Pub Date : 2022-03-01DOI: 10.1177/17298806221095974
Ziwei Xia, Zhen Deng, Bin Fang, Yiyong Yang, Fuchun Sun
Sensory perception for dexterous robotic hands is an active research area and recent progress in robotics. Effective dexterous manipulation requires robotic hands to accurately feedback their state or perceive the surrounding environment. This article reviews the state-of-the-art of sensory perception for dexterous robotic manipulation. Two types of sensors, such as intrinsic and extrinsic sensors, are introduced according to their function and layout in robotic hands. These sensors provide rich information to a robotic hand, which contains the posture, the contact information of objects, and the physical information of the environment. Then, a comprehensive analysis of perception methods including planning-level, control-level, and learning-level perceptions is presented. The information obtained from sensory perception can help robotic hands to make decisions effectively. Previously issued reviews mainly focus on the design of tactile senor, while we analyze and discuss the relationship among sensing, perception, and dexterous manipulation. Some potential research topics on sensory perception are also summarized and discussed.
{"title":"A review on sensory perception for dexterous robotic manipulation","authors":"Ziwei Xia, Zhen Deng, Bin Fang, Yiyong Yang, Fuchun Sun","doi":"10.1177/17298806221095974","DOIUrl":"https://doi.org/10.1177/17298806221095974","url":null,"abstract":"Sensory perception for dexterous robotic hands is an active research area and recent progress in robotics. Effective dexterous manipulation requires robotic hands to accurately feedback their state or perceive the surrounding environment. This article reviews the state-of-the-art of sensory perception for dexterous robotic manipulation. Two types of sensors, such as intrinsic and extrinsic sensors, are introduced according to their function and layout in robotic hands. These sensors provide rich information to a robotic hand, which contains the posture, the contact information of objects, and the physical information of the environment. Then, a comprehensive analysis of perception methods including planning-level, control-level, and learning-level perceptions is presented. The information obtained from sensory perception can help robotic hands to make decisions effectively. Previously issued reviews mainly focus on the design of tactile senor, while we analyze and discuss the relationship among sensing, perception, and dexterous manipulation. Some potential research topics on sensory perception are also summarized and discussed.","PeriodicalId":50343,"journal":{"name":"International Journal of Advanced Robotic Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43973548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/17298806221094925
Zixuan Zhao, Yucheng Zhang, Long Long, Zaiwang Lu, Jinglin Shi
The accuracy of agricultural unmanned ground vehicles’ localization directly affects the accuracy of their navigation. However, due to the changeable environment and fewer features in the agricultural scene, it is challenging for these unmanned ground vehicles to localize precisely in global positioning system-denied areas with a single sensor. In this article, we present an efficient and adaptive sensor-fusion odometry framework based on simultaneous localization and mapping to handle the localization problems of agricultural unmanned ground vehicles without the assistance of a global positioning system. The framework leverages three kinds of sub-odometry (lidar odometry, visual odometry and inertial odometry) and automatically combines them depending on the environment to provide accurate pose estimation in real time. The combination of sub-odometry is implemented by trading off the robustness and the accuracy of pose estimation. The efficiency and adaptability are mainly reflected in the novel surfel-based iterative closest point method for lidar odometry we propose, which utilizes the changeable surfel radius range and the adaptive iterative closest point initialization to improve the accuracy of pose estimation in different environments. We test our system in various agricultural unmanned ground vehicles’ working zones and some other open data sets, and the results prove that the proposed method shows better performance mainly in accuracy, efficiency and robustness, compared with the state-of-art methods.
