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Intuitive Robot Path Planning through Augmented Reality

Intuitive Robot Path Planning through Augmented Reality

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international conference on methods and models in automation and robotics (MMAR)

作者： Mohammad-Ehsan Matour Alexander Winkler Department of Automation Hochschule Mittweida UAS Mittweida Germany

In this paper, an approach for intuitive path planning of a collaborative robot (cobot) based on augmented reality (AR) is presented. For this purpose, a user interface is developed that offers practical functions to the operator for full motion control over a robot manipulator. A mixed reality head-mounted display (HMD) acts as an interface for the operator by displaying virtual content that allows interaction with the robotic system. Besides providing information about the state of the robot, including joint position, velocity, and the force exerted on the robot flange, the gripping force can be adjusted during gripping operations, and motion commands can also be sent to the robot controller. It includes motion commands in joint and Cartesian space, as well as intuitive path planning of the robot based on waypoints created by the operator. A virtual cursor is incorporated into the virtual robot model, with its position and orientation aligned to that of the tool center point (TCP) of the real robot. the operator can execute linear point-to-point as well as non-linear motion. Regarding the safety aspects of the robot motion, a virtual model of the robot is also overlaid on the working environment, allowing the operator to carry out a preview motion to the given trajectory, showing the current and final position of each joint, before executing the motion on the real system. While the human-robot interface and virtual content are created on top of the Unity3D game engine, the Robot Operating System (ROS) provides reliable data transmission and processing between the HMD and the robot controller. the novelty of this work lies on the one hand, in an extensive interface and, on the other hand, in the robot path planning that takes into account the orientation provided by a virtual pointer.

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Interpretable deep learning approach for classification of breast cancer - a comparative analysis of multiple instance learning models

Interpretable deep learning approach for classification of b...

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international conference on methods and models in automation and robotics (MMAR)

作者： Jakub Buler Rafał Buler Maciej Bobowicz Maria Ferlin Marlena Rygusik Arkadiusz Kwasigroch Michał Grochowski Department of Intelligent Control Systems and Decision Support Gdansk University of Technology Poland 2nd Department of Radiology Medical University of Gdansk Poland

Breast cancer is the most frequent female cancer. Its early diagnosis increases the chances of a complete cure for the patient. Suitably designed deep learning algorithms can be an excellent tool for quick screening analysis and support radiologists and oncologists in diagnosing breast *** design of a deep learning-based system for automated breast cancer diagnosis is not easy due to the lack of annotated data, especially at pixel level, the large size of the images with relatively small cancer lesion sizes and class imbalance, a wide diversity of cancerous lesions, a variety of breasts, both in size and density, make the training of the neural models challenging. Moreover, clinicians are often concerned about using these black-box models because of the lack of transparency in their inference. To address these issues, we propose an approach taking advantage of Multiple Instance Learning (MIL), supported by attention mechanisms. We researched Attention-based MIL (AMIL), Gated AMIL (GAMIL), Dual Stream MIL (DSMIL) and CLustering-constrained AMIL (CLAM) models trained in a weakly-supervised manner and compared them with a common model in image classification tasks, *** approach described in this paper is multimodal and combines two mammographic projections (CC and MLO) in the training process. the developed neural system achieved high classification efficiency. Furthermore, exploiting the generated attentional maps allowed the localisation of cancerous lesions, thus increasing the interpretability of the algorithm. thanks to this mechanism, we were also able to detect artifacts in the analyzed database, difficult to spot but drastically skewing the algorithm’s performance.

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UAP-BEV: Uncertainty Aware Planning Using Bird's Eye View Generated From Surround Monocular Images 19

UAP-BEV: Uncertainty Aware Planning Using Bird's Eye View Ge...

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19th IEEE international conference on automation Science and Engineering, CASE 2023

作者： Dewangan, Vikrant Sharma, Basant Choudhary, Tushar Sharma, Sarthak Aanegola, Aakash Singh, Arun K. Krishna, K. Madhava Iiit Hyderabad Robotics Research Center India University of Tartu Estonia

ISBN: (纸本)9798350320695

Autonomous driving requires accurate reasoning of the location of objects from raw sensor data. Recent end-to-end learning methods go from raw sensor data to a trajectory output via Bird's Eye View(BEV) segmentation as an interpretable intermediate representation. Motion planning over cost maps generated via Birds Eye View (BEV) segmentation has emerged as a prominent approach in autonomous driving. However, the current approaches have two critical gaps. First, the optimization process is simplistic and involves just evaluating a fixed set of trajectories over the cost map. the trajectory samples are not adapted based on their associated cost values. Second, the existing cost maps do not account for the uncertainty in the cost maps that can arise due to noise in RGB images, BEV annotations. As a result, these approaches can struggle in challenging scenarios where there is abrupt cut-in, stopping, overtaking, merging, etc from the neighboring vehicles. In this paper, we propose UAP-BEV, a novel approach that models the noise in Spatio-Temporal BEV predictions to create an uncertainty-aware occupancy grid map. Using queries of the distance to the closest occupied cell, we obtain a sample estimate of the collision probability of the ego-vehicle. Subsequently, our approach uses gradient-free sampling-based optimization to compute low-cost trajectories over the cost map. Importantly, the sampling distribution is adapted based on the optimal cost values of the sampled trajectories. By explicitly modeling probabilistic collision avoidance in the BEV space, our approach is able to outperform the cost-map-based baselines in collision avoidance, route completion, time to completion, and smoothness. To further validate our method, we also show results on the real-world dataset NuScenes, where we report improvements in collision avoidance and smoothness. © 2023 IEEE.

