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

共 24815 条记录，以下是4141-4150 订阅

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Shared Control of Robot-Robot Collaborative Lifting with Agent Postural and Force Ergonomic Optimization

Shared Control of Robot-Robot Collaborative Lifting with Age...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Rapetti, Lorenzo Tirupachuri, Yeshasvi Ranavolo, Alberto Kawakami, Tomohiro Yoshiike, Takahide Pucci, Daniele Ist Italiano Tecnol Ctr Robot Technol Dynam Interact Control Genoa Italy Univ Manchester Machine Learning & Optimisat Manchester Lancs England INAIL Dept Occupat & Environm Med Epidemiol & Hyg Rome Italy Honda Res & Dev Co Ltd Saitama Japan

ISBN: (纸本)9781728190778

Humans show specialized strategies for efficient collaboration. Transferring similar strategies to humanoid robots can improve their capability to interact with other agents, leading the way to complex collaborative scenarios with multiple agents acting on a shared environment. In this paper we present a control framework for robot-robot collaborative lifting. The proposed shared controller takes into account the joint action of both the robots thanks to a centralized controller that communicates with them, and solves the whole-system optimization. Efficient collaboration is ensured by taking into account the ergonomic requirements of the robots through the optimization of posture and contact forces. The framework is validated in an experimental scenario with two iCub humanoid robots performing different payload lifting sequences.

关键词： Smoothing methods Ergonomics Force Collaboration Humanoid robots Grasping Torque measurement

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Research on energy saving of hydraulic system of hydraulic brick press 2

Research on energy saving of hydraulic system of hydraulic b...

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2nd international conference on Artificial Intelligence, automation, and High-Performance Computing, AIAHPC 2022

作者： Zheng, HongBo Li, MingJun Wang, ZhouLin Yang, JunHao College of Robotics Guangdong Polytechnic of Science and Technology Guangdong Zhuhai519090 China

ISBN: (纸本)9781510657717

In view of the problem of high energy consumption of ordinary hydraulic brick presses, the scheme of energy-saving transformation is proposed to replace the asynchronous motor with AC permanent magnet synchronous servo motor. The simulation models of the ordinary hydraulic brick press and the reformed servo direct drive pump-controlled hydraulic brick press are established in AMESim, and the energy consumption simulation and analysis are carried out. The results show that the modified servo direct drive pump-controlled hydraulic brick press is 20.7% more energy-saving than before the transformation. The reasons for energy saving lies in that, compared with the ordinary hydraulic brick press, the servo direct drive pump-controlled hydraulic brick press saves overflow loss in the pressure holding stage and reduces the dormant loss in the dormant stage. © 2022 SPIE. All rights reserved.

关键词： Brick

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Learn to Path: Using neural networks to predict Dubins path characteristics for aerial vehicles in wind

Learn to Path: Using neural networks to predict Dubins path ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Phillips, Trevor Stolzle, Maximilian Turricelli, Erick Achermann, Florian Lawrance, Nicholas Siegwart, Roland Chung, Jen Jen Swiss Fed Inst Technol Autonomous Syst Lab CH-8092 Zurich Switzerland

ISBN: (纸本)9781728190778

For asymptotically optimal sampling-based path planners such as RRT*, path quality improves as the number of samples added to the motion tree increases. However, each additional sample requires a nearest-neighbor search. Calculating state transition costs can be particularly difficult in cases with complex dynamics such as aerial vehicles in non-isotropic cost fields like wind. Computationally costly nearest neighbor searches increase the time required to add new samples to the search tree, thereby reducing the likelihood of finding low-cost paths in a given computational time. In this paper, we propose the use of a lightweight neural network to approximate nearest neighbor cost calculations. The network approach uses a low-dimensional encoding of the cost space along with a start and goal query pair and returns an estimate of the path cost that can be used for nearest neighbor and path validity estimation. We demonstrate our method for a Dubins airplane model in a 3D wind field and show that the network method achieves equivalent path lengths as an existing iterative solver 32 % faster and, when given the same search time, up to 10.8% shorter.

关键词： Airplanes Solid modeling Costs Three-dimensional displays Neural networks Estimation Nearest neighbor methods

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Lyapunov-Based Kinematic Anti-Prance Control for a Modular Rimless-Wheel Exploration Rover

Lyapunov-Based Kinematic Anti-Prance Control for a Modular R...

引用

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

作者： Walter Schindler German Aerospace Center (DLR) Institute of System Dynamics and Control Weßling Germany

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

The Scout rover is a novel rover concept developed by the German Aerospace Center (DLR) for the exploration of extreme extraterrestrial environments. Its locomotion platform is designed to tackle rough and rugged terrain, rock and boulder traverses, craters and cave systems on foreign celestial bodies. The Scout features a modular design with a compliant rover backbone and elastic rimless wheels. The backbone consists of rigid segments which are coupled by elastic vertebrae in-between. Under unfavorable conditions, the backbone deflects vertically, individual segments rear up and the rover begins to prance. Unintended backbone deformation and rearing-up negatively affect rover locomotion capabilities. This contribution presents a robust kinematic chassis controller for monitoring and controlling the backbone configuration. The backbone deformation is generically modeled upon an elementary backbone unit based on the nonlinear beam theory and a kinematic surrogate model. Lyapunov theory is used to synthesize a backbone controller upon the surrogate model for the elementary backbone unit. A kinematic chassis controller is established upon the elementary backbone controller in order to reduce unintended backbone deformations on chassis level. The overall controller is verified by means of test scenarios on different terrains on the Scout outdoor testbed. The controller significantly reduces backbone deformation on rough terrains.

