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International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN)

作者： Sachin Dhyani Vijay Kumar Robotics and Mechatronics Research Lab Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

One of the crucial software elements in the upcoming generation of autonomous vehicles is image recognition. Traditional approaches to image recognition using computer vision and machine learning typically have a lengthy response time. Modern artificial neural network-based methods and designs, including the YOLOv5s algorithm, are able to tackle this issue without suffering precision losses. In this study, we demonstrate how to use the most recent YOLOv5s algorithm to identify traffic signs. We showed the reliability of the method by training the network for 4 traffic sign classes (speed limit, traffic light, crosswalks, stop,).

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Real-Time License Plate Detection and Recognition System using YOLOv7x and EasyOCR

Real-Time License Plate Detection and Recognition System usi...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Sachin Dhyani Vijay Kumar Robotics and Mechatronics Research Lab Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Automated license plate detection system is used for recognition and locating of license plates on vehicles using image processing and computer vision methods. Automated license plate detection is an important area of research in computer vision with far-reaching applications in the industrial sector. It has the potential to increase productivity and accuracy in numerous industrial applications. The procedure entails utilizing a camera to take a photo of a vehicle's license plate, processing the photo to identify the plate, and extracting the characters from the plate. This paper provides implementation of the YOLOv7x algorithm to detect license plate on vehicles. EasyOCR is applied to the detected license plate to recognize the text. The model is trained on a customized dataset consisting of only Indian vehicles license plate. The proposed work achieves a high accuracy of 99% and outperforms old techniques like Yolov3, darknet, CNN.

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Fuzzy Feedback Control for Electro-Hydraulic Actuators

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Intelligent Automation & Soft Computing 2023年第5期36卷 2441-2456页

作者： Tan Nguyen Van Huy Q.Tran Vinh Xuan Ha Cheolkeun Ha Phu Huynh Minh School of Engineering-Technology Thu Dau Mot UniversityThu Dau Mot City590000Vietnam Faculty of Engineering-Technology Nguyen Tat Thanh UniversityHo Chi Minh City754000Vietnam Hue Industrial College Hue City530000Vietnam Robotics and Mechatronics Lab University of UlsanUlsan City44610Korea

Electro-hydraulic actuators(EHA)have recently played a signiﬁcant role in modern industrial applications,especially in systems requiring extremely high *** can be explained by EHA’s ability to precisely control the position and force through advanced sensors and innovative control *** of the promising approaches to improve control accuracy for EHA systems is applying classical to modern control algorithms,in which the proportional–inte-gral–derivative(PID)algorithm,fuzzy logic controller,and a hybrid of these methods are popular *** this paper,we developed a novel version of the fuzzy control algorithm and linear feedback control method,namely fuzzy lin-ear feedback control,to improve the control *** achieve the highest performance,weﬁrst designed a mathematical EHA model based on the Matlab/Simulink software packages thanks to the selected parameters,which are similar to a real EHA ***,we respectively applied PID,fuzzy PID(FPID),and fuzzy linear feedback control(FLFC)before comparing them to have a full view of the outstanding advantages of the proposed *** simulation results showed that the proposed FLFC algorithm is approximately 99%and 77%super-ior in performance to the PID and feedback control algorithms,respectively.

关键词： Electro-hydraulic actuator fuzzy PID feedback control

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Enabling Communication between Heterogeneous Robots and Human Operators in Collaborative Missions

Enabling Communication between Heterogeneous Robots and Huma...

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IEEE Conference on Aerospace

作者： Marco Sewtz Florian Samuel Lay Xiaozhou Luo Thibaud Chupin Neal Y. Lii German Aerospace Center Institut of Robotics and Mechatronics Weßling Germany Human Robot Interaction Lab European Space Angency Noordwijk Netherlands

Enabling the return of human presence to the lunar surface constitutes a central objective for various space agencies. While previous lunar missions were of limited duration, the landscape is poised for significant transformation in the coming years, characterized by extended surface operations and the establishment of a permanent base near the lunar south pole. As emphasized in the National Aeronautics and Space Administration (NASA)’s technology roadmap and echoed by the European Space Agency (ESA) in its Terrae Novae 2030+ roadmap, the pivotal role of robotics is underscored for attaining a sustainable lunar base. The Surface Avatar mission, led by the German Aerospace Center (DLR) and partnered by ESA, represents a pioneering effort aimed at investigating the practical application of scalable autonomy through multi-modal tele-operation and task-oriented command protocols. This approach empowers astronauts with the capability to oversee and direct a diverse fleet of robots, each with unique functions and capabilities. Aboard the International Space Station (ISS), crew members are currently entrusted with the command of a diverse ensemble of ground-based robots, including the wheeled humanoid known as Rollin’ Justin, the versatile rover Interact, the articulated arm of a lander mockup, and a small four-legged system named BERT. Nonetheless, the coexistence of multiple disparate robotic systems within the same network presents a considerable challenge in achieving sustainable development. Adapting to each system’s specific requirements with every update or altering the communication infrastructure to accommodate new combinations of robots is not conducive to long-term operational efficiency. This work delves into a comprehensive and modular approach designed to mitigate these challenges by minimizing the prerequisite knowledge required for each system, offering an out-of-the-box solution for situational awareness during ongoing missions, and streamlining the integr

