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Adaptive Artificial Potential Fields with Orientation Control Applied to Robotic Manipulators

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IFAC-PapersOnLine 2020年第2期53卷 9924-9929页

作者： Caio Cristiano Barros Viturino Ubiratan de Melo Pinto Junior André Gustavo Scolari Conceição Leizer Schnitman LaR - Robotics Laboratory Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil CTAI - Training Center in Technology in Industrial Automation Department of Chemical Engineering Federal University of Bahia Salvador Brazil

This paper proposes the integration of an Adaptive Artificial Potential Fields algorithm with a new end effector orientation control technique for real-time robot path planning. The development of autonomous robotic systems has undergone several advances in path planning algorithms. These systems generate object collision-free paths in the robot’s workspace. In this context, the Artificial Potential Fields technique has been the focus of improvements in recent years due to its simplicity of application and efficiency in real-time systems, since it does not require a global mapping of the robot’s workspace. In spite of its efficiency, this technique is susceptible to local minimum problems of different natures, such as Goals Non-Reachable with Obstacles Nearby (GNRON). To solve this problem, we suggest the use of an improvement called Adaptive Artificial Potential Fields used in conjunction with the proposed end effector orientation control technique, which allows reaching a desired orientation of the end effector. The resulting force, generated from the Adaptive Artificial Potential Field, guides the robot end effector to the goal. The Robot Operating System (ROS) framework and a collaborative robot manipulator UR5 are used to validate the proposed method on an approaching task for an object on a 3D printer tray.

关键词： Artificial Potential Field Orientation Control Robotic manipulators Path planning

Simultaneous tactile exploration and grasp refinement for unknown objects

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arXiv 2021年

作者： de Farias, Cristiana Marturi, Naresh Stolkin, Rustam Bekiroglu, Yasemin The Extreme Robotics Laboratory School of Metallurgy and Materials University of Birmingham Birmingham United Kingdom Chalmers University of Technology Department of Electrical Engineering Automatic Control research group Sweden University College London Department of Computer Science Centre for Artificial Intelligence United Kingdom

This paper addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an observed object, for which the far side remains occluded. We show how an initial grasp attempt, based on an initial guess of the overall object shape, yields tactile glances of the far side of the object which enable the shape estimate and consequently the successive grasps to be improved. We propose a grasp exploration approach using a probabilistic representation of shape, based on Gaussian Process Implicit Surfaces. This representation enables initial partial vision data to be augmented with additional data from successive tactile glances. This is combined with a probabilistic estimate of grasp quality to refine grasp configurations. When choosing the next set of finger placements, a bi-objective optimisation method is used to mutually maximise grasp quality and improve shape representation during successive grasp attempts. Experimental results show that the proposed approach yields stable grasp configurations more efficiently than a baseline method, while also yielding improved shape estimate of the grasped object. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

Image-guided breast biopsy of MRI-visible lesions with a hand-mounted motorised needle steering tool

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arXiv 2021年

作者： Lagomarsino, Marta Groenhuis, Vincent Casadio, Maura Welleweerd, Marcel K. Siepel, Françoise J. Stramigioli, Stefano Laboratory Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Drienerlolaan 5 Enschede7522NB Netherlands Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via Opera Pia 13 Genoa16145 Italy Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy

A biopsy is the only diagnostic procedure for accurate histological confirmation of breast cancer. When sonographic placement is not feasible, a Magnetic Resonance Imaging(MRI)-guided biopsy is often preferred. The lack of real-time imaging information and the deformations of the breast make it challenging to bring the needle precisely towards the tumour detected in pre-interventional Magnetic Resonance (MR) images. The current manual MRI-guided biopsy workflow is inaccurate and would benefit from a technique that allows real-time tracking and localisation of the tumour lesion during needle insertion. This paper proposes a robotic setup and software architecture to assist the radiologist in targeting MR-detected suspicious tumours. The approach benefits from image fusion of preoperative images with intraoperative optical tracking of markers attached to the patient's skin. A hand-mounted biopsy device has been constructed with an actuated needle base to drive the tip toward the desired direction. The steering commands may be provided both by user input and by computer guidance. The workflow is validated through phantom experiments. On average, the suspicious breast lesion is targeted with a radius down to 2.3 mm. The results suggest that robotic systems taking into account breast deformations have the potentials to tackle this clinical challenge. Copyright © 2021, The Authors. All rights reserved.

