polynomial order, which reflects the nonlinearity level of the system. This dynamic adjustment allows the APPF to effectively capture the complex dynamics of nonlinear systems without manual tuning, thereby ensuring an accurate sensor data estimation and fusion. This study primarily focuses on validating the feasibility of the APPF method, and the theoretical basis, implementation, and performance evaluation of the APPF are covered, with results from MATLAB simulations and laboratory-based practical experiments comparing the APPF with Kalman filter techniques. Experiments using Hall effect sensors, which provide precise localization by detecting magnetic signals, were conducted to assess the effectiveness of the APPF in addressing the sensor data variances and disturbances commonly encountered in surgical robotic tracking. The findings demonstrate that the APPF significantly enhances estimation and prediction accuracy, highlighting its potential for improving sensor data reliability in various applications.

关键词： control theory Kalman filters polynomial approximation predictive control sensor fusion

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Proceedings of SPIE: sensor fusion and decentralized control in robotic systems

Proceedings of SPIE: Sensor Fusion and Decentralized Control...

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sensor fusion and decentralized control in robotic systems IV

This Volume 3523 of the conference proceedings contains 28 papers. Topics discussed include sensor fusion and decentralized control in robotic systems, multiagents and hierachial architectures, sensor-fused navigation and perception, interfaces for augmented reality, action selection, motion coordination and control and robotic workspace and calibration.

关键词： robotics

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Positioning Algorithm of Cold Source Underwater Pollution Inspection Robot Based on Multi-sensor fusion

Positioning Algorithm of Cold Source Underwater Pollution In...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Jiale Jian Jia Yang Yu Zhang Yinfan Jian China Nuclear Power Engineering Co. Ltd. Beijing Hainan Nuclear Power Co. Ltd. Hainan Province Shanghai Jingyi Intelligent Control Technology Co. Ltd. Shanghai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the rapid development of robotic systems, their application areas have expanded from traditional land to underwater, and have achieved better application range. In order to expand the application scenarios, this study proposes a cold source underwater pollution detection robot positioning algorithm based on multi-sensor fusion. The algorithm aims to solve the shortcomings of existing robots in sensor fusion and positioning accuracy, and provide a reasonable path. First, this study designed a mathematical model of the six-degree-of-freedom motion of the underwater robot based on mathematical modeling. In the optimization part, the fourth-order Runge-Kutta method was used to achieve a stable solution, and it was extended to robot control, and the PID algorithm was used to optimize the motion control framework. Subsequently, the designed model improves the accuracy of robot path planning and ground point cloud removal by introducing a 3D point cloud to 2D grid conversion algorithm, thereby achieving stable positioning. Then, the model proposes a multi-sensor fusion algorithm for underwater acoustic channels, combining multiple sensor data to effectively address the challenges of signal multi-path effects and interference. The experiment is carried out in a simulated underwater environment. The test results showed that the proposed algorithm performed well in terms of positioning accuracy and operation stability. At the same time, a visualization simulation of underwater robot path was carried out to prove the universality of the proposed framework.

关键词： Point cloud compression autonomous underwater vehicles Accuracy Pollution Service robots Inspection Robot sensing systems Mathematical models Stability analysis Robots

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D-DeepOCSORT: multi-object tracking algorithm based on LiDAR and monocular camera

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Optoelectronics Letters 2025年

作者： HE Xiao REN Shuai LIU Chang SHI Lei School of Information Engineering， Chang'an University School of Control of Robotic and Autonomous Systems， University of California Berkeley

Multi-object tracking is a fundamental problem in computer vision， and DeepOCSORT， due to its reliance on the 2D image position information of targets， tends to exhibit prediction drift. To enhance the tracking stability of DeepOCSORT， this paper proposes a novel multi-sensor data fusion-based multi-object tracking method. Specifically， we build upon the DeepOCSORT foundation and additionally integrate target velocity information directly measured by LiDAR. The introduction of this velocity information is conducted from three perspectives: firstly， during data association， a penalty term is constructed based on the differences in target velocities to constrain generating matches with consistent velocities; secondly， using LiDAR velocity for initialization and online updating of the velocity state within the tracker， making tracking predictions more stable; thirdly， controlling the degree of dependence on velocity information by adjusting the process noise covariance matrix. Evaluation results on the KITTI dataset demonstrate that compared to the original DeepOCSORT， the proposed improved multi-source heterogeneous information fusion method significantly enhances tracking performance， with maximum improvements of 3.35， 3.26， and 3.71 on the HOTA， MOTA， and IDF1 metrics， respectively. This study provides an effective approach to building a more stable and accurate multi-object tracking system.

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sensor fusion and decentralized control in robotic systems IV : 28-29 October, 2001, Newton, [Mas...

Sensor fusion and decentralized control in robotic systems I...

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2001年

作者： Gerard T. Mckee Paul S. Schenker chairs/editors sponsored ... by SPIE--the International Soci...

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