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检索条件"主题词=Robotics and automation"

共 11731 条记录，以下是81-90 订阅

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Subequivariant Reinforcement Learning Framework for Coordinated Motion Control

Subequivariant Reinforcement Learning Framework for Coordina...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Haoyu Wang Xiaoyu Tan Xihe Qiu Chao Qu Shanghai University of Engineering Science Shanghai China INF Technology (Shanghai) Co. Ltd

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Effective coordination is crucial for motion control with reinforcement learning, especially as the complexity of agents and their motions increases. However, many existing methods struggle to account for the intricate dependencies between joints. We introduce CoordiGraph, a novel architecture that leverages subequivariant principles from physics to enhance coordination of motion control with reinforcement learning. This method embeds the principles of equivariance as inherent patterns in the learning process under gravity influence, which aids in modeling the nuanced relationships between joints vital for motion control. Through extensive experimentation with sophisticated agents in diverse environments, we highlight the merits of our approach. Compared to current leading methods, CoordiGraph notably enhances generalization and sample efficiency.

关键词： Motion planning Couplings Process control Reinforcement learning Complexity theory Task analysis robotics and automation

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Pushing and Rotating a Heavy Mass Rigid Body Using an Omnidirectional Aerial Manipulator

Pushing and Rotating a Heavy Mass Rigid Body Using an Omnidi...

引用

International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Hameed Ullah Santos Miguel Orozco Soto Naveed Mazhar Irfan Ahmad Vincenzo Lippiello Fabio Ruggiero Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy Science and Technology College Autonomous University of Mexico City Mexico City Mexico Unmanned Vehicles Research Lab DEE CEME National University of Sciences & Technology Islamabad Pakistan Department of Automatic Control and Systems Engineering School of Bilbao University of the Basque Country Bilbao Spain

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

This paper presents the pushing and rotation of a heavy rigid body using an omnidirectional actively tilted aerial manipulator, controlled via a hybrid force/position controller. Unlike conventional unmanned aerial vehicles, the proposed system enables precise horizontal force application, making it ideal for aerial physical interaction tasks. The hybrid controller is implemented in the Robot Operating System and validated through extensive simulations in a Gazebo physics-engine-based environment. The manipulator's performance was evaluated in two scenarios: circular trajectory tracking and the rotation of a heavy rigid mass. Results demonstrated its capability to follow complex trajectories with high accuracy and exert sustained forces for object manipulation. The system exhibited excellent stability and precision, underscoring its potential for real-world applications in challenging environments.

关键词： Accuracy Trajectory tracking Force Refining Rotors Stability analysis Robustness Trajectory robotics and automation Manipulator dynamics

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Self-Supervised Learning of Monocular Visual Odometry and Depth with Uncertainty-Aware Scale Consistency

Self-Supervised Learning of Monocular Visual Odometry and De...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Changhao Wang Guanwen Zhang Wei Zhou Visual Intelligent Processing Laboratory School of Electronics and Information Northwestern Polytechnical University Xi’an Shannxi P.R. China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The inherent scale ambiguity issue greatly limits the performance of monocular visual odometry. In recent years, a variety of methods have been proposed for self-supervised learning of ego-motion and depth estimation, incorporating specifically designed scale-consistency constraints that utilize estimated depth as a reference. However, these existing methods neglect the influence of the depth uncertainty introduced by the dominant photometric loss, which leads to unreliable depth estimation in difficult regions and detrimentally affects scale alignment. To solve these problems, we introduces a feature-based visual odometry learning system with an effective scale recovery strategy in this paper. Additionally, we propose a learning method to estimate the photometric-sensitive depth uncertainty for guiding the scale recovery. The proposed method is evaluated on KITTI odometry, and the experimental results demonstrate that our system can predict scale-consistent trajectories from monocular videos and achieves state-of-the-art performance. Moreover, the proposed method achieves competitive performance on KITTI depth estimation.

