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Control Algorithm of Two-Wheeled Mobile Robot Based on Active Disturbance Rejection Control

Control Algorithm of Two-Wheeled Mobile Robot Based on Activ...

引用

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

作者： Siyi Tang Lijie Wang Hewei Zhang Liguo Tan School of Electrical Information Engineering Northeast Petroleum University Daqing China Institute of Space Environment and Physical Science Harbin Institute of Technology Harbin China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

For the complex multi-input multi-output two-wheeled mobile robot(TWR) modeled, after the dynamics analysis found that there is a coupling between the state variables. When decoupling control using PID, although it can achieve a high degree of accuracy, there is still a more obvious coupling phenomenon between the state variables. Aiming at the problem of strong coupling of state variables and loss of control effect, it is proposed to design a controller for the TWR system using an active disturbance rejection control (ADRC) algorithm, which can reduce the coupling degree by the extended state observer (ESO), so as to improve the control performance of the robot. Firstly, a reasonably simplified TWR dynamics model is analyzed, and a reasonable controller is designed and proved to be stable. Then, based on ADAMS View and MATLAB/Simulink, a TWR control simulation model is built, balance, steering and self-stabilization control experiments are simulated. Simulation results of the ADRC control are compared with those using PID. The results show that the balance control of the ADRC improves the RMSE by 87.4% and significantly improves the coupling state. Besides, ADRC has a smaller range of fluctuation during system oscillations, which significantly improves the performance of the TWR with high robustness.

关键词： Couplings Robust control Analytical models Accuracy Stability criteria Mathematical models Robustness Mobile robots State estimation Oscillators

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A Gaze-Speech System in Mixed Reality for Human-Robot Interaction

A Gaze-Speech System in Mixed Reality for Human-Robot Intera...

引用

IEEE international conference on robotics and automation (ICRA)

作者： John David Prieto Prada Myung Ho Lee Cheol Song Department of Robotics and Mechatronics Engineering DGIST Daegu South Korea

Human-robot interaction (HRI) demands efficient time performance along the tasks. However, some interaction approaches may extend the time to complete such tasks. Thus, the time performance in HRI must be enhanced. This work presents an effective way to enhance the time performance in HRI tasks with a mixed reality (MR) method based on a gaze-speech system. In this paper, we design an MR world for pick-and-place tasks. The hardware system includes an MR headset, the Baxter robot, a table, and six cubes. In addition, the holographic MR scenario offers two modes of interaction: gesture mode (GM) and gaze-speech mode (GSM). The input actions during the GM and GSM methods are based on the pinch gesture and gaze with speech commands, respectively. The proposed GSM approach can improve the time performance in pick-and-place scenarios. The GSM system is 21.33 % faster than the traditional system, GM. Also, we evaluated the target- to-target time performance against a reference based on Fitts' law. Our findings show a promising method for time reduction in HRI tasks through MR environments.

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IDD-X: A Multi-View Dataset for Ego-relative Important Object Localization and Explanation in Dense and Unstructured Traffic

IDD-X: A Multi-View Dataset for Ego-relative Important Objec...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Chirag Parikh Rohit Saluja C. V. Jawahar Ravi Kiran Sarvadevabhatla Centre for Visual Information Technology IIIT Hyderabad India Department of Computer Science IIT Mandi Mandi India

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Intelligent vehicle systems require a deep understanding of the interplay between road conditions, surrounding entities, and the ego vehicle’s driving behavior for safe and efficient navigation. This is particularly critical in developing countries where traffic situations are often dense and unstructured with heterogeneous road occupants. Existing datasets, predominantly geared towards structured and sparse traffic scenarios, fall short of capturing the complexity of driving in such environments. To fill this gap, we present IDD-X, a large-scale dual-view driving video dataset. With 697K bounding boxes, 9K important object tracks, and 1-12 objects per video, IDD-X offers comprehensive ego-relative annotations for multiple important road objects covering 10 categories and 19 explanation label categories. The dataset also incorporates rearview information to provide a more complete representation of the driving environment. We also introduce custom-designed deep networks aimed at multiple important object localization and per-object explanation prediction. Overall, our dataset and introduced prediction models form the foundation for studying how road conditions and surrounding entities affect driving behavior in complex traffic situations.