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Keypoints Estimation on Partial Point Clouds

Keypoints Estimation on Partial Point Clouds

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Maryam Saleem Mohammad Zohaib Syed Abbas Zilqurnain Naqvi Department of Mechatronics & Control Engineering Human Centered Robotics Lab UET Lahore Pakistan SIGMA Clermont University of Clermont Auvergne Aubiere France

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Estimating 3D keypoints of an object has achieved considerable interest, especially in computer graphics and ma-chine vision. The complexity of the problem increases when the object is noisy, decimated, or partially occluded. To this end, we present an approach to estimate 3D keypoints from the partial PCDs such that they also highlight the object's missing parts. It is based on two modules: shape completion and keypoints estimation. The first module computes the object's missing parts and generates a dense shape which is used by the second module to estimate semantically and geometrically consistent keypoints. The approach also estimates confidence scores that allow the selection of valid keypoint considering the object's structure. We evaluate the proposed method for four different backbones and observe that we get better results using PointNet. Furthermore, the presented results show that the Percentage of Correct Keypoints (PCK) and Dual Alignment Score (DAS) for the keypoints estimated from the dense shapes (36.61 and 76.29) are 23.51 % and 12.14% higher, respectively, compared to those estimated from the partial objects (13.10 and 64.15).

关键词： Point cloud compression Industries Three-dimensional displays Accuracy Shape Estimation Computer graphics Complexity theory Noise measurement robotics and automation

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IMU Based Pose Reconstruction and Closed-loop Control for Soft Robotic Arms

IMU Based Pose Reconstruction and Closed-loop Control for So...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Guanran Pei Francesco Stella Omar Meebed Zhenshan Bing Cosimo Della Santina Josie Hughes CREATE Lab EPFL Lausanne Switzerland Technical University of Munich Munich Germany Department of Cognitive Robotics Delft University of Technology Delft The Netherlands Institute of Robotics and Mechatronics German Aerospace Center (DLR) Wessling Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Soft continuum manipulators are celebrated for their versatility and physical robustness to external forces and perturbations. However, this feature comes at a cost. The many degrees of freedom and compliance pose challenges for accurate pose reconstruction, both in terms of distributed sensing and pose reconstruction algorithms. Moreover, soft arms are inherently susceptible to deformation from external forces or loads, meaning that closed-loop control is essential for robust task performance. In this article, we propose the integration of multiple Inertial Measurement Units (IMUs) of a soft robot arm, Helix, for reconstruction of pose under internal and external forces. Furthermore, we integrate this dynamic pose reconstruction for kinematic-based closed-loop control strategies. By serially integrating sensing in the body of the Helix soft manipulator, we provide the system with high-frequency pose reconstruction and demonstrate improvements in end effector position with comparison to open-loop performance.

关键词： Measurement units Perturbation methods Inertial navigation Soft robotics Reconstruction algorithms Robot sensing systems Robustness Sensors Manipulator dynamics Intelligent robots

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Learning flatness-based controller using neural networks

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ASME Letters in Dynamic Systems and Control 2021年第2期1卷 021003页

作者： Ren, Hailin Qi, Jingyuan Ben-Tzvi, Pinhas Robotics and Mechatronics Lab Department of Mechanical Engineering Virginia Tech BlacksburgVA24060 United States Department of Physics Virginia Tech BlacksburgVA24060 United States

This paper presents a method to imitate flatness-based controllers for mobile robots using neural networks. Sample case studies for a unicycle mobile robot and an unmanned aerial vehicle (UAV) quadcopter are presented. The goals of this paper are to (1) train a neural network to approximate a previously designed flatness-based controller, which takes in the desired trajectories previously planned in the flatness space and robot states in a general state space, and (2) present a dynamic training approach to learn models with high-dimensional inputs. It is shown that a simple feedforward neural network could adequately compute the highly nonlinear state variables transformation from general state space to flatness space and replace the complicated designed heuristic to avoid singularities in the control law. This paper also presents a new dynamic training method for models with high-dimensional independent inputs, serving as a reference for learning models with a multitude of inputs. Training procedures and simulations are presented to show both the effectiveness of this novel training approach and the performance of the welltrained neural network. Copyright © 2021 by ASME.

