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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Riccardo Franceschini Matteo Fumagalli Julian Cayero Becerra Eurecat Centre Tecnològic de Catalunya Robotics and Automation Unit Cerdanyola del Valles Barcelona Spain Department of Electrical Engineering Automation and Control group Danish Technical University Elektrovej Lyngby Denmark

ISBN: (纸本)9781665456814

Safe autonomous navigation of robots in complex and cluttered environments is a crucial task and is still an open challenge even in 2D environments. Being able to efficiently minimize multiple constraints such as safety or battery drain requires the ability to understand and leverage information from different cost maps. Rapid-exploring random trees (RRT) methods are often used in current path planning methods, thanks to their efficiency in finding a quick path to the goal. However, these approaches suffer from a slow convergence towards an optimal solution, especially when the planner's goal must consider other aspects like safety or battery consumption besides simply achieving the goal. Therefore, it is proposed a sample-efficient and cost-aware sampling RRT* method that can overcome previous methods by exploiting the information gathered from map analysis. In particular, the use of a Reinforcement Learning agent is leveraged to guide the RRT* sampling toward an almost optimal solution. The performance of the proposed method is demonstrated against different RRT* implementations in multiple synthetic environments.

关键词： Costs Three-dimensional displays Reinforcement learning Robot sensing systems Path planning Safety Batteries

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Immune Optimization Algorithm for Traveling Salesman Problem Based on Clustering Analysis and Self-Circulation 21

Immune Optimization Algorithm for Traveling Salesman Problem...

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Proceedings of the 3rd International Conference on Advanced Information Science and System

作者： Yunqiang Gao Yan Shen Zhe Yang Dapeng Chen Mingxin Yuan School of Mechanical Engineering Jiangsu University of Science and Technology China School of Mechanical and Power Engineering Jiangsu University of Science and Technology China Department of Robotics Jiangsu Institute of automation China

ISBN: (纸本)9781450385862

In order to improve the population diversity during immune optimization of high-dimensional TSP problem, avoid falling into local optimal solutions, and then improve the algorithm's global optimization capability and optimization efficiency, Immune Optimization Algorithm for TSP Based on Cluster Analysis and Self-Circulation, (IOATCS) is proposed. Inspired by cluster analysis, TSP population data is classified and evolved according to the relevance of the problem to improve the efficiency of population optimization; a self-circulation strategy is designed to obtain new offspring through the self-circulation and crossover of the parent body, and then inherit the gene fragments of excellent individuals to the offspring, which improves the global search ability of the algorithm. The test results of six groups of high-dimensional TSP problems show that compared with the basic genetic algorithm, immune genetic algorithm and ant colony algorithm, the planning performance of the algorithm in this paper is the best, and the average relative error and relative error standard deviation of the algorithm are reduced by 10.21% and 4.54% respectively, and the average convergence algebra and convergence algebra standard deviation are reduced by 31.2% and 38.7% respectively, which verifies the strong optimization ability, high optimization efficiency and good stability of the proposed algorithm.

关键词： self-circulation immune evolution high-dimensional TSP cluster analysis adaptability

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Adaptive Stiffness Control of Series Elastic Actuator Manipulators Based on Dynamic System

Adaptive Stiffness Control of Series Elastic Actuator Manipu...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Kunlin Guo Chao Zeng Weiyong Si Ning Wang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control School of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK School of Computer Science and Electronic Engineering University of Essex Colchester Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In traditional control methods, Series Elastic Actuator (SEA) joint manipulators are limited by their hardware and can only perform simple tasks with low stiffness. To address this issue, we propose a stiffness adjustment control strategy for SEA manipulators based on Dynamic Systems (DS) with good generalization performance. By enhancing the generalization capability of DS in complex tasks and combining posture control, the SEA manipulators can autonomously adjust control gains and postures according to task requirements to achieve higher stiffness at the end-effector. Experimental results involving the activation of air switches with different stiffness levels and installation angles have demonstrated the effectiveness of our proposed method. The results indicate that compared to traditional methods of adjusting the control gain and retraining DS for similar tasks, our approach exhibits superior generalization performance while maintaining end-effector stiffness during interactions.

关键词： Actuators Computer aided software engineering automation Adaptive systems Control systems End effectors Hardware Dynamical systems Manipulator dynamics

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A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp

arXiv

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arXiv 2024年

作者： Xu, Yansong Wang, Xiaohui Li, Junlin Zhang, Xiaoqian Li, Feng Gao, Qing Fu, Chenglong Leng, Yuquan The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen518107 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China

The anthropomorphism of grasping process significantly benefits the experience and grasping efficiency of prosthetic hand wearers. Currently, prosthetic hands controlled by signals such as brain-computer interfaces (BCI) and electromyography (EMG) face difficulties in precisely recognizing the amputees’ grasping gestures and executing anthropomorphic grasp processes. Although prosthetic hands equipped with vision systems enables the objects’ feature recognition, they lack perception of human grasping intention. Therefore, this paper explores the estimation of grasping gestures solely through visual data to accomplish anthropopathic grasping control and the determination of grasping intention within a multi-object environment. To address this, we propose the Spatial Geometry-based Gesture Mapping (SG-GM) method, which constructs gesture functions based on the geometric features of the human hand grasping processes. It’s subsequently implemented on the prosthetic hand. Furthermore, we propose the Motion Trajectory Regression-based Grasping Intent Estimation (MTR-GIE) algorithm. This algorithm predicts pre-grasping object utilizing regression prediction and prior spatial segmentation estimation derived from the prosthetic hand’s position and trajectory. The experiments were conducted to grasp 8 common daily objects including cup, fork, etc. The experimental results presented a similarity coefficient R2 of grasping process of 0.911, a Root Mean Squared Error (RMSE) of 2.47°, a success rate of grasping of 95.43%, and an average duration of grasping process of 3.07±0.41 s. Furthermore, grasping experiments in a multi-object environment were conducted. The average accuracy of intent estimation reached 94.35%. Our methodologies offer a groundbreaking approach to enhance the prosthetic hand’s functionality and provides valuable insights for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Electromyography

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive Control for Tractor-Trailer Trajectory Tracking with Unknown Parameters

arXiv

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arXiv 2023年

作者： Wang, Zehao Zhang, Han Wang, Jingchuan Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive Control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC. © 2023, CC BY-NC-SA.

