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A Framework for Learning Behavior Trees in Collaborative Robotic Applications

A Framework for Learning Behavior Trees in Collaborative Rob...

引用

IEEE international conference on automation Science and Engineering (CASE)

作者： Matteo Iovino Jonathan Styrud Pietro Falco Christian Smith ABB Corporate Research Västerås Sweden Division of Robotics Perception and Learning KTH - Royal Institute of Technology Stockholm Sweden ABB Robotics Västerås Sweden

In modern industrial collaborative robotic applications, it is desirable to create robot programs automatically, intuitively, and time-efficiently. Moreover, robots need to be controlled by reactive policies to face the unpredictability of the environment they operate in. In this paper we propose a framework that combines a method that learns Behavior Trees (BTs) from demonstration with a method that evolves them with Genetic Programming (GP) for collaborative robotic applications. The main contribution of this paper is to show that by combining the two learning methods we obtain a method that allows non-expert users to semi-automatically, time-efficiently, and interactively generate BTs. We validate the framework with a series of manipulation experiments. The BT is fully learnt in simulation and then transferred to a real collaborative robot.

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Research on the Development Status and Future Trends of Foreign Unmanned Ground Equipment

Research on the Development Status and Future Trends of Fore...

引用

Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Mingming Shi Xin He Haoyuan Li Hao Zhang Peng Lu Ji Ma Wuyi Liu Wenchao Wei Unit No 32322 PLA Urumqi China School of Information Systems Engineering Strategic Support Force Information Engineering University Zhengzhou China Department of UAV Engineering Army Engineering University Shijiazhuang China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

In recent years, it has been observed that the army's intelligent and unmanned weapons and equipment have played a remarkable role in close combat during local wars, armed conflicts, and counter-terrorism operations. Furthermore, their utilization has evolved from individual soldiers and sporadic usage to cluster deployment. Combining with the operational situation, this paper divided the development of foreign unmanned ground equipment (UGE) into three stages (application exploring, gradual maturity and dominant battle), and analyzed the main characteristics of UGE's overall development (top-level design for traction, supporting construction for foundation and technology advantage for guidance). At the same time, the classification methods of foreign UGE were discussed in detail according to the classification of function (reconnaissance and surveillance equipment, fire strike equipment and comprehensive support equipment), and the development trends of foreign UGE were summarized as intelligent, modularization, swarm, flexibility, stealth and autonomy. The aim of this paper is to provide a theoretical reference for advancing the construction of the armyUGE.

关键词： Control engineering automation Service robots Weapons Surveillance Robot kinematics Standardization Reconnaissance Market research Sustainable development

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Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection

Viewer-Centred Surface Completion for Unsupervised Domain Ad...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Darren Tsai Julie Stephany Berrio Mao Shan Eduardo Nebot Stewart Worrall Australian Centre for Field Robotics (ACFR) at the University of Sydney NSW Australia

Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to overfit the datasets they are trained on. This causes a drastic decrease in accuracy when the detectors are trained on one dataset and tested on another. We observe that lidar scan pattern differences form a large component of this reduction in performance. We address this in our approach, SEE-VCN, by designing a novel viewer-centred surface completion network (VCN) to complete the surfaces of objects of interest within an unsupervised domain adaptation framework, SEE [1]. With SEE-VCN, we obtain a unified representation of objects across datasets, allowing the network to focus on learning geometry, rather than overfitting on scan patterns. By adopting a domain-invariant representation, SEE-VCN can be classed as a multi-target domain adaptation approach where no annotations or re-training is required to obtain 3D detections for new scan patterns. Through extensive experiments, we show that our approach outperforms previous domain adaptation methods in multiple domain adaptation settings. Our code and data are available at https://***/darrenjkt/SEE-VCN.