关键词： Machine learning

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A Learning-based Controller for Multi-Contact Grasps on Unknown Objects with a Dexterous Hand

A Learning-based Controller for Multi-Contact Grasps on Unkn...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Dominik Winkelbauer Rudolph Triebel Berthold Bäuml DLR Institute of Robotics & Mechatronics Germany Learning AI for Dextrous Robots Lab (***) Technical University of Munich Germany Karlsruhe Institute of Technology (KIT) Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Existing grasp controllers usually either only support finger-tip grasps or need explicit configuration of the inner forces. We propose a novel grasp controller that supports arbitrary grasp types, including power grasps with multi-contacts, while operating self-contained on before unseen objects. No detailed contact information is needed, but only a rough 3D model, e.g., reconstructed from a single depth image. First, the external wrench being applied to the object is estimated by using the measured torques at the joints. Then, the torques necessary to counteract the estimated wrench while keeping the object at its initial pose are predicted. The torques are commanded via desired joint angles to an underlying joint-level impedance controller. To reach real-time performance, we propose a learning-based approach that is based on a wrench estimator- and a torque predictor neural network. Both networks are trained in a supervised fashion using data generated via the analytical formulation of the controller. In an extensive simulation-based evaluation, we show that our controller is able to keep 83.1% of the tested grasps stable when applying external wrenches with up to 10 N. At the same time, we outperform the two tested baselines by being more efficient and inducing less involuntary object movement. Finally, we show that the controller also works on the real DLR-Hand II, reaching a cycle time of 6 ms. Website: ***/grasping

关键词： Solid modeling Torque Three-dimensional displays Power measurement Neural networks Robot sensing systems Real-time systems Torque measurement Impedance Intelligent robots

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Toward Long-Lasting Large-Scale Soft Robots: The Durability Challenge in Architectured Materials

Toward Long-Lasting Large-Scale Soft Robots: The Durability ...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Francesco Stella Guanran Pei Omar Meebed Qinghua Guan Zhenshan Bing Cosimo Della Santina Josie Hughes CREATE Lab EPFL Lausanne Switzerland Department of Cognitive Robotics Delft University of Technology Delft The Netherlands Technical University of Munich Munich Germany German Aerospace Center (DLR) Institute of Robotics and Mechatronics Wessling Germany

Soft robots promise groundbreaking advancements across various industries. However, soft robots are susceptible to wear, fatigue, and material degradation. Their durability and long-term reliability are often overlooked, despite being critical for the successful deployment of these systems in real-world applications. This article contributes to solving this challenge by identifying metrics that reflect material wear, mechanical hysteresis, and drift occurring during long-term operations in soft architectured materials. While this same pipeline can be generalized to different soft robots, we test these metrics on the trimmed helicoid architectured materials, and we validate the improvement in performance on the Helix soft manipulator. Thanks to the proposed metrics, we demonstrate a 75% reduction in repeatability errors over long-duration experiments.

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Robot-Assisted Navigation for Visually Impaired through Adaptive Impedance and Path Planning

Robot-Assisted Navigation for Visually Impaired through Adap...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Pietro Balatti Idil Ozdamar Doganay Sirintuna Luca Fortini Mattia Leonori Juan M. Gandarias Arash Ajoudani HRI2 Lab Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and System Engineering University of Genoa Genoa Italy Systems Engineering and Automation Department Robotics and Mechatronics Lab University of Malaga Malaga Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a framework to navigate visually impaired people through unfamiliar environments by means of a mobile manipulator. The Human-Robot system consists of three key components: a mobile base, a robotic arm, and the human subject who gets guided by the robotic arm via physically coupling their hand with the cobot’s end-effector. These components, receiving a goal from the user, traverse a collision-free set of waypoints in a coordinated manner, while avoiding static and dynamic obstacles through an obstacle avoidance unit and a novel human guidance planner. With this aim, we also present a legs tracking algorithm that utilizes 2D LiDAR sensors integrated into the mobile base to monitor the human pose. Additionally, we introduce an adaptive pulling planner responsible for guiding the individual back to the intended path if they veer off course. This is achieved by establishing a target arm end-effector position and dynamically adjusting the impedance parameters in real-time through a impedance tuning unit. To validate the framework we present a set of experiments both in laboratory settings with 12 healthy blindfolded subjects and a proof-of-concept demonstration in a real-world scenario.

关键词： Target tracking Navigation Robot kinematics End effectors Real-time systems Path planning Sensors

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