J. Robotics最新文献_第8页

A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model 基于二维网格模型的移动机器人路径规划平滑跳跃点搜索算法

J. Robotics Pub Date : 2022-08-31 DOI: 10.1155/2022/7682201

Zhenyu Yang, Junli Li, Liwei Yang, Hejiang Chen

{"title":"A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model","authors":"Zhenyu Yang, Junli Li, Liwei Yang, Hejiang Chen","doi":"10.1155/2022/7682201","DOIUrl":"https://doi.org/10.1155/2022/7682201","url":null,"abstract":"To address the problems of the traditional A\u0000 \u0000 \u0000 \u0000 ∗\u0000 \u0000 \u0000 algorithm in solving paths with many expansion nodes, high memory overhead, low operation efficiency, and many path corners, this paper improved the traditional A\u0000 \u0000 \u0000 \u0000 ∗\u0000 \u0000 \u0000 algorithm by combining jump point search strategy and adaptive arc optimization strategy. Firstly, to improve the safety of our paths, the risk area of the obstacles was expanded. Then, the A\u0000 \u0000 \u0000 \u0000 ∗\u0000 \u0000 \u0000 algorithm was combined with the jump point search strategy to achieve the subnode jump search, reducing the calculation scale and memory overhead, and improving search efficiency. Considering the influence of the density of obstacles on search efficiency, the heuristic function was enhanced according to the special effects of the density of obstacles. Finally, the redundant jump point and adaptive arc optimization strategies were used to shorten the path length further and enhance the initial path’s smoothness. Simulation results showed that our algorithm outperforms traditional A\u0000 \u0000 ∗\u0000 \u0000 and literature algorithms in path length, security, and smoothness, and then was further validated and applied in large-scale marine environments and realistic settings.","PeriodicalId":186435,"journal":{"name":"J. Robotics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129143164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Robust Finite-Time Tracking Control Based on Disturbance Observer for an Uncertain Quadrotor under External Disturbances 基于扰动观测器的不确定四旋翼机外部扰动鲁棒有限时间跟踪控制

J. Robotics Pub Date : 2022-08-10 DOI: 10.1155/2022/4581165

Hamid Hassani, A. Mansouri, A. Ahaitouf

引用次数: 5

Multirobot Coverage Path Planning Based on Deep Q-Network in Unknown Environment 未知环境下基于深度q -网络的多机器人覆盖路径规划

J. Robotics Pub Date : 2022-08-03 DOI: 10.1155/2022/6825902

Wenhao Li, Tao Zhao, S. Dian

{"title":"Multirobot Coverage Path Planning Based on Deep Q-Network in Unknown Environment","authors":"Wenhao Li, Tao Zhao, S. Dian","doi":"10.1155/2022/6825902","DOIUrl":"https://doi.org/10.1155/2022/6825902","url":null,"abstract":"Aiming at the problems of security, high repetition rate, and many restrictions of multirobot coverage path planning (MCPP) in an unknown environment, Deep Q-Network (DQN) is selected as a part of the method in this paper after considering its powerful approximation ability to the optimal action value function. Then, a deduction method and some environments handling methods are proposed to improve the performance of the decision-making stage. The deduction method assumes the movement direction of each robot and counts the reward value obtained by the robots in this way and then determines the actual movement directions combined with DQN. For these reasons, the whole algorithm is divided into two parts: offline training and online decision-making. Online decision-making relies on the sliding-view method and probability statistics to deal with the nonstandard size and unknown environments and the deduction method to improve the efficiency of coverage. Simulation results show that the performance of the proposed online method is close to that of the offline algorithm which needs long time optimization, and the proposed method is more stable as well. Some performance defects of current MCPP methods in an unknown environment are ameliorated in this study.","PeriodicalId":186435,"journal":{"name":"J. Robotics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121181864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Charging Station Distribution Optimization Using Drone Fleet in a Disaster 灾害中无人机编队充电站分布优化

