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检索条件"主题词=Robotics and automation"

共 11731 条记录，以下是61-70 订阅

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Design and Test of Power Chassis for Small Tracked Sand Barrier Placing Vehicle

Design and Test of Power Chassis for Small Tracked Sand Barr...

引用

Advanced Information, Mechanical Engineering, robotics and automation (AIMERA), IEEE International Conference on

作者： Weiqiang Zheng Zehui Wang Liping Zhang Jianqiao Long Chang Liu Feiyang Xing School of Mechanical Engineering Xinjiang University Urumqi China School of Computing Newcastle University UK

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

Aiming at the problems of low efficiency of the existing sand barriers burying machinery, large quality of the whole machine, inconvenient operation of large-scale machinery, and difficult to adapt to the complex terrain environment in the desert area, a self-propelled small and flexible power chassis has been developed, which mainly consists of the frame, the power system, the traveling system, and the control system. According to the environment of sand barriers buried, put forward the design target performance requirements and the overall structure of the program; first of all, the walking system components for the calculation and selection, followed by the matching calculation of the drive motor and power battery and the control system hardware circuit design and software programming; finally, the power chassis for the field test. The test results show that the power chassis has good range and climbing ability, with a range of up to 12km, a maximum speed of 6.1km/h, the ability to cross 25° sandy slopes, and a wireless remote control distance of 100m, which is in line with the desert environment driving and sand barriers burying operational needs.

关键词： Wireless communication Legged locomotion Programming Control systems Motors Software Power systems robotics and automation Mechanical engineering Remote control

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Analysis of Three-Body Pitching Motion of New Unmanned Boat Based on Genetic Algorithm

Analysis of Three-Body Pitching Motion of New Unmanned Boat ...

引用

Advanced Information, Mechanical Engineering, robotics and automation (AIMERA), IEEE International Conference on

作者： Di Zhang Guoming Li Ke Zhang Meng Li School of Mechanical and Electrical Engineering Weifang Engineering Vocational College Weifang China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

When the current rescue unmanned boat is sailing on the sea, the vertical shake on the boat is too large, which will lead to a great reduction in the sailing performance of rescue unmanned boats. This article uses the three-body boat modal water vertical shake attenuation experiments as the research object. The programming ideas based on systematic identification methods and genetic algorithms, and establish four vertical system identification mathematical models. Through experiments, the peak of the boat model increased, the peak of the vertical corner speed increased, and the time of the boat model reached a stable state increase. Secondly, the best fitting model pitch motion identification equation is obtained, which not only verifies the feasibility of the unmanned vehicle model, but also verifies the reliability of the adapted software. Finally, through the comparison of dual - boating models, the three-body boat models have a good reduction effect.

关键词： Adaptation models Systematics Boats Mathematical models Software System identification Software reliability robotics and automation Autonomous vehicles Genetic algorithms

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MORPH: Design Co-optimization with Reinforcement Learning via a Differentiable Hardware Model Proxy

MORPH: Design Co-optimization with Reinforcement Learning vi...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Zhanpeng He Matei Ciocarlie Department of Computer Science Columbia University New York USA Department of Mechanical Engineering Columbia University New York USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We introduce MORPH, a method for co-optimization of hardware design parameters and control policies in simulation using reinforcement learning. Like most co-optimization methods, MORPH relies on a model of the hardware being optimized, usually simulated based on the laws of physics. However, such a model is often difficult to integrate into an effective optimization routine. To address this, we introduce a proxy hardware model, which is always differentiable and enables efficient co-optimization alongside a long-horizon control policy using RL. MORPH is designed to ensure that the optimized hardware proxy remains as close as possible to its realistic counterpart, while still enabling task completion. We demonstrate our approach on simulated 2D reaching and 3D multi-fingered manipulation tasks.

关键词： Three-dimensional displays Reinforcement learning Hardware Task analysis robotics and automation Physics Optimization

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LSTM-based modeling of the Tangnaihai and Fen River basins Rainfall Runoff Simulation

LSTM-based modeling of the Tangnaihai and Fen River basins R...

引用

Advanced Information, Mechanical Engineering, robotics and automation (AIMERA), IEEE International Conference on

作者： Zhengfang Jiang Baohong Lu Zunguang Zhou Yirui Zhao College of Hydrology and Water Resources Hohai University Nanjing China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

In this study, the Tangnaihai basin, characterized by weak human activities at the source of the Yellow River, and the Fenhe River basin, marked by significant human activities in the middle reaches of the Yellow River, were chosen as research subjects. Rainfall and runoff simulations with forecasting foresight periods of 1-3 days were conducted using LSTM and BPNN models. Despite declining simulation accuracy with longer forecast periods, LSTM consistently outperforms BPNN. Notably, the LSTM model effectively captures the hydrological nuances of different watershed types, although the precise internal hidden layer structure and historical information inputs remain ambiguous. The study reveals that watersheds experiencing strong human disturbances necessitate a complex hidden layer structure for accurate rainfall and runoff simulation. Furthermore, it is observed that the model’s dependence on historical information diminishes with the extension of the forecast period, highlighting the evolving relationship between historical data and forecasting accuracy.