{"title":"Efficient and adaptive lidar–visual–inertial odometry for agricultural unmanned ground vehicle","authors":"Zixuan Zhao, Yucheng Zhang, Long Long, Zaiwang Lu, Jinglin Shi","doi":"10.1177/17298806221094925","DOIUrl":"https://doi.org/10.1177/17298806221094925","url":null,"abstract":"The accuracy of agricultural unmanned ground vehicles’ localization directly affects the accuracy of their navigation. However, due to the changeable environment and fewer features in the agricultural scene, it is challenging for these unmanned ground vehicles to localize precisely in global positioning system-denied areas with a single sensor. In this article, we present an efficient and adaptive sensor-fusion odometry framework based on simultaneous localization and mapping to handle the localization problems of agricultural unmanned ground vehicles without the assistance of a global positioning system. The framework leverages three kinds of sub-odometry (lidar odometry, visual odometry and inertial odometry) and automatically combines them depending on the environment to provide accurate pose estimation in real time. The combination of sub-odometry is implemented by trading off the robustness and the accuracy of pose estimation. The efficiency and adaptability are mainly reflected in the novel surfel-based iterative closest point method for lidar odometry we propose, which utilizes the changeable surfel radius range and the adaptive iterative closest point initialization to improve the accuracy of pose estimation in different environments. We test our system in various agricultural unmanned ground vehicles’ working zones and some other open data sets, and the results prove that the proposed method shows better performance mainly in accuracy, efficiency and robustness, compared with the state-of-art methods.","PeriodicalId":50343,"journal":{"name":"International Journal of Advanced Robotic Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47311191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/17298806221090077
Gongwu Sun, Jirong Xie, Junqi Qu, Xinguang Li
The horizontal thrusters located as X shape is a commonly used configuration in remotely operated underwater vehicle (ROV). To improve the motion performance of the remotely operated underwater vehicle with this configuration for some typical operation tasks, a multistep thrust allocation method based on priority idea is proposed. Firstly, the thrust allocation result of every single force/moment among the horizontal thrusters is obtained by using a piecewise calculation method. Then, a thrust allocation function of multiple forces and moment is constructed, transforming the thrust allocation problem into a multiparameter optimization problem. The objective function of the optimization problem is established based on priority idea, and limits of the thrusters are considered as constraints. Finally, a thrust redistribution method is adopted in order to further utilize the unsaturated thrusters in the propulsion system. Simulation results show that the proposed thrust allocation method has various advantages over the pseudo-inverse method, which makes the remotely operated underwater vehicle perform perfectly in the given operation task. The proposed method has important practical value and application prospects.
{"title":"Multistep thrust allocation method based on priority idea for remotely operated underwater vehicle with horizontal thrusters configured as X shape","authors":"Gongwu Sun, Jirong Xie, Junqi Qu, Xinguang Li","doi":"10.1177/17298806221090077","DOIUrl":"https://doi.org/10.1177/17298806221090077","url":null,"abstract":"The horizontal thrusters located as X shape is a commonly used configuration in remotely operated underwater vehicle (ROV). To improve the motion performance of the remotely operated underwater vehicle with this configuration for some typical operation tasks, a multistep thrust allocation method based on priority idea is proposed. Firstly, the thrust allocation result of every single force/moment among the horizontal thrusters is obtained by using a piecewise calculation method. Then, a thrust allocation function of multiple forces and moment is constructed, transforming the thrust allocation problem into a multiparameter optimization problem. The objective function of the optimization problem is established based on priority idea, and limits of the thrusters are considered as constraints. Finally, a thrust redistribution method is adopted in order to further utilize the unsaturated thrusters in the propulsion system. Simulation results show that the proposed thrust allocation method has various advantages over the pseudo-inverse method, which makes the remotely operated underwater vehicle perform perfectly in the given operation task. The proposed method has important practical value and application prospects.","PeriodicalId":50343,"journal":{"name":"International Journal of Advanced Robotic Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47431355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/17298806221082398
Kejin Liu, J. Xia, Fei Zhong, Li Zhang
To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot.
{"title":"Structural parameters identification for industrial robot using a hybrid algorithm","authors":"Kejin Liu, J. Xia, Fei Zhong, Li Zhang","doi":"10.1177/17298806221082398","DOIUrl":"https://doi.org/10.1177/17298806221082398","url":null,"abstract":"To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot.","PeriodicalId":50343,"journal":{"name":"International Journal of Advanced Robotic Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44709773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/17298806221084825
Chunyang Hu, Wenping Cao, Bin Ning
Visual servoing is a key approach to achieve visual control for the rotor unmanned helicopter. The challenges of the inaccurate matrix estimation and the target loss restrict the performance of the visual servoing control systems. This work proposes a novel visual servoing controller using the deep Q-network to achieve an efficient matrix estimation. A deep Q-network learning agent learns a policy estimating the interaction matrix for visual servoing of a rotor unmanned helicopter using continuous observation. The observation includes a combination of feature errors. The current matrix and the desired matrix constitute the action space. A well-designed reward guides the deep Q-network agent to get a policy to generate a time-varying linear combination between the current matrix and the desired matrix. Then, the interaction matrix is calculated by the linear combination. The potential mapping between the observation and the interaction matrix is learned by cascading the deep neural network layers. Experimental results show that the proposed method achieves faster convergence and lower target loss probability in tracking than the visual servoing methods with the fixed parameter.