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CLIPPER: A Graph-Theoretic Framework for Robust Data Association

CLIPPER: A Graph-Theoretic Framework for Robust Data Associa...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Lusk, Parker C. Fathian, Kaveh How, Jonathan P. MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

ISBN: (纸本)9781728190778

We present CLIPPER (Consistent Linking, Pruning, and Pairwise Error Rectification), a framework for robust data association in the presence of noise and outliers. We formulate the problem in a graph-theoretic framework using the notion of geometric consistency. State-of-the-art techniques that use this framework utilize either combinatorial optimization techniques that do not scale well to large-sized problems, or use heuristic approximations that yield low accuracy in high-noise, high-outlier regimes. In contrast, CLIPPER uses a relaxation of the combinatorial problem and returns solutions that are guaranteed to correspond to the optima of the original problem. Low time complexity is achieved with an efficient projected gradient ascent approach. Experiments indicate that CLIPPER maintains a consistently low runtime of 15 ms where exact methods can require up to 24 s at their peak, even on small-sized problems with 200 associations. When evaluated on noisy point cloud registration problems, CLIPPER achieves 100% precision and 98% recall in 90% outlier regimes while competing algorithms begin degrading by 70% outliers. In an instance of associating noisy points of the Stanford Bunny with 990 outlier associations and only 10 inlier associations, CLIPPER successfully returns 8 inlier associations with 100% precision in 138 ms. Code is available at https://***/clipper.

关键词： Runtime Codes automation conferences Noise measurement Time complexity Optimization

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Research on Steel Plate Linear Heating Robot Based on Deep Q Network of Gradient Strategy Method

Research on Steel Plate Linear Heating Robot Based on Deep Q...

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IEEE international conference on robotics and Biomimetics

作者： Zhiliang Zhang Ryojun Ikeura Zheng Wang Department of Engineering Mie University Tsu Mie Japan

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The heating and forming process of hull steel plates not only affects the hydrodynamic performance of ships, but also affects the cycle and cost of hull construction. In the process of 3D curved surface linear heating and forming of hull steel plates, the heating line planning difficulties, as well as the low forming efficiency and precision are solved. ANSYS is used to simulate the linear heating and forming process of steel plates to obtain data, and a linear heating model of a deep Q network(DQN) based on the gradient strategy method is established. This model can not only plan the heating line, but also optimize the heating line in real time, and realize the linear planning of steel plate heating for auxiliary workers. The results show that this research method can effectively realize the robot to plan the linear heating line of the target steel plate and heat the steel plate, providing a new solution for the optimization of the 3D curved surface forming process of steel plates and the realization of semi-automation.

关键词： Heating systems Deformable models Three-dimensional displays Costs Deformation Data models Planning Steel Robots Surface treatment

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COVID-19 Classification with CT Scan and Advanced Deep Learning Technologies 3

COVID-19 Classification with CT Scan and Advanced Deep Learn...

引用

3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Li, Ziyi Nanjing Normal University School of Electrical and Automation Engineering Nanjing China

ISBN: (纸本)9781665459112

The COVID-19 epidemic is still very serious, because the United States and other countries have relaxed prevention and control, and the vaccine is ineffective against the mutant virus, resulting in a large number of new cases. The existing epidemic detection methods are still insufficient, and some detection methods are relatively expensive and complicated, resulting in the supply not keeping up with the demand for detection. The purpose of this study is to use relatively convenient, fast and low-cost computer vision technology for epidemic detection. We tried the VGG, ResNet and DenseNet models on an open Kaggle dataset, and found that DenseNet achieved the best results, achieving 95% accuracy, and there is hope for further applications in the future. © 2022 IEEE.

关键词： Computerized tomography

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Standardized Transfer Learning models Enhance Classification of Breast Ultrasound Data 27

Standardized Transfer Learning Models Enhance Classification...

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27th international conference on System Theory, Control and Computing, ICSTCC 2023

作者： Bota, Maria Aurora Ciobotaru, Alexandru Bota, Peter Gota, Dan Ioan Stefan, Iulia Adina Valean, Honoriu Miclea, Liviu Automation Department Technical University of Cluj-Napoca Cluj-Napoca Romania Medical Department California University of Science and Medicine ColtonCA United States