关键词： Analytical models Accuracy Rain Watersheds Predictive models Rivers Solar radiation Forecasting robotics and automation Long short term memory

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Towards Geometric Motion Planning for High-Dimensional Systems: Gait-Based Coordinate Optimization and Local Metrics

Towards Geometric Motion Planning for High-Dimensional Syste...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Yanhao Yang Capprin Bass Ross L. Hatton Collaborative Robotics and Intelligent Systems (CoRIS) Institute Oregon State University Corvallis OR USA AI Institute Cambridge MA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Geometric motion planning offers effective and interpretable gait analysis and optimization tools for locomoting systems. However, due to the curse of dimensionality in coordinate optimization, a key component of geometric motion planning, it is almost infeasible to apply current geometric motion planning to high-dimensional systems. In this paper, we propose a gait-based coordinate optimization method that overcomes the curse of dimensionality. We also identify a unified geometric representation of locomotion by generalizing various nonholonomic constraints into local metrics. By combining these two approaches, we take a step towards geometric motion planning for high-dimensional systems. We test our method in two classes of high-dimensional systems - low Reynolds number swimmers and free-falling Cassie - with up to 11-dimensional shape variables. The resulting optimal gait in the high-dimensional system shows better efficiency compared to that of the reduced-order model. Furthermore, we provide a geometric optimality interpretation of the optimal gait.

关键词： Measurement Shape Optimization methods Reduced order systems Planning robotics and automation

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Augmented Differential Dynamic Programming for Humanoid Robot Flipping Motion Optimization

Augmented Differential Dynamic Programming for Humanoid Robo...

引用

International Conference on robotics and automation Engineering (ICRAE)

作者： Mingyue Jin Junyao Gao Jingwei Cao Xiaokun Jin Leilei Xie Xilong Xin School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Medical Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798331518301

ISBN: (纸本)9798331518318

Differential Dynamic Programming (DDP) is an efficient tool for non-linear optimal problems. And DDP shows a characteristic of fast convergence. However, the traditional DDP cannot solve the nonlinear constraints. Therefore, an augmented differential dynamic programming method is proposed in this paper for trajectory optimization of humanoid robot motion planning. The flipping motion is adopted in our work. The continuous dynamic model and collision dynamic model are constructed, in which the reset map of the collision dynamics model calculates the instantaneous velocity and acceleration change before and after the collision. Moreover, the Augmented Lagrangian (AL) method is adopted in DDP to deal with equality constraints and inequality constraints. After n times of augmented DDP iterations, the optimal flipping motion trajectory is obtained. The simulation experiments on the BHR-FCR humanoid robot show that the proposed approach has the ability to solve nonlinear constraint optimal problems.

关键词： Dynamics Humanoid robots Dynamic programming Planning Time factors robotics and automation Trajectory optimization Convergence

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Multi-class Road Defect Detection and Segmentation using Spatial and Channel-wise Attention for Autonomous Road Repairing

Multi-class Road Defect Detection and Segmentation using Spa...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Jongmin Yu Chen Bene Chi Sebastiano Fichera Paolo Paoletti Devansh Mehta Shan Luo Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Department of Engineering King’s College London Strand London United Kingdom Department of Informatics King’s College London Strand London United Kingdom Robotiz3D Sci-Tech Daresbury Keckwick Lane United Kingdom School of Engineering University of Liverpool Liverpool United Kingdom

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Road pavement detection and segmentation are critical for developing autonomous road repair systems. However, developing an instance segmentation method that simultaneously performs multi-class defect detection and segmentation is challenging due to the textural simplicity of road pavement image, the diversity of defect geometries, and the morphological ambiguity between classes. We propose a novel end-to-end method for multi-class road defect detection and segmentation. The proposed method comprises multiple spatial and channel-wise attention blocks available to learn global representations across spatial and channel-wise dimensions. Through these attention blocks, more globally generalised representations of morphological information (spatial characteristics) of road defects and colour and depth information of images can be learned. To demonstrate the effectiveness of our framework, we conducted various ablation studies and comparisons with prior methods on a newly collected dataset annotated with nine road defect classes. The experiments show that our proposed method outperforms existing state-of-the-art methods for multi-class road defect detection and segmentation methods.