关键词： Model predictive control

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Design and Simulation of 3 Fingered Underactuated Gripper

Design and Simulation of 3 Fingered Underactuated Gripper

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2020 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2020

作者： Mohan, Anand Soman, Gokul Srichitra, S. Cletus, Jaimon Robotics And Automation College Of Engineering Trivandrum ThiruvananthapuramKerala India College Of Engineering Trivandrum Department Of Mechanical Engineering ThiruvananthapuramKerala India

ISBN: (纸本)9781728190525

The paper proposes to design and develop an under-actuated three fingered gripper with 7 degrees of freedom. The objective is to simulate a flexible gripper, able to grasp objects of a variety of shapes, using ROS framework. A gripper is designed which is driven by four motors - one each for the three fingers, and a fourth motor to emulate the opposing action of two fingers. The prototype is designed to be used as an end effector of ABB IRB 120 manipulator. The paper discusses the different stages of design and simulation involved - specification definition, CAD modeling, torque computation for motor selection and simulation of the model in Gazebo. © 2020 IEEE.

关键词： Grippers

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Speech-Vision Based Multi-Modal AI Control of a Magnetic Anchored and Actuated Endoscope

Speech-Vision Based Multi-Modal AI Control of a Magnetic Anc...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Li, Jixiu Huang, Yisen Ng, Wing Yin Cheng, Truman Wu, Xixin Dou, Qi Meng, Helen Heng, Pheng Ann Liu, Yunhui Chan, Shannon Melissa Navarro-Alarcon, David Hang Ng, Calvin Sze Yan Chiu, Philip Wai Li, Zheng The Department of Surgery The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The T Stone Robotics Institute The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Mechanical Engineering The Hong Kong Polytechnic University Hong Kong Hong Kong The Department of Surgery and the Chow Yuk Ho Technology Centre for Innovative Medicine The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Surgery ChowYuk Ho Technology Centre of Innovative Medicine Li Ka Shing Institute of Health Science Multiscale Medical Robotics Center Ltd. The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781665481090

In minimally invasive surgery (MIS), controlling the endoscope view is crucial for the operation. Many robotic endoscope holders were developed aiming to address this prob-lem,. These systems rely on joystick, foot pedal, simple voice command, etc. to control the robot. These methods requires surgeons extra effort and are not intuitive enough. In this paper, we propose a speech-vision based multi-modal AI approach, which integrates deep learning based instrument detection, automatic speech recognition and robot visual servo control. Surgeons could communicate with the endoscope by speech to indicate their view preference, such as the instrument to be tracked. The instrument is detected by the deep learning neural network. Then the endoscope takes the detected instrument as the target and follows it with the visual servo controller. This method is applied to a magnetic anchored and guided endoscope and evaluated experimentally. Preliminary results demonstrated this approach is effective and requires little efforts for the surgeon to control the endoscope view intuitively. © 2022 IEEE.

关键词： Endoscopy

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Corrections to “Model-Based Design of the COAST Guidewire Robot for Large Deflection”

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IEEE robotics and automation Letters 2023年第2期9卷 1155-1157页

作者： Yash Chitalia Achraj Sarma Timothy A. Brumfiel Nancy J. Deaton Maxina Sheft Jaydev P. Desai Healthcare Robotics and Telesurgery (HeaRT) Laboratory Department of Mechanical Engineering University of Louisville Louisville KY USA RoboMed Laboratory at the Georgia Institute of Technology Atlanta GA USA Matician Inc Mountain View CA USA Medical Robotics and Automation Laboratory Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA USA Harvard-MIT Health Sciences and Technology Cambridge MA USA Georgia Institute of Technology Atlanta GA USA

Presents corrections to the article “Model-Based Design of the COAST Guidewire Robot for Large Deflection.

关键词： Robots

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Deformation-aware data-driven grasp synthesis

arXiv

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arXiv 2021年

作者： Le, Tran Nguyen Lundell, Jens Abu-Dakka, Fares J. Kyrki, Ville Intelligent Robotics Group The Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Finland

Grasp synthesis for 3D deformable objects remains a little-explored topic, most works aiming to minimize deformations. However, deformations are not necessarily harmful-humans are, for example, able to exploit deformations to generate new potential grasps. How to achieve that on a robot is though an open question. This paper proposes an approach that uses object stiffness information in addition to depth images for synthesizing high-quality grasps. We achieve this by incorporating object stiffness as an additional input to a state-of-the-art deep grasp planning network. We also curate a new synthetic dataset of grasps on objects of varying stiffness using the Isaac Gym simulator for training the network. We experimentally validate and compare our proposed approach against the case where we do not incorporate object stiffness on a total of 2800 grasps in simulation and 420 grasps on a real Franka Emika Panda. The experimental results show significant improvement in grasp success rate using the proposed approach on a wide range of objects with varying shapes, sizes, and stiffness. Furthermore, we demonstrate that the approach can generate different grasping strategies for different stiffness values, such as pinching for soft objects and caging for hard objects. Together, the results clearly show the value of incorporating stiffness information when grasping objects of varying stiffness. Copyright © 2021, The Authors. All rights reserved.

关键词： Stiffness

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

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