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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24622 条记录，以下是251-260 订阅

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Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection 39

Cluttered Food Grasping with Adaptive Fingers and Synthetic-...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Ummadisingu, Avinash Takahashi, Kuniyuki Fukaya, Naoki Preferred Networks Inc Tokyo Japan

ISBN: (纸本)9781728196817

The food packaging industry handles an immense variety of food products with wide-ranging shapes and sizes, even within one kind of food. Menus are also diverse and change frequently, making automation of pick-and-place difficult. A popular approach to bin-picking is to first identify each piece of food in the tray by using an instance segmentation method. However, human annotations to train these methods are unreliable and error-prone since foods are packed close together with unclear boundaries and visual similarity making separation of pieces difficult. To address this problem, we propose a method that trains purely on synthetic data and successfully transfers to the real world using sim2real methods by creating datasets of filled food trays using high-quality 3d models of real pieces of food for the training instance segmentation models. Another concern is that foods are easily damaged during grasping. We address this by introducing two additional methods- a novel adaptive finger mechanism to passively retract when a collision occurs, and a method to filter grasps that are likely to cause damage to neighbouring pieces of food during a grasp. We demonstrate the effectiveness of the proposed method on several kinds of real foods.

关键词： Training Solid modeling Adaptation models Visualization Three-dimensional displays automation Filtering

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Towards Robust Perception for Assistive robotics: An RGB-Event-LiDAR Dataset and Multi-Modal Detection Pipeline 20

Towards Robust Perception for Assistive Robotics: An RGB-Eve...

引用

IEEE 20th international conference on automation Science and Engineering (CASE)

作者： Scicluna, Adam Le Gentil, Cedric Sutjipto, Sheila Paul, Gavin Univ Technol Sydney UTS Fac Engn & Informat Technol Robot Inst Sydney NSW Australia

ISBN: (纸本)9798350358513;9798350358520

The increasing adoption of human-robot interaction presents opportunities for technology to positively impact lives, particularly those with visual impairments, through applications such as guide-dog-like assistive robotics. We present a pipeline exploring the perception and "intelligent disobedience" required by such a system. A dataset of two people moving in and out of view has been prepared to compare RGB-based and event-based multi-modal dynamic object detection using LiDAR data for 3D position localisation. Our analysis highlights challenges in accurate 3D localisation using 2D image-LiDAR fusion, indicating the need for further refinement. Compared to the performance of the frame-based detection algorithm utilised (YOLOv4), current cutting-edge event-based detection models appear limited to contextual scenarios, such as for automotive platforms. This is highlighted by weak precision and recall over varying confidence and Intersection over Union (IoU) thresholds when using frame-based detections as a ground truth. Therefore, we have publicly released this dataset to the community, containing RGB, event, point cloud and Inertial Measurement Unit (IMU) data along with ground truth poses for the two people in the scene to fill a gap in the current landscape of publicly available datasets and provide a means to assist in the development of safer and more robust algorithms in the future: https://***/revel/.

关键词： Human robot interaction

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A Performance Optimization Strategy Based on Improved NSGA-II for a Flexible Robotic Fish

A Performance Optimization Strategy Based on Improved NSGA-I...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Lu, Ben Wang, Jian Liao, Xiaocun Zou, Qianqian Tan, Min Zhou, Chao Chinese Acad Sci Inst Automat Lab Cognit & Decis Intelligence Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9798350323658

The high speed and low energy cost are two conflicting objectives in the motion optimization of bio-inspired underwater robots, but playing a very important role. To this end, this paper proposes an optimization strategy for swimming speed and power cost using an improved NSGA-II for a flexible robotic fish. A dynamic model involving flexible deformation is established for speed prediction with the hydrodynamic parameters identified. A back propagation (BP) neural network is applied to perform compensation of power cost prediction with the dynamic model's prediction as input. In particular, an NSGA-II-AMS method is developed to improve the efficiency of solving the two-objective optimization problem based on NSGA-II. Finally, extensive simulations and experimental results demonstrate the effectiveness of the proposed optimization strategy, which offers promising prospects for the flexible robotic fish performing aquatic tasks with different performance constraints.

关键词： Dynamic models

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Servo system Cascade Linear Active Disturbance Rejection Control Based on Motor models 4

Servo system Cascade Linear Active Disturbance Rejection Con...