J. Robotics Pub Date : 2022-07-31 DOI: 10.1155/2022/7329346

Zohaib Hassan, Syed Irtiza Ali Shah, A. Rana

{"title":"Charging Station Distribution Optimization Using Drone Fleet in a Disaster","authors":"Zohaib Hassan, Syed Irtiza Ali Shah, A. Rana","doi":"10.1155/2022/7329346","DOIUrl":"https://doi.org/10.1155/2022/7329346","url":null,"abstract":"A disaster is an unforeseen calamity that can cause damage to properties and can bring about a loss of human lives. Usually, many relief supplies, such as clean water, food, and medical supplies, are required by disaster victims. Quick response and rapid distribution of essential relief items into the affected region can save countless lives and prevent or slow down the effects of disasters. In this regard, disaster management comes into play, which is highly dependent on the topography and access of the disaster-hit area. If the disaster-hit site has little or no road connectivity, the use of UAVs/drones becomes essential in delivering health packages to the affected areas to assist with humanitarian aid. Since the battery capacity of the drone is limited, UAVs/drones require charging stations located at various places to carry out the necessary relief work. These charging stations should be transported using road infrastructure and preinstalled in disaster-prone areas, as access to these areas may be denied once the disaster hits. This article presents a novel optimization model to distribute relief items to disaster-hit areas. The objective of this model is to optimize the location and the number of the charging stations. We consider the relative priority of locations where a preference is given to locations with higher priority levels. The optimal number of charging stations and optimal routes has also been determined by using our optimization model. To illustrate the use of our model, numerical examples have been simulated for a different number of targets. Through our numerical simulation, it was observed that the drone’s maximum distance capacity is the key factor in determining the optimal grid size, which directly correlates to the number of charging stations.","PeriodicalId":186435,"journal":{"name":"J. Robotics","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115672412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects 自主机器人操作:实时、深度学习抓取未知物体的方法

J. Robotics Pub Date : 2022-06-30 DOI: 10.1155/2022/2585656

Malak H. Sayour, Sharbel E. Kozhaya, S. Saab

{"title":"Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects","authors":"Malak H. Sayour, Sharbel E. Kozhaya, S. Saab","doi":"10.1155/2022/2585656","DOIUrl":"https://doi.org/10.1155/2022/2585656","url":null,"abstract":"Recent advancement in vision-based robotics and deep-learning techniques has enabled the use of intelligent systems in a wider range of applications requiring object manipulation. Finding a robust solution for object grasping and autonomous manipulation became the focus of many engineers and is still one of the most demanding problems in modern robotics. This paper presents a full grasping pipeline proposing a real-time data-driven deep-learning approach for robotic grasping of unknown objects using MATLAB and convolutional neural networks. The proposed approach employs RGB-D image data acquired from an eye-in-hand camera centering the object of interest in the field of view using visual servoing. Our approach aims at reducing propagation errors and eliminating the need for complex hand tracking algorithm, image segmentation, or 3D reconstruction. The proposed approach is able to efficiently generate reliable multi-view object grasps regardless of the geometric complexity and physical properties of the object in question. The proposed system architecture enables simple and effective path generation and a real-time tracking control. In addition, our system is modular, reliable, and accurate in both end effector path generation and control. We experimentally justify the efficacy and effectiveness of our overall system on the Barrett Whole Arm Manipulator.","PeriodicalId":186435,"journal":{"name":"J. Robotics","volume":"2022 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130501298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Sim-to-Real Reinforcement Learning for Autonomous Driving Using Pseudosegmentation Labeling and Dynamic Calibration 基于伪分割标记和动态校准的模拟到真实的自动驾驶强化学习

J. Robotics Pub Date : 2022-06-26 DOI: 10.1155/2022/9916292

Jiseong Heo, Hyoung woo Lim

引用次数: 1

Comparative Study of Two Localization Approaches for Mobile Robots in an Indoor Environment 室内环境下移动机器人两种定位方法的比较研究