关键词： Trajectories

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Boosting Robot Intelligence in Practice: Enhancing Robot Task Planning with Large Language models

Boosting Robot Intelligence in Practice: Enhancing Robot Tas...

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international conference on robotics and automation Engineering (ICRAE)

作者： Yisheng Zhang Zhigang Wang Shengmin Zhang Yanlong Peng Ming Chen School of Mechanical Engineering Shanghai Jiaotong University Shanghai China Intel Labs China Beijing China

Task planning capabilities are crucial for intelligent robots to operate autonomously in the physical world. However, traditional Planning Domain Definition Language (PDDL) based methods often suffer from combinatorial explosion and unsat-isfactory planning time. In this paper, we propose enhancing robot task planning with large language models (LLMs) in an innovative way - using LLMs to guide the search process of PDDL planners rather than replacing PDDL planning completely. the LLMs guide the search process of PDDL planners with learned heuristics and provide constraint reasoning to reduce the search space. To address potential pitfalls of LLMs, a verification mechanism is added at the execution stage to validate plan correctness. We evaluated our method on a real scenario, end-of-life vehicle battery disassembly. Experimental results demonstrate that incorporating LLMs into the planning pipeline can significantly improve planning efficiency and scalability while maintaining plan validity. this research provides a promising direction towards integrating language models with classical approaches to boost robot intelligence for practical applications. the proposed framework makes a solid step forward in en-hancing the task planning capability of future intelligent robotic systems.

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Towards Fast Face Image Quality Assessment via Latent Diffusion Model 18th

Towards Fast Face Image Quality Assessment via Latent Diffu...

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18th Chinese conference on Biometric Recognition, CCBR 2024

作者： Yan, Zheyu Zhao, Weisong Zhu, Xiangyu Gao, Li Zhang, Xiao-Yu Lei, Zhen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China China Mobile Financial Technology Co. Ltd. Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819610679

Face recognition technology has gradually come into wide application in real life. However, in real-life unconstrained scenarios, low-quality images could be matched to unpredictable individuals, thereby affecting the stability of recognition and even causing security problems. Conducting quality assessment on face images before facial recognition can help alleviate these problems by rejecting low-quality samples. In this paper, we propose a Fast Diffusion-model-based Face Image Quality Assessment method (FDif-FIQA). It uses a diffusion model to perturb face images and measures image quality based on the similarity of features before and after the perturbation. the denoising process is transferred from the pixel space to the latent space, thereby reducing the computational cost. We evaluated the performance of this method with multiple mainstream face recognition models on various public datasets. Compared to state-of-the-art methods, our approach demonstrates highly competitive performance and significant improvements in computational efficiency. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition

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Interactive Context-Comparative Model for Text Matching 4

Interactive Context-Comparative Model for Text Matching

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4th international conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Zou, Zhuozhang Yang, Wei Pang, Longlong Liang, Chen Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350321951

Most of the existing text matching models are based on the interactive network and use cross-attention mechanism to capture the semantic links between two texts, and have made outstanding achievements. However, the current methods generally do not pay enough attention to the context information of the text and ignore the importance of comparing these features. To solve this problem, this paper proposes an interactive context-comparative model for text matching (ICCM). First, the Bi-directional Long Short-Term Memory network is used to encode the word vector. then the key information of related words between different texts is aligned through the cross-attention mechanism, and the key information in their contexts is focused through the context-attention mechanism. the two kinds of information are compared using comparison functions, and the convolutional neural network is used to aggregate the features after splicing. Finally, the results of text matching are predicted. Testing on LCQMC dataset shows that the accuracy rate is 85.5%. the experimental result and analysis prove that the proposed model is superior to the existing main text matching methods and has certain effectiveness. © 2022 IEEE.

关键词： Semantics

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Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning

Multi-Modal Mutual Information (MuMMI) Training for Robust S...

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IEEE international conference on robotics and automation (ICRA)

作者： Chen, Kaiqi Lee, Yong Soh, Harold Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (纸本)9781728190778

this work focuses on learning useful and robust deep world models using multiple, possibly unreliable, sensors. We find that current methods do not sufficiently encourage a shared representation between modalities;this can cause poor performance on downstream tasks and over-reliance on specific sensors. As a solution, we contribute a new multi-modal deep latent stale-space model, trained using a mutual information lower-bound. the key innovation is a specially-designed density ratio estimator that encourages consistency between the latent codes of each modality. We tasked our method to learn policies (in a self-supervised manner) on multi-modal Natural MuJoCo benchmarks and a challenging Table Wiping task. Experiments show our method significantly outperforms state-of-the-art deep reinforcement learning methods, particularly in the presence of missing observations.