关键词： Biopsy

Urban Digital Twins for Intelligent Road Inspection

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Urban Digital Twins for Intelligent Road Inspection

IEEE International Conference on Big Data

作者： Rui Fan Yikang Zhang Sicen Guo Jiahang Li Yi Feng Shuai Su Yanting Zhang Wenshuo Wang Yu Jiang Mohammud Junaid Bocus Xingyi Zhu Qijun Chen the Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous Systems and State Key Laboratory of Intelligent Autonomous Systems Robotics & Artificial Intelligence Laboratory (RAIL) Tongji University Shanghai P. R. China Department of Computer Science & Technology Donghua University Shanghai P. R. China Department of Civil Engineering McGill University Montreal Canada CTO office at ClearMotion Billerica MA USA Department of Electrical & Electronic Engineering the University of Bristol BS8 1UB U.K Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji University Shanghai P. R. China

ISBN: (纸本)9781665480468

Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at ***/view/udtiri-workshop/bigdata-2022.

关键词： Roads Data acquisition Inspection Big Data Rough surfaces Digital twins

Robot-Assisted Vascular Shunt Insertion with the dVRK Surgical Robot

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Journal of Medical robotics Research 2023年第3-4期8卷

作者： Dharmarajan, Karthik Panitch, Will Shi, Baiyu Huang, Huang Chen, Lawrence Yunliang Moghani, Masoud Yu, Qinxi Hari, Kush Low, Thomas Fer, Danyal Garg, Animesh Goldberg, Ken Department of Electrical Engineering and Computer Sciences University of California Berkeley 94709 CA United States Department of Industrial Engineering and Operations Research University of California Berkeley 94709 CA United States Department of Mechanical and Industrial Engineering University of Toronto Toronto M5S 3G8 ON Canada Department of Computer Science University of Toronto Toronto M5S 3G8 ON Canada Robotics Laboratory Sri International Menlo Park 94025 CA United States Department of General Surgery University of California San Francisco East Bay Oakland 94602 CA United States School of Interactive Computing Georgia Institute of Technology Atlanta 30332 GA United States

Vascular shunt insertion is a common surgical procedure performed to restore blood flow to damaged tissues temporarily. It usually requires a surgeon and a surgical assistant. We consider three scenarios: (1) a surgeon is available locally;(2) a remote surgeon is available via teleoperation;(3) no surgeon is available. In each scenario, a minimally invasive surgical-assistant da Vinci robot operates in a different mode either by teleoperation or automation. Robotic assistance for this procedure is challenging due to precision and control uncertainty. The role of the robot in this task depends on the availability of a human surgeon. We propose a trimodal framework for vascular shunt insertion assisted by a da Vinci Research Kit (dVRK) robotic surgical assistant (RSA). To help further study for the community, we also present a physics-based simulated environment for shunt insertion built on top of the NVIDIA Isaac ORBIT simulator. We collect a large dataset of trajectories for the shunt insertion environment using ORBIT and implement these trajectories to show the simulator's realism, showcasing the possibility for future work to use the simulator for policy learning. Physical experiments demonstrate a success rate of 65-100% for mode (1), 100% for mode (2), and 75-95% for mode (3) across vessel phantoms with different sizes, color, and material properties. For dataset and videos, see https://***/***/ravsi. © 2023 World Scientific Publishing Company.

关键词： robot simulation Surgical robotics teleoperation visual servoing

A Reinforcement Learning Based Multiple Strategy Framework for Tracking a Moving Target

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A Reinforcement Learning Based Multiple Strategy Framework f...

IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Zixuan Huo Shilong Dai Mingxing Yuan Xiang Chen Xuebo Zhang Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China Department of Electrical and Computer Engineering University of Windsor Ontario Canada

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

The pursuit-evasion game has been a classic research topic in the field of mobile robotics. Reinforcement learning (RL), which shows outstanding advantages in the decision-making area, is a widely used method in pursuitevasion game. This paper proposes a hierarchical framework in which RL allows the pursuer to select an appropriate strategy for the current condition in the upper level from multiple underlying strategies in the lower level. Through the analysis of existing motion planning algorithms, the dynamic window approach (DWA) and the proportional guidance method (PG) are used as the lower level strategies of the framework. Individual discussions on the merits and limitations of the two motion planning algorithms indicate a possible complementarity between them. Simulations are carried out and the corresponding results validate the excellent performance of the proposed approach.