引用

4th international conference on Industrial automation, robotics and Control Engineering, IARCE 2024

作者： He, Yuang Zheng, Qijia Liu, Jialun Wang, Yan Fan, Qian Beijing Institute of Precision Mechatronics and Controls Innovation Center for Control Actuators Beijing China

ISBN: (纸本)9798350380323

The electromechanical servo system is usually implemented by three closed loops, with the inner loop being the current loop, the middle loop being the speed loop, and the outer loop being the position loop. The traditional system tuning method is the PI parameter trial and error method, which cannot achieve the optimal performance of the system. This article innovatively adopts the LADRC three closed-loop cascade control method. Compared with previous control methods, the control method applied in this article can ensure that the current loop, speed loop, and position loop control circuits all meet the requirements of the Bode theorem, which provides a basis for the control objectives. This article provides a detailed description of the selection methods for the control order, observer bandwidth, controller bandwidth, and loop gain of each control loop. This method was validated and ultimately achieved stable control of the electromechanical servo system. © 2024 IEEE.

关键词： Disturbance rejection

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Mobility Analysis of Screw-Based Locomotion and Propulsion in Various Media

Mobility Analysis of Screw-Based Locomotion and Propulsion i...

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IEEE international conference on robotics and automation (ICRA)

作者： Lim, Jason Joyce, Calvin Peiros, Elizabeth Yeoh, Mingwei Gavrilov, Peter V. Wickenhiser, Sara G. Schreiber, Dimitri A. Richter, Florian Yip, Michael C. Univ Calif San Diego Elect & Comp Engn Dept La Jolla CA 92093 USA Univ Calif nia San Diego Mech & Aerosp Engn Dept La Jolla CA 92093 USA

ISBN: (纸本)9798350323658

Robots "in-the-wild" encounter and must traverse widely varying terrain, ranging from solid ground to granular materials like sand to full liquids. Numerous approaches exist, including wheeled and legged robots, each excelling in specific domains. Screw-based locomotion is a promising approach for multi-domain mobility, leveraged in exploratory robotic designs, including amphibious vehicles and snake robotics. However, unlike other forms of locomotion, there is limited exploration of the models, parameter effects, and efficiency for multi-terrain Archimedes screw locomotion. In this work, we present work towards this missing component in understanding screw-based locomotion: comprehensive experimental results and performance analysis across different media. We designed a mobile test bed for indoor and outdoor experimentation to collect this data. Beyond quantitatively showing the multidomain mobility of screw-based locomotion, we envision future researchers and engineers using the presented results to design effective screw-based locomotion systems.

关键词： robotics

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Operating with Inaccurate models by Integrating Control-Level Discrepancy Information into Planning

Operating with Inaccurate Models by Integrating Control-Leve...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Rainer, Ellis Tomlin, Claire J. Likhachev, Maxim Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94704 USA Carnegie Mellon Univ Inst Robot Pittsburgh PA USA

ISBN: (纸本)9798350323658

Typical robotic systems rely on models for planning. Therefore, the quality of the robot's behavior is heavily dependent on how accurately the model can predict the outcome of the robot's actions in the environment. A challenge, however, is that no model is perfect;moreover, we often do not know where discrepancies between the model's prediction and the actual outcome occur prior to observing executions in the real-world. One way to address this is to bias the planner away from these discrepancies by inflating the cost of states and actions where we previously observed the model to be inaccurate. Making such decisions about where and how to bias purely at the planning-level, however, neglects valuable information from the control-level, which gives a more fine-grained understanding of where and how the model went wrong during execution. Based on this observation, our key idea is to first infer a statistical model over discrepancies in the control-level's model. Then, we translate this model to the planning-level, where we use it to more informatively bias the planner away from states and actions where the model's predicted outcome is likely to be inaccurate. We demonstrate that our framework enables a robot to complete tasks, despite an inaccurate planning model, with greater efficiency than existing approaches. We do so through an experimental evaluation in simulation and real-robot experiments on NASA's Astrobee free-flyer.