J. Robotics Pub Date : 2022-06-02 DOI: 10.1155/2022/1999082

Eman Alhamdi, R. Hedjar

引用次数: 0

Design, Implementation, and Performance Evaluation of a Web-Based Multiple Robot Control System 基于web的多机器人控制系统的设计、实现和性能评估

J. Robotics Pub Date : 2022-05-30 DOI: 10.1155/2022/9289625

U. Rajapaksha, C. Jayawardena, B. MacDonald

{"title":"Design, Implementation, and Performance Evaluation of a Web-Based Multiple Robot Control System","authors":"U. Rajapaksha, C. Jayawardena, B. MacDonald","doi":"10.1155/2022/9289625","DOIUrl":"https://doi.org/10.1155/2022/9289625","url":null,"abstract":"Heterogeneous multiple robots are currently being used in smart homes and industries for different purposes. The authors have developed the Web interface to control and interact with multiple robots with autonomous robot registration. The autonomous robot registration engine (RRE) was developed to register all robots with relevant ROS topics. The ROS topic identification algorithm was developed to identify the relevant ROS topics for the publication and the subscription. The Gazebo simulator spawns all robots to interact with a user. The initial experiments were conducted with simple instructions and then changed to manage multiple instructions using a state transition diagram. The number of robots was increased to evaluate the system’s performance by measuring the robots’ start and stop response time. The authors have conducted experiments to work with the semantic interpretation from the user instruction. The mathematical equations for the delay in response time have been derived by considering each experiment’s input given and system characteristics. The Big O representation is used to analyze the running time complexity of algorithms developed. The experiment result indicated that the autonomous robot registration was successful, and the communication performance through the Web decreased gradually with the number of robots registered.","PeriodicalId":186435,"journal":{"name":"J. Robotics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132778065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Learning a Robust Hybrid Descriptor for Robot Visual Localization 机器人视觉定位的鲁棒混合描述符学习

J. Robotics Pub Date : 2022-05-19 DOI: 10.1155/2022/9354909

Qingwu Shi, Junjun Wu, Zeqin Lin, Ningwei Qin

{"title":"Learning a Robust Hybrid Descriptor for Robot Visual Localization","authors":"Qingwu Shi, Junjun Wu, Zeqin Lin, Ningwei Qin","doi":"10.1155/2022/9354909","DOIUrl":"https://doi.org/10.1155/2022/9354909","url":null,"abstract":"Long-term robust visual localization is one of the main challenges of long-term visual navigation for mobile robots. Due to factors such as illumination, weather, and season, mobile robots continuously navigate with visual information in a complex scene, which is likely to lead to failure localization within a few hours. However, semantic segmentation images will be more stable than the original images against considerable drastically variable environments; therefore, to make full use of the advantages of both semantic segmentation image and its original image, this paper solves the above problems with the latest work of semantic segmentation and proposes the novel hybrid descriptor for long-term visual localization, which is generated by combining a semantic image descriptor extracted from segmentation images and an image descriptor extracted from RGB images with a certain weight, and then trained by a convolutional neural network. Our experiments show that the localization performance of our method combining the advantages of semantic image descriptor and image descriptor is superior to those long-term visual localization methods with only an image descriptor or semantic image descriptor. Finally, our experimental results mostly exceed state-of-the-art 2D image-based localization methods under various challenging environmental conditions in the Extended CMU Seasons and RobotCar Seasons datasets in specific precision metrics.","PeriodicalId":186435,"journal":{"name":"J. Robotics","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123392408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emotional State Analysis Model of Humanoid Robot in Human-Computer Interaction Process 仿人机器人在人机交互过程中的情绪状态分析模型

J. Robotics Pub Date : 2022-05-06 DOI: 10.1155/2022/8951671

Boxin Peng

引用次数: 0