{"title":"Visual servoing with deep reinforcement learning for rotor unmanned helicopter","authors":"Chunyang Hu, Wenping Cao, Bin Ning","doi":"10.1177/17298806221084825","DOIUrl":"https://doi.org/10.1177/17298806221084825","url":null,"abstract":"Visual servoing is a key approach to achieve visual control for the rotor unmanned helicopter. The challenges of the inaccurate matrix estimation and the target loss restrict the performance of the visual servoing control systems. This work proposes a novel visual servoing controller using the deep Q-network to achieve an efficient matrix estimation. A deep Q-network learning agent learns a policy estimating the interaction matrix for visual servoing of a rotor unmanned helicopter using continuous observation. The observation includes a combination of feature errors. The current matrix and the desired matrix constitute the action space. A well-designed reward guides the deep Q-network agent to get a policy to generate a time-varying linear combination between the current matrix and the desired matrix. Then, the interaction matrix is calculated by the linear combination. The potential mapping between the observation and the interaction matrix is learned by cascading the deep neural network layers. Experimental results show that the proposed method achieves faster convergence and lower target loss probability in tracking than the visual servoing methods with the fixed parameter.","PeriodicalId":50343,"journal":{"name":"International Journal of Advanced Robotic Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42889336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/17298806221091885
Gongxing Wu, Taotao Xu, Yu-shan Sun, Jiawei Zhang
In recent years, the research of multiple unmanned surface vessels collaboration has received great attention. More and more researchers have proposed different methods of multiple unmanned surface vessels collaboration, such as cooperative collision avoidance, formation, and rendezvous. Based on the significant advantages of biological swarm intelligence applications in these collaborative methods, this article summarizes the research methods of multiple unmanned surface vessels collaborative search and hunting from the perspective of swarm intelligence. First of all, this article summarizes the key technologies of multiple unmanned surface vessels collaborative search and hunting from the aspects of the multi-robot system, group communication, environment modeling, collaboration mechanism, and path planning. Then, it reviews some classic swarm intelligence algorithms, analyzes the advantages and disadvantages of these algorithms, and proposes optimization directions for existing disadvantages based on relevant literature. Finally, the article points out some existing problems in every stage and suggestions for future research.
{"title":"Review of multiple unmanned surface vessels collaborative search and hunting based on swarm intelligence","authors":"Gongxing Wu, Taotao Xu, Yu-shan Sun, Jiawei Zhang","doi":"10.1177/17298806221091885","DOIUrl":"https://doi.org/10.1177/17298806221091885","url":null,"abstract":"In recent years, the research of multiple unmanned surface vessels collaboration has received great attention. More and more researchers have proposed different methods of multiple unmanned surface vessels collaboration, such as cooperative collision avoidance, formation, and rendezvous. Based on the significant advantages of biological swarm intelligence applications in these collaborative methods, this article summarizes the research methods of multiple unmanned surface vessels collaborative search and hunting from the perspective of swarm intelligence. First of all, this article summarizes the key technologies of multiple unmanned surface vessels collaborative search and hunting from the aspects of the multi-robot system, group communication, environment modeling, collaboration mechanism, and path planning. Then, it reviews some classic swarm intelligence algorithms, analyzes the advantages and disadvantages of these algorithms, and proposes optimization directions for existing disadvantages based on relevant literature. Finally, the article points out some existing problems in every stage and suggestions for future research.","PeriodicalId":50343,"journal":{"name":"International Journal of Advanced Robotic Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45753736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/17298806221086763
Z. Fan, Wang
In the online published article, the second affiliation of author Fan Zhenquan is missing. The affiliations for Fan Zhenquan are following: 1. School of Mechanical Engineering, Dalian Jiaotong University, Dalian, China. 2. CRRC Qingdao Sifang Co., Ltd., Qingdao, China. International Journal of Advanced Robotic Systems March-April 2022: 1 a The Author(s) 2022 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/17298806221086763 journals.sagepub.com/home/arx
