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

共 24812 条记录，以下是4211-4220 订阅

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Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach

Collision Avoidance in Tightly-Constrained Environments with...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Shen, Xu Zhu, Edward L. Sturz, Yvonne R. Borrelli, Francesco Univ Calif Berkeley Dept Mech Engn Berkeley CA 94701 USA

ISBN: (纸本)9781728190778

We present a hierarchical control approach for maneuvering an autonomous vehicle (AV) in tightly-constrained environments where other moving AVs and/or human driven vehicles are present. A two-level hierarchy is proposed: a high-level data-driven strategy predictor and a lower-level model-based feedback controller. The strategy predictor maps an encoding of a dynamic environment to a set of high-level strategies via a neural network. Depending on the selected strategy, a set of time-varying hyperplanes in the AV's position space is generated online and the corresponding halfspace constraints are included in a lower-level model-based receding horizon controller. These strategy-dependent constraints drive the vehicle towards areas where it is likely to remain feasible. Moreover, the predicted strategy also informs switching between a discrete set of policies, which allows for more conservative behavior when prediction confidence is low. We demonstrate the effectiveness of the proposed data-driven hierarchical control framework in a two-car collision avoidance scenario through simulations and experiments on a 1/10 scale autonomous car platform where the strategy-guided approach outperforms a model predictive control baseline in both cases.

关键词： conferences Neural networks Switches Predictive models Encoding Autonomous automobiles Collision avoidance

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Prediction-Based Reachability for Collision Avoidance in Autonomous Driving

Prediction-Based Reachability for Collision Avoidance in Aut...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Li, Anjian Sun, Liting Zhan, Wei Tomizuka, Masayoshi Chen, Mo Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

ISBN: (纸本)9781728190778

Safety is an important topic in autonomous driving since any collision may cause serious injury to people and damage to property. Hamilton-Jacobi (HJ) Reachability is a formal method that verifies safety in multi-agent interaction and provides a safety controller for collision avoidance. However, due to the worst-case assumption on the car's future behaviours, reachability might result in too much conservatism such that the normal operation of the vehicle is badly hindered. In this paper, we leverage the power of trajectory prediction and propose a prediction-based reachability framework to compute safety controllers. Instead of always assuming the worst case, we cluster the car's behaviors into multiple driving modes, e.g. left turn or right turn. Under each mode, a reachability-based safety controller is designed based on a less conservative action set. For online implementation, we first utilize the trajectory prediction and our proposed mode classifier to predict the possible modes, and then deploy the corresponding safety controller. Through simulations in a T-intersection and an 8-way roundabout, we demonstrate that our prediction-based reachability method largely avoids collision between two interacting cars and reduces the conservatism that the safety controller brings to the car's original operation.

关键词： Switches Predictive models Safety Trajectory Planning Automobiles Collision avoidance

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Identification of Mangrove Leaf Disease in Deep Convolutional Neural Network Approach

Identification of Mangrove Leaf Disease in Deep Convolutiona...

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Control & automation, Electronics, robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE international conference on

作者： Romi Fadillah Rahmat Opim Salim Sitompul Maya Silvi Lydia Fahmi Fahmi Raska Almashura Fahrurrozi Lubis Department of Information Technology Universitas Sumatera Utara Medan Indonesia Department of Electrical Engineering Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

Mangrove forests in Indonesia are distributed over many coastal regions, exhibiting significant biodiversity and diverse structural forms. Mangroves serve as a mitigating factor against wave action and intense wind energy resulting from adverse weather conditions. Another purpose of mangroves is to provide as a food source for various marine species. Mangroves consist of many principal components, including roots, stems, leaves, and blooms. For optimal growth, some portions of mangrove plants must be safeguarded from several diseases, whether induced by parasites/pests or by environmental conditions. The leaves of the mangrove are the most vulnerable to disease; yet, pests visible on the leaves are typically inadequately managed, resulting in the plant being immediately removed when all its leaves are infested. This study uses a Deep Convolutional Neural Network to recognize these pests effectively. The image processing methods used in this study include smoothing and sharpening. The study findings indicate that the used approach effectively identifies mangrove leaf diseases, with an accuracy of 99.2%.