关键词： Deformable models automation Deformation Wheels Kinematics Rocks Robots Monitoring

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One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video

One to Many: Adaptive Instrument Segmentation via Meta Learn...

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IEEE international conference on robotics and automation (ICRA)

作者： Zhao, Zixu Jin, Yueming Lu, Bo Ng, Chi-Fai Dou, Qi Liu, Yun-Hui Heng, Pheng-Ann Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Chinese Univ Hong Kong T Stone Robot Inst Dept Mech & Automat Engn Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Hong Kong Macao Joint Lab Human Machine Shenzhen Peoples R China Chinese Univ Hong Kong Dept Surg Hong Kong Peoples R China

ISBN: (纸本)9781728190778

Surgical instrument segmentation in robot-assisted surgery (RAS) - especially that using learning-based models - relies on the assumption that training and testing videos are sampled from the same domain. However, it is impractical and expensive to collect and annotate sufficient data from every new domain. To greatly increase the label efficiency, we explore a new problem, i.e., adaptive instrument segmentation, which is to effectively adapt one source model to new robotic surgical videos from multiple target domains, only given the annotated instruments in the first frame. We propose MDAL, a meta-learning based dynamic online adaptive learning scheme with a two-stage framework to fast adapt the model parameters on the first frame and partial subsequent frames while predicting the results. MDAL, learns the general knowledge of instruments and the fast adaptation ability through the video-specific meta-learning paradigm. The added gradient gale excludes the noisy supervision from pseudo masks for dynamic online adaptation on target videos. We demonstrate empirically that MDAL outperforms other state-of-the-art methods on two datasets (including a real-world RAS dataset). The promising performance on ex-vivo scenes also benefits the downstream tasks such as robot-assisted suturing and camera control.

关键词： Surgical instrument segmentation meta learning in robotics online adaptation robotic surgical video

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Enhancing Autonomous Robot Safety through Localization Performance Analysis

Enhancing Autonomous Robot Safety through Localization Perfo...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Ulaş Süreyya Bingöl Akif Hacınecipoğlu Mustafa Mert Ankaralı Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Milvus Robotics Ankara Turkey Center for Robotics & AI Middle East Technical University Ankara Turkey

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Autonomous robots play a vital role in various applications, including industrial automation and logistics. Guaranteeing their safe and dependable operation is of utmost importance, particularly in human-robot collaborative scenarios. While existing safety standards address numerous aspects of autonomous systems, the persisting challenges linked to localization issues demand attention. Accurate localization is paramount for safety in autonomous robotic systems due to its direct impact on preventing accidents, mitigating risks, and ensuring human well-being. In scenarios where robots operate alongside humans, such as collaborative workspaces or shared environments, precise localization is crucial to avoid collisions and potential harm to individuals. Furthermore, robust localization enhances system dependability by minimizing errors and ensuring consistent performance, vital for industrial automation and logistics where disruptions can lead to costly downtime. To proactively manage these risks and ensure uninterrupted operations, this paper introduces a novel solution: the Localization Performance Analyzer (LPA). We present an architecture for estimating localization performance and a set of innovative features that serve as predictors of localization quality. Our findings indicate that implementing the LPA algorithm in a fleet of robots leads to more accurate estimations of localization performance. Our results from ablation studies demonstrate that integrating the LPA algorithm into a fleet of robots significantly enhances the accuracy of localization performance estimation. The predictor model and trainer is available at https://***/ulassbin/localization_performance_analyser.

关键词： Location awareness Measurement automation Accuracy Service robots Predictive models Feature extraction Prediction algorithms Safety Logistics

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IMU/Vehicle Calibration and Integrated Localization for Autonomous Driving

IMU/Vehicle Calibration and Integrated Localization for Auto...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Liu, Zhenbo Wang, Leijie Wen, Feng Zhang, Hongbo Noahs Ark Lab Beijing 100085 Peoples R China

ISBN: (纸本)9781728190778

The localization system, which outputs vehicle position, velocity, and attitude, is one of the fundamental components in the autonomous driving vehicle. The global pose is not only used for the planning and control system, but also an important reference for the cloud source-based HD Map building and updating. The accuracy, availability, and reliability are key requirements for the localization system to ensure that the whole system runs smoothly and efficiently. IMU/Vehicle extrinsic calibration is one of the primary jobs that should be addressed. Due to the observability issue, the IMU/vehicle relative roll cannot be calibrated by the traditional maneuver-based calibration method. In this paper, we solve this issue with the proposed Multiple Orientation-based Vehicle/IMU Extrinsic Calibration (MOVIE-Cali) method, which is evaluated by Monte Carlo simulations and experiments. When the vehicle is cornering or making a U-turn, the sideslip of the tires will have negative influence on the localization system which uses Non-Holonomic Constraints (NHC)/Wheel speed sensor measurement in the model. We derive a sideslip angle model and propose an online slip parameter calibration and compensation method to improve the localization accuracy. The performance of proposed method has been evaluated by the vehicle tests.