关键词： Training Technological innovation conferences Reinforcement learning Robot sensing systems Sensors State-space methods

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AI-Based Finite-Element Simulation automation Workflow Applied to Automotive Electronics Field

AI-Based Finite-Element Simulation Automation Workflow Appli...

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international conference on (ICEPT) Electronic Packaging Technology

作者： Yinlong Wang Pei Xie Engineering Technology China & Digitalization Bosch Automotive Products (Suzhou) Co. Ltd. City Suzhou China

ISBN: (数字)9798350353808

ISBN: (纸本)9798350353815

Electronic component is one of the key design elements considered during the development of automotive ECU (Electronic Control Unit). As the current state-of-the-art method, FEM (Finite-Element Method) simulation is widely used for thermal design optimization and solder joint reliability assessment of electronics at early stage of ECU development. To address the challenges of modeling fidelity and efficiency, the AI (Artificial Intelligence) integrated automated simulation workflow platform is developed. Leveraging standard and Bosch in-house developed Python libraries called “pyact”, an Ansys-based automation toolset has been developed which accelerates the simulation workflow. With the assistance of AI methodologies, more precise electronic component models could be expected during calibration process. this paper presents 3 different industrial practices and examples focusing on the board level thermal management and solder joint reliability assessment. the first industrial example presents the standardization and automatization of the reliability simulation workflow of chip-resistor components. Utilizing Ansys ACT (Application Customization Toolkit), basic APIs (Application Programming Interface) of ACT are encapsulated into the Python library as pyact, enabling a fully automated simulation workflow to predicted solder joint lifetime. To further realize the fast assessment of reliability and component virtual release, an AI metamodel is derived from automated simulation-based DoE (Design of Experiments). For pursuit of accuracy for simulation models, the second industrial example presents the combination of Bayesian Optimization with BGA (Ball Grid Array) warpage calibration process to outperform the traditional manual methods. the third industrial example outlines another application of Bayesian optimization into the Zth (Transient thermal Impedance) calibration process for component thermal models. Utilizing the multi-objective optimization algorithm, the Zth c

关键词： thermal management of electronics automation Reliability engineering Calibration Finite element analysis Bayes methods Artificial intelligence

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Underactuated Ship Heading Control in Harbor Based on Model-Free Adaptive Sliding Mode Control

Underactuated Ship Heading Control in Harbor Based on Model-...

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

作者： Wei Li Junqing Han Shuai Fang Yazhen Du Yang Cao Jiujiang Branch Tianjin Navigation Instruments Research Institute Tianjin China Maritime College Tianjin University of Technology Tianjin China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

To address the challenges posed by restricted waters, uncertain model parameters, and strong environmental disturbances such as wind and currents during the harbor navigation of underactuated ships, this paper introduces a method for controlling ship heading using partial dynamic linearization, which operates without relying on any model information. Firstly, a partial dynamic linearization model is developed using the system's input-output data, and then the model is adjusted online with changing navigation conditions through pseudo-partial derivative estimation. Additionally, conventional model-free adaptive control methods applied directly to ship heading control in the body coordinate system can result in non-convergent control errors. To solve this problem, this paper introduces a model-free adaptive sliding mode control method. Finally, MATLAB simulation tests and a comparative analysis with the traditional model-free PID control method demonstrate the effectiveness of the proposed control method and its strong robustness against environmental disturbances.

关键词： Adaptation models PI control Navigation Service robots Mathematical models Data models PD control Marine vehicles Adaptive control Sliding mode control

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A Calibration Method based on Heteroscedastic Evolutionary Bayesian Optimization for Simulation System of HVAC

A Calibration Method based on Heteroscedastic Evolutionary B...

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robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Zhe Tan Bo Liu School of Automation Wuhan University of Technology Wuhan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

In recent years, increasing the energy efficiency of buildings has become one of the objectives of facility managers. Advanced control methods can be used to improve the efficiency of Heating, Ventilation, and Air Conditioning (HVAC). However, advanced control methods cannot be effectively applied due to inconvenient commissioning. therefore, simulating and modeling the HVAC system becomes particularly important for verifying various control algorithms. Modeling and calibrating the HVAC simulation system becomes crucial for the application of various control strategies based on actual operational data, while current calibration methods are usually based on experience. In this research, a method for the calibration of an HVAC model based on Heteroscedastic Evolutionary Bayesian Optimization (HEBO) is presented. Firstly, the HVAC model is built using the Modelica language in the Dymola environment. Secondly, real operation and weather data are fed to the HVAC model to predict the energy consumption of various equipment. thirdly, the Root Mean Squared Error (RMSE) between predicted and real values is minimized by HEBO so that the optimal parameters of the equipment can be obtained. Finally, a case study is conducted to verify the effectiveness of the proposed method. the results show that the CV(RMSE) of calibrated models meets the guideline requirements of ASHRAE, IPMVP, and FEMP.

关键词： HVAC Atmospheric modeling Buildings Predictive models Ventilation Data models Calibration Bayes methods Optimization Robots

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