关键词： Accuracy Smoothing methods Wind energy Noise Sea measurements Real-time systems Mobile handsets Convolutional neural networks Robots Diseases

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Development and Evaluation of a Machine Learning Model for Cobot Action Prediction: Optimizing Algorithms Through Cognitive Workload Metrics

Development and Evaluation of a Machine Learning Model for C...

引用

international conference on robotics and Artificial Intelligence (ICRAI)

作者： Lala Rukh Yasir Ahmad Uzair Khaleeq Uz Zaman Mohsin Islam Tiwana Afshan Naseem Department of Engineering Management NUST College of Electrical and Mechanical Engineering Islamabad Pakistan Mechanical Engineering Department NUST College of Electrical and Mechanical Engineering Islamabad Pakistan Mechatronics Engineering Department NUST College of Electrical and Mechanical Engineering Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Collaborative robots (Cobots) have replaced traditional industrial robots on the factory floors, enhancing automation and efficiency. From the perspective of Industry 5.0, centering around H.F. (Human Factors), concerns about cognitive ergonomics persist as Cobots' unpredictable actions can increase cognitive workload and reduce productivity. Identifying and prioritising the operators' mental and physical well-being is necessary for safer and more efficient Human Robot Collaboration (HRC). This paper proposes a novel machine-learning model for Cobot action prediction and evaluates which algorithm is best for predicting Cobot actions based on operators' cognitive states. 75 participants with different levels of technophobia, technical expertise, and age collaborated with a Cobot to solve a complex maze puzzle. Firstly, each participant's Cognitive Workload (CWL) was measured using a combination of subjective and objective metrics (NASA-Task Load index, Heart Rate Variability, and Error Rate), followed by the development of a machine learning model that takes human factors as input parameters and predicts Cobot's actions accordingly. This study evaluates various machine learning models for Cobot action prediction and identifies that the Random Forest Classifier outperforms other models with an accuracy of 90%. The model can be implemented in various industrial settings to improve HRC safety and operators' cognitive ergonomics.

关键词： Productivity Service robots Ergonomics Collaborative robots Human factors Predictive models Prediction algorithms Safety Random forests Load modeling

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Gen2Sim: Scaling up Robot Learning in Simulation with Generative models

Gen2Sim: Scaling up Robot Learning in Simulation with Genera...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Pushkal Katara Zhou Xian Katerina Fragkiadaki Carnegie Mellon University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Generalist robot manipulators need to learn a wide variety of manipulation skills across diverse environments. Current robot training pipelines rely on humans to provide kinesthetic demonstrations or to program simulation environments and to code up reward functions for reinforcement learning. Such human involvement is an important bottleneck towards scaling up robot learning across diverse tasks and environments. We propose Generation to Simulation (Gen2Sim), a method for scaling up robot skill learning in simulation by automating generation of 3D assets, task descriptions, task decompositions and reward functions using large pre-trained generative models of language and vision. We generate 3D assets for simulation by lifting open-world 2D object-centric images to 3D using image diffusion models and querying LLMs to determine plausible physics parameters. Given URDF files of generated and human-developed assets, we chain-of-thought prompt LLMs to map these to relevant task descriptions, temporal decompositions, and corresponding python reward functions for reinforcement learning. We show Gen2Sim succeeds in learning policies for diverse long horizon tasks, where reinforcement learning with non temporally decomposed reward functions fails. Gen2Sim provides a viable path for scaling up reinforcement learning for robot manipulators in simulation, both by diversifying and expanding task and environment development, and by facilitating the discovery of reinforcement-learned behaviors through temporal task decomposition in RL. Our work contributes hundreds of simulated assets, tasks and demonstrations, taking a step towards fully autonomous robotic manipulation skill acquisition in simulation.