关键词： Instance segmentation Geometry Image color analysis Roads Maintenance engineering robotics and automation Defect detection

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Optimal Driver Warning Generation in Dynamic Driving Environment

Optimal Driver Warning Generation in Dynamic Driving Environ...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Chenran Li Aolin Xu Enna Sachdeva Teruhisa Misu Behzad Dariush Department of Mechanical Engineering University of California Berkeley California USA Honda Research Institute USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The driver warning system that alerts the human driver about potential risks during driving is a key feature of an advanced driver assistance system. Existing driver warning technologies, mainly the forward collision warning and unsafe lane change warning, can reduce the risk of collision caused by human errors. However, the current design methods have several major limitations. Firstly, the warnings are mainly generated in a one-shot manner without modeling the ego driver’s reactions and surrounding objects, which reduces the flexibility and generality of the system over different scenarios. Additionally, the triggering conditions of warning are mostly rule-based threshold-checking given the current state, which lacks the prediction of the potential risk in a sufficiently long future horizon. In this work, we study the problem of optimally generating driver warnings by considering the interactions among the generated warning, the driver behavior, and the states of ego and surrounding vehicles on a long horizon. The warning generation problem is formulated as a partially observed Markov decision process (POMDP). An optimal warning generation framework is proposed as a solution to the proposed POMDP. The simulation experiments demonstrate the superiority of the proposed solution to the existing warning generation methods.

关键词： Simulation Markov decision processes Design methodology Alarm systems Vehicle dynamics robotics and automation Advanced driver assistance systems

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A Novel Adaptive Deep Belief Network for Bearing Fault Diagnosis in Variable Working Conditions Based on Salp Swarm Algorithm Under Small Sample

A Novel Adaptive Deep Belief Network for Bearing Fault Diagn...

引用

Advanced Information, Mechanical Engineering, robotics and automation (AIMERA), IEEE International Conference on

作者： Qingbin Tong Xin Qi Xuedong Jiang Feiyu Lu Ziwei Feng Jianjun Xu School of Electrical Engineering Beijing Jiaotong University Beijing People's Republic of China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

The actual operating conditions of equipment are complicated and changeable, and it is not easy to obtain enough training sample data quickly. Meanwhile, setting neural network structure parameters by experience or repeated trials will also affect the diagnosis results. To solve this issue, a Domain Adaptive Deep Belief Network based on SSA and Second-Order Statistics Transfer (SSA-SOST-DADBN) of features is proposed. Firstly, the structure parameters of deep belief network (DBN) were optimized by salp swarm algorithm. By solving the envelope spectrum of bearing signal, the hidden information of fault characteristic frequency is obtained, and the training dataset of DBN is constructed. The covariance matrix of distribution of features across the different domains is considered to realize transfer training under different operating situations. The dropout method is employed for effective small-sample rolling bearing fault diagnosis so as to mitigate the risk of overfitting in the network. Based on the public dataset of CWRU, the experiment proves that the network has high accuracy. The method can achieve an average classification accuracy as 95.08% under different working conditions, which has higher accuracy and generalization compared with the other 5 methods.

关键词： Training Fault diagnosis Employee welfare Accuracy Adaptive systems Neural networks Rolling bearings robotics and automation Mechanical engineering Covariance matrices

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CAMInterHand: Cooperative Attention for Multi-View Interactive Hand Pose and Mesh Reconstruction

CAMInterHand: Cooperative Attention for Multi-View Interacti...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Guwen Han Qi Ye Anjun Chen Jiming Chen College of Computer Science and Technology Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China The College of Control Science and Engineeringthe State Key Laboratory of Industrial Control Technology Zhejiang University and the Key Key Lab of CS&AUS of Zhejiang Province

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Interactive hand mesh reconstruction from singleview images poses a significant challenge with the severe occlusion and depth ambiguity inherent in interactive hand gestures. Recent approaches that employ probabilistic models and tokenpruned techniques have shown decent results in multi-view human body reconstruction. Nevertheless, these methods have not fully utilized multi-scale semantic information from multiview images and are not applicable in scenarios involving severe occlusion during dual-hand interactions. Simultaneously, current single-view methods independently reconstruct the left and right hands, which are ineffective in enhancing the interaction between both hands. To address these challenges, we propose CAMInterHand, a cooperative attention-based method for multi-view interactive hand pose and mesh reconstruction. Specifically, CAMInterHand extracts local pyramid features and global vertex features from multi-scale feature maps of multi-view images, enabling the exploration of rich local semantic information and facilitating effective feature alignment. Furthermore, CAMInterHand employs the cooperative attention fusion module to fuse all features from multi-view images, enhancing interactions among vertices of dual hands within global and local contexts. We conduct extensive experiments on the large-scale multi-view dataset InterHand2.6M and CAMInterHand achieves a substantial performance improvement over existing methods for multi-view and single-view interactive hand reconstruction.

关键词： Fuses Semantics Feature extraction Probabilistic logic Data mining robotics and automation Image reconstruction

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