关键词： Location awareness Navigation Intelligent vehicles Annotations Roads Predictive models Developing countries

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Efficient Planar Pose Estimation via UWB Measurements

Efficient Planar Pose Estimation via UWB Measurements

引用

IEEE international conference on robotics and automation (ICRA)

作者： Haodong Jiang Wentao Wang Yuan Shen Xinghan Li Xiaoqiang Ren Biqiang Mu Junfeng Wu Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) School of Data Science the Chinese University of HongKong Shenzhen P. R. China College of Control Science and Engineering and the State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou P. R. China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing P. R. China School of Mechatronic Engineering and Automation Shanghai University Shanghai P. R. China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing P. R. China

State estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband (UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics treat UWB as a stand-alone state estimation solution. The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements. We prove the excellent property of a two-step scheme, which says we can refine a consistent estimator to be asymptotically efficient by one step of Gauss-Newton iteration. Grounded on this result, we design the GN-ULS estimator, which reduces the computation time significantly compared to previous methods and presents the possibility of using only UWB for real-time state estimation.

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Task-Space Clustering for Mobile Manipulator Task Sequencing

Task-Space Clustering for Mobile Manipulator Task Sequencing

引用

IEEE international conference on robotics and automation (ICRA)

作者： Quang-Nam Nguyen Nicholas Adrian Quang-Cuong Pham Singapore Centre for 3D Printing (SC3DP) Nanyang Technological University (NTU) Singapore HP-NTU Digital Manufacturing Corporate Lab Nanyang Technological University (NTU) Singapore Eureka Robotics Singapore

Mobile manipulators have gained attention for the potential in performing large-scale tasks which are beyond the reach of fixed-base manipulators. The Robotic Task Sequencing Problem for mobile manipulators often requires optimizing the motion sequence of the robot to visit multiple targets while reducing the number of base placements. A two-step approach to this problem is clustering the task-space into clusters of targets before sequencing the robot motion. In this paper, we propose a task-space clustering method which formulates the clustering step as a Set Cover Problem using bipartite graph and reachability analysis, then solves it to obtain the minimum number of target clusters with corresponding base placements. We demonstrated the practical usage of our method in a mobile drilling experiment containing hundreds of targets. Multiple simulations were conducted to benchmark the algorithm and also showed that our proposed method found, in practical time, better solutions than the existing state-of-the-art methods.

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Letter Identification in EEG Signals Using Scalograms

Letter Identification in EEG Signals Using Scalograms

引用

IEEE international conference on automation, Quality and Testing, robotics, AQTR

作者： Rareș Țânț Vlad Mihaly Department of Automation Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798350361933

ISBN: (纸本)9798350361940

Considering the improvements in Brain-Computer interface (BCI) systems and their cost reduction, a new chance for a normal life appeared for people with a wide range of disabilities. With the rise of artificial neural networks (ANN), traditional machine learning (ML) techniques heavily decreased in popularity. These methods are usually a good alternative and, also, an easier-to-understand way to get into data science in plenty of domains. Considering the use of scalograms as training material, the current paper proposes the utilization of classical ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and k-Nearest Neighbors (KNN). The images are obtained by transforming the electroencephalogram (EEG) signals using continuous wavelet transform (CWT) in order to show their strengths in terms of learning. To illustrate the advantages of the proposed approach, a comparison of the obtained results with those obtained from training on EEG signals directly has been performed. Moreover, another comparison has been made between the results obtained with a neural network-based solution applied to both representations of the training data.

关键词： Training Support vector machines Continuous wavelet transforms Training data Artificial neural networks Electroencephalography Brain-computer interfaces

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Semantic Editing Increment Benefits Zero-Shot Composed Image Retrieval 24

Semantic Editing Increment Benefits Zero-Shot Composed Image...

引用

32nd ACM international conference on Multimedia, MM 2024

作者： Yang, Zhenyu Qian, Shengsheng Xue, Dizhan Wu, Jiahong Yang, Fan Dong, Weiming Xu, Changsheng Institute of Automation Cas Beijing China Ucas Beijing China Kuaishou Technology Beijing China Peng Cheng Laboratory Beijing China

ISBN: (纸本)9798400706868

Zero-Shot Composed Image Retrieval (ZS-CIR) has attracted more attention in recent years, focusing on retrieving a specific image based on a query composed of a reference image and a relative text without training samples. Specifically, the relative text describes the differences between the two images. Prevailing ZS-CIR methods employ image-to-text (I2T) models to convert the query image into a single caption, which is further merged with the relative text by text-fusion approaches to form a composed text for retrieval. However, these methods neglect the fact that ZS-CIR entails considering not only the final similarity between the composed text and retrieved images but also the semantic increment during the compositional editing process. To address this limitation, this paper proposes a training-free method called Semantic Editing Increment for ZS-CIR (SEIZE) to retrieve the target image based on the query image and text without training. Firstly, we employ a pre-trained captioning model to generate diverse captions for the reference image and prompt Large Language models (LLMs) to perform breadth compositional reasoning based on these captions and relative text, thereby covering the potential semantics of the target image. Then, we design a semantic editing search to incorporate the semantic editing increment contributed by the relative text into the retrieval process. Concretely, we comprehensively consider relative semantic increment and absolute similarity as the final retrieval score, which is subsequently utilized to retrieve the target image in the CLIP feature space. Extensive experiments on three public datasets demonstrate that our proposed SEIZE achieves the new state-of-the-art performance. The code is publicly available at https://***/yzy-bupt/SEIZE. © 2024 ACM.