关键词： Training Solid modeling Three-dimensional displays Training data Reinforcement learning Manipulators Robot learning

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Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation 7

Med-Tuning: A New Parameter-Efficient Tuning Framework for M...

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7th international conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Shen, Jiachen Wang, Wenxuan Chen, Chen Jiao, Jianbo Liu, Jing Zhang, Yan Song, Shanshan Li, Jiangyun School of Automation and Electrical Engineering University of Science and Technology Beijing China Center for Research in Computer Vision University of Central Florida United States School of Computer Science University of Birmingham United Kingdom Institute of Automation Chinese Academy of Sciences China

The "pre-training then fine-tuning (FT)" paradigm is widely adopted to boost the model performance of deep learning-based methods for medical volumetric segmentation. However, conventional full FT incurs high computational and memory costs. Thus, it is of increasing importance to fine-tune pre-trained models for medical volumetric segmentation tasks in a both effective and parameter-efficient manner. In this paper, we introduce a new framework named Med-Tuning to realize parameter-efficient tuning (PET) for medical volumetric segmentation task and an efficient plug-and-play module named Med-Adapter for task-specific feature extraction. With a small number of tuned parameters, our framework enhances the 2D baselines’s precision on segmentation tasks, which are pre-trained on natural images. Extensive experiments on three benchmark datasets (CT and MRI modalities) show that our method achieves better results than previous PET methods on volumetric segmentation tasks. Compared to full FT, Med-Tuning reduces the fine-tuned model parameters by up to 4×, with even better segmentation performance. Our project webpage is at https://***/Med-Tuning/. © 2024 CC-BY 4.0, J. Shen, W. Wang, C. Chen, J. Jiao, J. Liu, Y. Zhang, S. Song & J. Li.

关键词： Image enhancement

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Deep Model Predictive Optimization

Deep Model Predictive Optimization

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IEEE international conference on robotics and automation (ICRA)

作者： Jacob Sacks Rwik Rana Kevin Huang Alex Spitzer Guanya Shi Byron Boots University of Washington Seattle WA USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

A major challenge in robotics is to design robust policies which enable complex and agile behaviors in the real world. On one end of the spectrum, we have model-free reinforcement learning (MFRL), which is incredibly flexible and general but often results in brittle policies. In contrast, model predictive control (MPC) continually re-plans at each time step to remain robust to perturbations and model inaccuracies. However, despite its real-world successes, MPC often under-performs the optimal strategy. This is due to model quality, myopic behavior from short planning horizons, and approximations due to computational constraints. And even with a perfect model and enough compute, MPC can get stuck in bad local optima, depending heavily on the quality of the optimization algorithm. To this end, we propose Deep Model Predictive Optimization (DMPO), which learns the inner-loop of an MPC optimization algorithm directly via experience, specifically tailored to the needs of the control problem. We evaluate DMPO on a real quadrotor agile trajectory tracking task, on which it improves performance over a baseline MPC algorithm for a given computational budget. It can outperform the best MPC algorithm by up to 27% with fewer samples and an end-to-end policy trained with MFRL by 19%. Moreover, because DMPO requires fewer samples, it can also achieve these benefits with 4.3× less memory. When we subject the quadrotor to turbulent wind fields with an attached drag plate, DMPO can adapt zero-shot while still outperforming all baselines. Additional results can be found at https://***/mr2ywmnw.

关键词： Runtime Trajectory tracking Computational modeling Predictive models Prediction algorithms Approximation algorithms Trajectory

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Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping

Kimera-Multi: a System for Distributed Multi-Robot Metric-Se...