关键词： NASA

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Unbiased Scene Graph Generation via Visual Language models 24

Unbiased Scene Graph Generation via Visual Language Models

引用

24th international conference on Control, automation and Systems

作者： Kim, Eunseo Park, Han-Mu Korea Elect Technol Inst Artificial Intelligence Res Ctr Seoul 13488 South Korea

ISBN: (纸本)9798331517939;9788993215380

Scene graph generation becomes significantly important as it bridges the gap between linguistic and visual information of scenes, facilitating a high-dimensional understanding of scenes. In this paper, we analyze the limitations of current scene graph generation methods induced by the inherent semantic relationship biases embedded in existing datasets. Furthermore, we propose a method to enhance scene graph by leveraging visual language models (VLMs). This approach leverages the strengths of VLMs in understanding and generating triplets with semantic predicates, ensuring unbiased and fine-grained scene graphs.

关键词： Scene graph generation Multimodal understanding Large Language models Visual Language models

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Model Identification and Control of a Low-cost Mobile Robot with Omnidirectional Wheels using Differentiable Physics 39

Model Identification and Control of a Low-cost Mobile Robot ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Granados, Edgar Boularias, Abdeslam Bekris, Kostas Aanjaneya, Mridul Rutgers State Univ Dept Comp Sci New Brunswick NJ 08901 USA

ISBN: (纸本)9781728196817

We present a new data-driven technique for predicting the motion of a low-cost omnidirectional mobile robot under the influence of motor torques and friction forces. Our method utilizes a novel differentiable physics engine for analytically computing the gradient of the deviation between predicted motion trajectories and real-world trajectories. This allows to automatically learn and fine-tune the unknown friction coefficients on-the-fly, by minimizing a carefully designed loss function using gradient descent. Experiments show that the predicted trajectories are in excellent agreement with their real-world counterparts. Our proposed approach is computationally superior to existing black-box optimization methods, requiring very few real-world samples for accurate trajectory prediction compared to physics-agnostic techniques, such as neural networks. Experiments also demonstrate that the proposed method allows the robot to quickly adapt to changes in the terrain. Our proposed approach combines the data-efficiency of classical analytical models that are derived from first principles, with the flexibility of data-driven methods, which makes it appropriate for low-cost mobile robots. Project website: https://***/mqxn2x6h

关键词： Analytical models Adaptation models automation Friction Wheels Optimization methods Trajectory

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EILLM: efficient imitate with large language models

EILLM: efficient imitate with large language models

引用

2024 international conference on automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Qiu, Minghao Zhuo, Hankz Hankui School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781510682597

Rapid evolution is underway in the fields of nature language processing and robotics. Our research endeavors to integrate Large Language models (LLMs) with imitation learning, aiming to enhance the efficiency of action generation in robotics. The proposed model, Efficient Imitate with Large Language models (EILLM), utilizes the vast knowledge of LLMs, thus improving the limitations that have traditionally plagued imitation learning approaches, which often falter when confronted with limited expert data. Employing a multi-pronged approach, EILLM adopts LLMs for task understanding, state-action pair analysis, and Monte Carlo search tree pruning, thereby simultaneously raising the accuracy of action generation and efficiency of search process. Furthermore, our experiments demonstrated the superior performance of EILLM compared to baseline models, corroborating its efficacy in achieving sample-efficient robot motion by integrating LLMs and imitation learning. © 2024 SPIE.

关键词： Intelligent systems

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Comparative Analysis of Off-the-Shelf methods for Energy Consumption Anomaly Detection 10

Comparative Analysis of Off-the-Shelf Methods for Energy Con...

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10th international conference on automation, robotics, and Applications, ICARA 2024

作者： Haddad, Hatem Jerbi, Feres Smaali, Issam Wattnow Tunis Tunisia

ISBN: (纸本)9798350394245

Research in Deep Neural Networks (DNNs) has gained significant attention from industries and academia achieving unprecedented success. However, DNNs need large-sized datasets and high computation times. In the field of energy consumption anomaly detection in buildings, DNNs methods were recently applied. Nevertheless, It's state of the art main issue is the lack of platforms (Datasets and Frameworks) for reproducing existing solutions results. This may hinder performances comparison between existing off-the-shelf methods. In this paper, we conduct an empirical comparison by benchmarking 15 Off-the-shelf supervised, semi-supervised and unsupervised methods. We conducted extensive experiments to evaluate their Accuracy and F1 score performances, in addition to their training time and inference time. Experiments were conducted on three publicly available annotated datasets. Preliminary results show that Bagging and Boosting methods achieve better performances compared to Neural Networks methods and require less training and inference computation time. © 2024 IEEE.

关键词： Energy utilization

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