关键词： Games Robots Planning Heuristic algorithms Dynamics Target tracking Trajectory

Vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9288-9294页

作者： Christoforos Kanellakis Petros Karvelis Sina Sharif Mansouri Ali-akbar Agha-mohammadi George Nikolakopoulos The authors are with the Robotics Team Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå Sweden The author is with Jet Propulsion Laboratory California Institute of Technology Pasadena CA 91109

This work establishes a novel robocentric Non-linear Model Predictive Control (NMPC) framework for fast fully autonomous navigation of quadrotors in featureless dark tunnel environments. Additionally, this work leverages the processing of a single camera to generate direction commands along the tunnel axis, while regulating the platform’s altitude. The extracted visual dynamics are coupled in the sequel with the NMPC problem, structured around the Proximal Averaged Newton-type method for Optimal Control (PANOC), which is a fast numerical optimization method that is not sensitive to ill conditioning and is suitable for embedded NMPC implementations. Multiple fully realistic simulation results demonstrate the effectiveness of the proposed method in challenging environments.

关键词： Autonomous Vehicles Robot Navigation Non linear model predictive control MAV

An AI-powered Public Health Automated Kiosk System for Personalized Care: An Experimental Pilot Study

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arXiv 2025年

作者： Falahati, Sonya Alizadeh, Morteza Safahi, Zhino Khaledian, Navid Meybodi, Mohsen Alambardar Salmanpour, Mohammad R. VancouverBC Canada Electrical and Computer Engineering Department Nooshirvani University of Technology Babol Iran Department of Mathematics University of Isfahan Isfahan Iran Social & Biological Network Laboratory Department of Computer Engineering University of Kurdistan Sanandaj Iran University of Luxembourg Esch-sur-Alzette Luxembourg Department of Applied Mathematics and Computer Science University of Isfahan Isfahan Iran Department of Integrative Oncology BC Cancer Research Institute Vancouver Canada

Background: The HERMES Kiosk (Healthcare Enhanced Recommendations through Artificial Intelligence & Expertise System) is designed to provide personalized Over-the-Counter (OTC) medication recommendations, addressing the limitations of traditional health kiosks. It integrates an advanced GAMENet model enhanced with Graph Attention Networks (GAT) and Multi-Head Cross-Attention (MHCA) while ensuring user privacy through federated learning. This paper outlines the conceptual design and architecture of HERMES, with a focus on deployment in high-traffic public areas. Methods: HERMES analyzes self-reported symptoms and anonymized medical histories using AI algorithms to generate context-aware OTC medication recommendations. The system was initially trained using Electronic Health Records (EHR) from the MIMIC-III dataset (6,350 patients) and Drug-Drug Interaction (DDI) data from the TWOSIDES database, incorporating the top 90 severity DDI types. Real-time DDI checks and ATC-mapped drug codes further improve safety. The kiosk is designed for accessibility, offering multilingual support, large fonts, voice commands, and Braille compatibility. A built-in health education library promotes preventive care and health literacy. A survey was conducted among 10 medical professionals to evaluate its potential applications in medicine. Results: Preliminary results show that the enhanced GAMENet model achieved a Precision-Recall AUC (PRAUC) of 0.74, outperforming the original model. These findings suggest a strong potential for delivering accurate and secure healthcare recommendations in public settings. Conclusion: HERMES demonstrates how AI-driven, privacy-preserving kiosks can enhance public health access, empower users, and alleviate burdens on healthcare systems. Future work will focus on real-world deployment, usability testing, and scalability for broader adoption. © 2025, CC BY-NC-ND.

关键词： Personalized medicine

A heterogeneous group CNN for image super-resolution

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arXiv 2022年

作者： Tian, Chunwei Zhang, Yanning Zuo, Wangmeng Lin, Chia-Wen Zhang, David Yuan, Yixuan The School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China The School of Computer Science Northwestern Polytechnical University The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China The School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan Guangdong Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong

Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image superresolution (SR) under complex scenes. In this paper, we present a heterogeneous group SR CNN (HGSRCNN) via leveraging structure information of different types to obtain a high-quality image. Specifically, each heterogeneous group block (HGB) of HGSRCNN uses a heterogeneous architecture containing a symmetric group convolutional block and a complementary convolutional block in a parallel way to enhance internal and external relations of different channels for facilitating richer low-frequency structure information of different types. To prevent appearance of obtained redundant features, a refinement block with signal enhancements in a serial way is designed to filter useless information. To prevent loss of original information, a multi-level enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN. Besides, a parallel up-sampling mechanism is developed to train a blind SR model. Extensive experiments illustrate that the proposed HGSRCNN has obtained excellent SR performance in terms of both quantitative and qualitative analysis. Codes can be accessed at https://***/hellloxiaotian/HGSRCNN. © 2022, CC BY-NC-SA.

关键词： Convolutional neural networks