关键词： Zero-shot learning

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Visuomotor Control in Multi-Object Scenes Using Object-Aware Representations

Visuomotor Control in Multi-Object Scenes Using Object-Aware...

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IEEE international conference on robotics and automation (ICRA)

作者： Negin Heravi Ayzaan Wahid Corey Lynch Pete Florence Travis Armstrong Jonathan Tompson Pierre Sermanet Jeannette Bohg Debidatta Dwibedi Stanford University Robotics at Google

Perceptual understanding of the scene and the relationship between its different components is important for successful completion of robotic tasks. Representation learning has been shown to be a powerful technique for this, but most of the current methodologies learn task specific representations that do not necessarily transfer well to other tasks. Furthermore, representations learned by supervised methods require large, labeled datasets for each task that are expensive to collect in the real-world. Using self-supervised learning to obtain representations from unlabeled data can mitigate this problem. However, current self-supervised representation learning methods are mostly object agnostic, and we demonstrate that the resulting representations are insufficient for general purpose robotics tasks as they fail to capture the complexity of scenes with many components. In this paper, we show the effectiveness of using object-aware representation learning techniques for robotic tasks. Our self-supervised representations are learned by observing the agent freely interacting with different parts of the environment and are queried in two different settings: (i) policy learning and (ii) object location prediction. We show that our model learns control policies in a sample-efficient manner and outperforms state-of-the-art object agnostic techniques as well as methods trained on raw RGB images. Our results show a 20% increase in performance in low data regimes (1000 trajectories) in policy training using implicit behavioral cloning (IBC). Furthermore, our method outperforms the baselines for the task of object localization in multi-object scenes. Further qualitative results are available at https://***/view/slots4robots.

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Camera Calibration Model Comparison for Marker Position Estimation

Camera Calibration Model Comparison for Marker Position Esti...

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international conference on Autonomous Robots and Agents, ICARA

作者： Shannon Van De Velde Risang Gatot Yudanto Sam Weckx Motions Flanders Make Leuven Belgium

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

In this study, we investigate the impact of different camera calibration models on the accuracy of fiducial marker position estimations. With a perfect calibration, the positions of detected marker corners in the image should precisely match the projections of their physical coordinates onto the image plane. The extent and nature of any divergence serve as crucial indicators of position estimation accuracy. Our findings reveal a symmetrical divergence, corresponding to low position estimation accuracy in the direction perpendicular to the image plane and in the plane an accuracy decrease with the distance from the center. This knowledge is important to be able to account for these errors in practical applications. Future research will aim to develop strategies to correct these errors effectively.

关键词： Accuracy automation Robot kinematics Robot vision systems Estimation Cameras Fiducial markers

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Obstacle Avoidance of Multiple UAVs Based on Reinforcement Learning

Obstacle Avoidance of Multiple UAVs Based on Reinforcement L...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Weiting Mo Jinna Li School of Information and Control Engineering Liaoning Petrochemical University Fushun China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Improving the obstacle avoidance capability of Unmanned Aerial Vehicles (UAVs) is crucial for maintaining their operational safety. UAVs with autonomous driving ability is the development trend of future aircraft. This paper introduces a novel UAV obstacle avoidance approach utilizing the Artificial Potential Field-Dueling Deep Q-Network (APF-Dueling DQN) method. According to the dynamic model of UAVs, the three-dimensional dynamic equation of UAVs is established, and the motion space of UAVs is constructed by combining pitch and heading angles. In order to improve the obstacle avoidance performance of UAVs, an improved Deep Reinforcement Learning (DRL) algorithm is designed, and the algorithm is used to improve the reward potential function of DQN algorithm. Simulation studies demonstrate that the APF-Dueling DQN approach surpasses the traditional DQN in performance, exhibiting robustness against local minima and yielding efficient, smooth flight paths. This underscores the efficacy of the APF-Dueling DQN in addressing UAV path planning problem.

关键词： Solid modeling Heuristic algorithms Dynamics Autonomous aerial vehicles Mathematical models Robustness Safety Collision avoidance Vehicle dynamics Robots

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