关键词： Location awareness Monte Carlo methods Estimation Robot sensing systems Tires Calibration Planning

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A Generalized Acquisition Function for Preference-based Reward Learning

A Generalized Acquisition Function for Preference-based Rewa...

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IEEE international conference on robotics and automation (ICRA)

作者： Evan Ellis Gaurav R. Ghosal Stuart J. Russell Anca Dragan Erdem Bıyık Department of Electrical Engineering and Computer Sciences UC Berkeley Machine Learning Department Carnegie Mellon University Thomas Lord Department of Computer Science University of Southern California

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Preference-based reward learning is a popular technique for teaching robots and autonomous systems how a human user wants them to perform a task. Previous works have shown that actively synthesizing preference queries to maximize information gain about the reward function parameters improves data efficiency. The information gain criterion focuses on precisely identifying all parameters of the reward function. This can potentially be wasteful as many parameters may result in the same reward, and many rewards may result in the same behavior in the downstream tasks. Instead, we show that it is possible to optimize for learning the reward function up to a behavioral equivalence class, such as inducing the same ranking over behaviors, distribution over choices, or other related definitions of what makes two rewards similar. We introduce a tractable framework that can capture such definitions of similarity. Our experiments in a synthetic environment, an assistive robotics environment with domain transfer, and a natural language processing problem with real datasets demonstrate the superior performance of our querying method over the state-of-the-art information gain method.

关键词： Measurement Learning systems Autonomous systems Education Artificial neural networks Natural language processing Bayes methods

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A Coupling Enhanced Emergency Braking Approach for Double Pendulum Cranes

A Coupling Enhanced Emergency Braking Approach for Double Pe...

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2022 IEEE international conference on Real-Time Computing and robotics, RCAR 2022

作者： Chen, He Li, Mengyuan Wu, Yinan Hebei University of Technology School of Artificial Intelligence Tianjin300401 China Nankai University Inst. of Robotics and Automat. Info. System and the Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Tianjin300350 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Crane systems are commonly used transportation tools in industry to transport heavy cargoes to corresponding target positions. If the cargo shape is large which could not be regarded as a mass point or the hook mass also needs to be taken into account, the cranes would perform as double pendulum systems, whose dynamics are more complex than those of single pendulum systems. Since the working environment for crane systems can be very complex in practice, unavoidable emergency situations may occur, which are very dangerous. To avoid possible accidents, effective emergency braking methods are urgently needed, especially for double pendulum cranes with large cargoes. Considering this fact, this paper presents a coupling enhanced braking strategy for double pendulum cranes, which stops the trolley fast and achieves double pendulum vibration elimination objectives simultaneously. Specifically, an elaborately designed variable consisting of the trolley movement, the hook's swing, and the payload's swing, is utilized as feedback, which enhances the system couplings and improves the vibration elimination ability. Some constraint items are also introduced to restrict the payload in a safety domain and further avoid possible collisions. The effectiveness is rigorously analyzed and proved by mathematical analysis. Some experiments are executed to show the performance of the designed strategy. © 2022 IEEE.

关键词： Cranes

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Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills

Contextual Latent-Movements Off-Policy Optimization for Robo...

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IEEE international conference on robotics and automation (ICRA)

作者： Tosatto, Samuele Chalvatzaki, Georgia Peters, Jan Tech Univ Darmstadt Intelligent Autonomous Syst Hochschulstr 10 D-64289 Darmstadt Germany

ISBN: (纸本)9781728190778

Parameterized movement primitives have been extensively used for imitation learning of robotic tasks. However, the high-dimensionality of the parameter space hinders the improvement of such primitives in the reinforcement learning (KL) setting, especially for learning with physical robots. In this paper we propose a novel view on handling the demonstrated trajectories for acquiring low-dimensional, non-linear latent dynamics, using mixtures of probabilistic principal component analyzers (MPPCA) on the movements' parameter space. Moreover, we introduce a new contextual off-policy RL algorithm, named LAtent-Movements Policy Optimization (LAMPO). LAMPO can provide gradient estimates from previous experience using self-normalized importance sampling, hence, making full use of samples collected in previous learning iterations. These advantages combined provide a complete framework for sample-efficient off-policy optimization of movement primitives for robot learning of high-dimensional manipulation skills. Our experimental results conducted both in simulation and on a real robot show that LAMPO provides sample-efficient policies against common approaches in literature. Code available at https://github com/StunuelePolimi/Lampo.

关键词： Monte Carlo methods Codes Heuristic algorithms conferences Reinforcement learning Probabilistic logic Robot learning

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