关键词： Uncertainty Estimation Self-supervised learning Trajectory Odometry robotics and automation Optimization

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Motorization of Vehicle Positioning

Motorization of Vehicle Positioning

引用

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： S. Gowtham Manojkumar V. Sasikumar R. Sudhev C. Mohanraj Department of Electrical and Electronics Engineering Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Parking lots that are well-organized can make drivers' lives easier by saving them time and gas. In this work, a fully automated system for car parking and washing is designed. On the FX5U programmable logic controller (PLC), functional ladder logic is based. resistor that changes color with light At the entrance and exit gates, electronic sensors were put in order to detect the cars that are waiting for either entry or exit. The PLC receives the input signals after that and begins counting the number of vehicles entering and exiting the parking area, correspondingly. The created system has the ability to automatically monitor and limit the vehicles in the parking space. The difference between the number of cars arriving and those leaving will determine how many cars are actually in the parking space. PLC will determine whether or not there is room as a car approaches the entry gate. When no more room is available, the PLC will command the entry gate to remain closed and turn on the red light. While leaving the car parking space there is an availability of an automated car washing system which can be utilized by the drivers. Once the car enters the washing sector the sensors at the entrance sense the car and signal for the operation of the washing process. All these activities make the car parking and washing system automatic. Thus, making it easy for people to park and wash the car simultaneously saving a lot of time and reducing manual efforts. The evolution of this approach can be used to solve issues with car parking spaces in urban areas, malls, and other public places.

关键词： Space vehicles Resistors Technological innovation Urban areas Programmable logic devices Logic gates Sensor systems Sensors Reliability robotics and automation

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Deep Learning-Based Weed Detection for Precision Agriculture Applications

Deep Learning-Based Weed Detection for Precision Agriculture...

引用

International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Saaim Ahmed Muhammad Waris Ali Muhammad Ali Arshad Shahbaz Khan Department of Mechanical Engineering Aerial Robotics Lab ARL National University of Sciences and Technology Islamabad Pakistan Department of Robotics and AI Aerial Robotics Lab ARL National University of Sciences and Technology Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Weed detection and management is an intricate and formidable challenge, affecting the efficiency of human agricultural practices. Object detection has merged as a key technique in precision-based agriculture for combatting this issue. This paper analyzes the performance of two cutting-edge object detection models namely YOLOv8x and Faster RCNN on a specialized weed-detection dataset. By leveraging transfer learning, these models proved computationally feasible and accurate. The models were built and trained in a Kaggle environment, promoting a more collaborative approach to developing and implementing deep-learning models. The Faster RCNN model showcased a comparatively better mAP as compared to the YOLOv8x model. This paper highlights the significance of advanced models like Faster RCNN and YOLOv8x for precision agriculture while advocating for further research in deep-learning techniques for promoting resource-efficient agricultural and farming practices.

关键词： Precision agriculture Training Analytical models Technological innovation Computational modeling Prevention and mitigation Transfer learning Object detection robotics and automation Tuning

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A Neural-Evolutionary Algorithm for Autonomous Transit Network Design

A Neural-Evolutionary Algorithm for Autonomous Transit Netwo...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Andrew Holliday Gregory Dudek School of Computer Science McGill University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Planning a public transit network is a challenging optimization problem, but essential in order to realize the benefits of autonomous buses. We propose a novel algorithm for planning networks of routes for autonomous buses. We first train a graph neural net model as a policy for constructing route networks, and then use the policy as one of several mutation operators in a evolutionary algorithm. We evaluate this algorithm on a standard set of benchmarks for transit network design, and find that it outperforms the learned policy alone by up to 20% and a plain evolutionary algorithm approach by up to 53% on realistic benchmark instances.

关键词： Evolutionary computation Benchmark testing Graph neural networks Planning robotics and automation Standards Optimization

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Path Planning Algorithm for Heavy-duty AGV Based on A* Algorithm Fused with DWA

Path Planning Algorithm for Heavy-duty AGV Based on A* Algor...

引用

IEEE International Conference on Mechatronics, robotics and automation (ICMRA)

作者： Sanbao Zhu Wenxia Xu Lei Zhai Baocheng Yu Hanyan He Zhiqiang Tang Wuhan Institute of Technology Wuhan China State Key Laboratory of Bridge Intelligent and Green Construction Wuhan China

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

In a complex working environment, it is particularly important to automatically plan a safe and real-time path when heavy-duty AGV are used for automated transportation. The traditional A* algorithm has shortcomings in terms of low search efficiency, many redundant points and obstacle avoidance. Meanwhile, the traditional dynamic window method (DWA) is prone to falling into a local optimal solution. Therefore, this paper proposes a fusion algorithm. First, the A* algorithm is improved by using a two-way search, introducing dynamic weights in the heuristic function, and deleting redundant points. Then the dynamic obstacle avoidance strategy is introduced in the evaluation function of the DWA algorithm, and the improved A* algorithm is fused with the improved DWA algorithm. Finally, the superiority of the improved fusion algorithm is verified through simulation experiment comparison and real experiment.