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IEEE international conference on robotics and automation (ICRA)

作者： Chang, Yun Tian, Yulun How, Jonathan P. Carlone, Luca MIT Lab Informat & Decis Syst 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781728190778

We present the first fully distributed multi-robot system for dense metric-semantic Simultaneous Localization and Mapping (SLAM). Our system, dubbed Kimera-Multi, is implemented by a team of robots equipped with visual-inertial sensors, and builds a 3D mesh model of the environment in real-time, where each face of the mesh is annotated with a semantic label (e.g., building, road, objects). In Kimera-Multi, each robot builds a local trajectory estimate and a local mesh using Kimera [1] Then, when two robots are within communication range, they initiate a distributed place recognition and robust pose graph optimization protocol with a novel incremental maximum clique outlier rejection;the protocol allows the robots to improve their local trajectory estimates by leveraging inter-robot loop closures. Finally, each robot uses its improved trajectory estimate to correct the local mesh using mesh deformation techniques. We demonstrate Kimera-Multi in photo-realistic simulations and real data. Kimera-Multi (i) is able to build accurate 3D metric-semantic meshes, (ii) is robust to incorrect loop closures while requiring less computation than state-of-the-art distributed SLAM backends, and (iii) is efficient, both in terms of computation at each robot as well as communication bandwidth.

关键词： Solid modeling Three-dimensional displays Simultaneous localization and mapping Protocols Computational modeling Data models Trajectory

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Hybrid Obstacle Avoidance and Repetitive Motion Scheme of Mobile Robots Synthesized by Varying-Parameter Dynamic Learning Networks

Hybrid Obstacle Avoidance and Repetitive Motion Scheme of Mo...

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Intelligent robotics and Automatic Control (IRAC), international conference on

作者： Xiaodong Zhang Mingyang Zhang Zhijun Zhang School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

In order to effectively avoid obstacles and resolve non-repetitive problems of mobile redundant manipulators, a hybrid obstacle avoidance and repetitive motion (HOARM) scheme is proposed, analyzed, and applied, which is synthesized by varying-parameter dynamic learning networks (VPDLN). Specifically, to guarantee the efficient and accurate tasks, a periodic relaxation constrained repetitive motion scheme and an obstacle avoidance approach are integrated as a quadratic criterion, then a bicriteria scheme is constructed by combining the schemes and kinematic models of mobile redundant manipulators, and is further deduced as a quadratic programming (QP) problem through a dynamic weighting method. Then a varying-parameter dynamic learning networks method is applied to solve the proposed QP problem. Furthermore, a path-tracking task is conducted to demonstrate the accuracy and efficiency of the proposed HOARM scheme.

关键词： Accuracy Dynamics Kinematics Dynamic programming Quadratic programming Mobile robots Collision avoidance Manipulator dynamics

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Bayesian Optimization-Based Ideal Landing Planning for Ramp Jump of Single-Track Two-Wheeled Robots

Bayesian Optimization-Based Ideal Landing Planning for Ramp ...

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IEEE international conference on robotics and Biomimetics

作者： Boyi Wang Feilong Jing Yang Deng Zhang Chen Bin Liang Department of Automation Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

For the ramp jump of Single-Track Two-Wheeled (STTW) robots, determining an appropriate landing attitude to ensure a smooth landing is a challenging issue. In this paper, we first conduct a comprehensive analysis of the governing dynamics of the ramp jump, including the gyroscopic effect caused by steering during the flight phase and the impact forces upon landing in the landing phase. Subsequently, we define an ideal landing attitude from which the system naturally returns to its equilibrium point without requiring active control. Building on the dynamics and constraints of the ideal landing, we propose an Ideal Landing-Based Planning (ILBP) method to achieve these ideal landing attitudes. To mitigate the method's dependence on precise modeling, we employ Bayesian Opti-mization (BO) to learn the parameters of the ILBP, enabling the robot to withstand larger disturbances than those managed by the original ILBP. Finally, we validate the effectiveness of the proposed approach within the MuJoCo simulation environment, where the robot effectively masters landing control in fewer than ten trials under parameter perturbations.

关键词： Uncertainty Simulation Perturbation methods Dynamics Buildings Position measurement Linear programming Planning Bayes methods Robots

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