ISBN: (纸本)9798350337983

Ultrasound imaging is an often employed technique in diagnosing of breast cancer, although the prediction reliability depends on the specialist's experience. Computer Aided Diagnosis (CAD) systems have been introduced for the enhancement the quality and time invested in classifying breast ultrasound(BUS) images. Deep Convolutional Neural Networks based algorithms is considered one of the most successful strategy in breast ultrasound image analysis. Data limitation is one of the prioritizing issues at the current moment. This problem is referred by introducing transfer-learning-based models and stratification as a data augmentation technique for achieving a better accuracy of the classification. The paper has demonstrated that the deep feature extraction and feature selection can categorized the breast ultrasound images using the pre-training methods. A dataset containing 1578 breast ultrasound images was used for model training and testing, and the optimal level of achievement was reached by InceptionResNetV2 and DenseNet121 with an accuracy of 83% and an"one over the rest" AUC score of 0.933 for DenseNet121, respectively 0.923 for InceptionResNetV2. © 2023 IEEE.

关键词： Convolutional neural networks

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Chance-Constrained SMPC for Autonomous Vehicles Approaching Unsignalized Crosswalks

Chance-Constrained SMPC for Autonomous Vehicles Approaching ...

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international conference on Autonomous Robots and Agents, ICARA

作者： Lea Pavelko Branimir Škugor Joško Deur Faculty of Mechanical Engineering and Naval Architecture University of Zagreb Zagreb Croatia

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

The paper proposes a chance-constrained stochastic model predictive control strategy to handle autonomous vehicle interactions with pedestrians near unsignalized crosswalks-in a safe and efficient manner. The strategy is designed to account for inherent uncertainties of pedestrian crossing behavior, and the effect of the vehicle state on pedestrian decisions. Probabilistic constraints are introduced within the optimal control problem to facilitate pedestrians' understanding of vehicle intentions and thus to avoid safety-critical situations. Finally, the proposed strategy is verified against a baseline control strategy via simulations of pedestrian model developed by using experimental data, for a variety of initial conditions and levels of pedestrian behavior uncertainty.

关键词： Pedestrians Uncertainty Stochastic processes Optimal control Predictive models Probabilistic logic Data models

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Data-Driven Robust Model Predictive Control for Pose Tracking of Redundant Manipulators

Data-Driven Robust Model Predictive Control for Pose Trackin...

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IEEE international conference on robotics and Biomimetics

作者： Zhaoyang Chen Fenglei Ni Kang Min Hong Liu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin Heilongjiang Province China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In this paper, we present a data-driven robust model predictive control (DD-RMPC) method for pose tracking of redundant manipulators. Firstly, a MPC-based trajectory tracking framework is established, in which the joint limits at three different levels (angle, velocity, and acceleration) are satisfied. Secondly, to deal with the system model's uncertainty, accelerate the error convergence speed, and reduce tracking errors, a novel data-driven RMPC is proposed, in which the system's input-output data are used to compensate for the conservatism of the system. Finally, the simulation results on a seven degrees of freedoms (DOFs) redundant manipulator show that DD-RMPC provides a faster error convergence rate and achieves smaller pose tracking errors than that of the comparison methods.

关键词： Jacobian matrices Uncertainty Trajectory tracking Simulation Biomimetics Predictive models Manipulators Data models Convergence Predictive control

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Enhancing Depth Image with Richer Convolution Feature for Improved Perception

Enhancing Depth Image with Richer Convolution Feature for Im...

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international conference on Control, automation and Systems ( ICCAS)

作者： Donggil You Jihoon Jung Jusang Jeong Junghyun Oh Department of Robotics Kwangwoon University Seoul Korea

Depth cameras are widely used in various fields, particularly in robotics, for tasks such as robot path planning, object avoidance, target tracking, and autonomous driving. However, the incomplete depth sensing technology of depth cameras results in depth images with significant noise, including errors in depth values and detection failures due to environmental instability. This noise adversely affects the performance and stability of robotic applications. To address this issue, various filtering methods have been proposed, but the corrected depth images still contain residual noise. This paper utilize a deep learning-based edge detection method, Richer Convolution Feature, to accurately estimate planar regions in depth images. The RANSAC algorithm is used to enhance the planes of objects. Experimental results demonstrate the effectiveness of the proposed approach in reducing noise and improving depth image quality.

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