关键词： Mechatronics Heuristic algorithms Transportation Path planning Real-time systems Collision avoidance robotics and automation

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FuncGrasp: Learning Object-Centric Neural Grasp Functions from Single Annotated Example Object

FuncGrasp: Learning Object-Centric Neural Grasp Functions fr...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Hanzhi Chen Binbin Xu Stefan Leutenegger Smart Robotics Lab School of CIT Technical University of Munich Munich Institute of Robotics and Machine Intelligence (MIRMI) University of Toronto Robotics Institute University of Toronto

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We present FuncGrasp, a framework that can infer dense yet reliable grasp configurations for unseen objects using one annotated object and single-view RGB-D observation via categorical priors. Unlike previous works that only transfer a set of grasp poses, FuncGrasp aims to transfer infinite configurations parameterized by an object-centric continuous grasp function across varying instances. To ease the transfer process, we propose Neural Surface Grasping Fields (NSGF), an effective neural representation defined on the surface to densely encode grasp configurations. Further, we exploit function-to-function transfer using sphere primitives to establish semantically meaningful categorical correspondences, which are learned in an unsupervised fashion without any expert knowledge. We showcase the effectiveness through extensive experiments in both simulators and the real world. Remarkably, our framework significantly outperforms several strong baseline methods in terms of density and reliability for generated grasps.

关键词： Training Degradation Filtering Semantics Grasping Reliability engineering robotics and automation

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Comparative Analysis of Reaching Laws in Sliding Mode Control for Electrohydraulic Active Suspension Systems under Complex Road Trajectories

Comparative Analysis of Reaching Laws in Sliding Mode Contro...

引用

IEEE International Conference on Mechatronics, robotics and automation (ICMRA)

作者： Rachid Fattah Jean-Pierre Kenne Khalid Benjelloun Ahmed Chebak Green Tech Institute (GTI) Mohammed VI Polytechnic University (UM6P) Ben Guerir Morocco Department of Mechanical Engineering Ecole de technologie supérieure (ETS) Montreal Canada École Mohammadia d’Ingénieurs (EMI) MOHAMMED V UNIVERSITY (UM5) Rabat Morocco

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

This paper presents a comparative study on various reaching laws within the sliding mode control approach for electrohydraulic active suspension systems under complex road conditions. Unlike traditional single bump profiles, this study evaluates the performance of constant, exponential, and power reaching laws with scenarios involving a speed bump, a pothole, and a sidewalk. The objective is to enhance vehicle handling and passenger *** address chattering in sliding mode controllers, we explore exponential and power reaching laws. Performance metrics for road holding and passenger comfort are compared with those of a traditional PID control. Simulation results indicate that the exponential reaching law excels in both position and force control, potentially negating the need for hybrid control when used alone. However, hybrid control is necessary for constant rate or power rate reaching laws.

关键词： Suspensions (mechanical systems) PI control Mechatronics Roads Simulation Trajectory Electrohydraulics PD control robotics and automation Sliding mode control

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Omnidirectional Dense SLAM for Back-to-back Fisheye Cameras

Omnidirectional Dense SLAM for Back-to-back Fisheye Cameras

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IEEE International Conference on robotics and automation (ICRA)

作者： Weijian Xie Guanyi Chu Quanhao Qian Yihao Yu Shangjin Zhai Danpeng Chen Nan Wang Hujun Bao Guofeng Zhangv State Key Lab of CAD&CG Zhejiang University SenseTime Research Tetras.AI

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We propose a real-time visual-inertial dense SLAM system that utilizes the online data streams from back-to-back dual fisheye cameras setup, providing 360 ◦ coverage of the environment. Firstly, we employ a sliding-window-based front-end to estimate real-time poses from the binocular fisheye images and IMU data. Then, we implement a lightweight panoramic depth completion network based on multi-basis depth representation. The network takes panoramic images (obtained by stitching dual-fisheye images with extrinsics and intrinsic parameters) and sparse depths (generated by the front-end local tracking) as input and predicts multiple depth bases along with corresponding confidence as output. The final dense depth is the linear combination of the multiple depth bases. Thanks to the multi-basis depth representation, we can continuously optimize the 360° depth with the traditional optimizer to achieve higher global consistency in depth. We conducted experiments on both simulated and real-world datasets to evaluate our method. The results demonstrate that the proposed method outperforms SoTA methods in terms of depth prediction and 3D reconstruction. In addition, we develop a demo that can run on a mobile to demonstrate the real-time capabilities of our method.

关键词： Simultaneous localization and mapping Three-dimensional displays Cameras Real-time systems robotics and automation Streams

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