{"title":"Erratum to Distributed variable stiffness joint assist mechanism based on laminated structure","authors":"Z. Fan, Wang","doi":"10.1177/17298806221086763","DOIUrl":"https://doi.org/10.1177/17298806221086763","url":null,"abstract":"In the online published article, the second affiliation of author Fan Zhenquan is missing. The affiliations for Fan Zhenquan are following: 1. School of Mechanical Engineering, Dalian Jiaotong University, Dalian, China. 2. CRRC Qingdao Sifang Co., Ltd., Qingdao, China. International Journal of Advanced Robotic Systems March-April 2022: 1 a The Author(s) 2022 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/17298806221086763 journals.sagepub.com/home/arx","PeriodicalId":50343,"journal":{"name":"International Journal of Advanced Robotic Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45991865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/17298806221081665
Hanxu Sun, Mingzhe Li, Jingzhou Song, Yun Wang
Self-reconfigurable robot is a complex system composed of multiple modular robots. Aiming at high efficiency and low energy consumption of self-reconfigurable robot configuration transformation, a self-reconfiguration strategy based on module mapping of the common parts is proposed. This strategy describes the configuration of the robot in the form of a graph, and a method to determine the central node of configuration is proposed. The central node module as the starting node for comparison of different configurations, and the common part between the two configurations is reserved. Then the module closest to the target module is searched, the target configuration is reconfigured from the inside to the outside with the minimum energy consumption constraint. Finally, based on the experiment results, compared with other self-reconfiguration strategies, the proposed self-reconfiguration strategy reduces the times of reconfiguration operations and improves the reconfiguration efficiency.
{"title":"Research on self-reconfiguration strategy of modular spherical robot","authors":"Hanxu Sun, Mingzhe Li, Jingzhou Song, Yun Wang","doi":"10.1177/17298806221081665","DOIUrl":"https://doi.org/10.1177/17298806221081665","url":null,"abstract":"Self-reconfigurable robot is a complex system composed of multiple modular robots. Aiming at high efficiency and low energy consumption of self-reconfigurable robot configuration transformation, a self-reconfiguration strategy based on module mapping of the common parts is proposed. This strategy describes the configuration of the robot in the form of a graph, and a method to determine the central node of configuration is proposed. The central node module as the starting node for comparison of different configurations, and the common part between the two configurations is reserved. Then the module closest to the target module is searched, the target configuration is reconfigured from the inside to the outside with the minimum energy consumption constraint. Finally, based on the experiment results, compared with other self-reconfiguration strategies, the proposed self-reconfiguration strategy reduces the times of reconfiguration operations and improves the reconfiguration efficiency.","PeriodicalId":50343,"journal":{"name":"International Journal of Advanced Robotic Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49285926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/17298814211040688
Li Bian, X. Che, Liu Chengyang, Dai Jiageng, He Hui
Despite advances in modern control theory and artificial intelligence technology, current methods for tuning proportional-integral-derivative (PID) controller parameters based on the traditional particle swarm optimization (PSO) algorithm do not meet the requirements for controlling an unmanned surface vessel (USV) propulsion motor. To overcome the disadvantages of the PSO algorithm, such as low precision and easily falling into a local optimum, the beetle antennae search (BAS) algorithm can be introduced into the PSO algorithm by replacing particles with beetles, and effectively prevents the PSO algorithm from easily falling into the local optimum. At the same time, the BAS algorithm will no longer be limited to single objective parameterization. Herein, we propose a PID parameter optimization method based on the hybrid BAS-PSO algorithm for a USV propulsion motor. The PID parameter optimization of propulsion motor effectively becomes a beetle foraging problem with group optimization. Numerical results show that the method can effectively solve the problems of PSO and greatly improve convergence speed. Compared with the genetic algorithm and standard PSO algorithm, the BAS-PSO algorithm is superior for PID parameter tuning and can improve performance of USV propulsion system.
{"title":"Parameter optimization of unmanned surface vessel propulsion motor based on BAS-PSO","authors":"Li Bian, X. Che, Liu Chengyang, Dai Jiageng, He Hui","doi":"10.1177/17298814211040688","DOIUrl":"https://doi.org/10.1177/17298814211040688","url":null,"abstract":"Despite advances in modern control theory and artificial intelligence technology, current methods for tuning proportional-integral-derivative (PID) controller parameters based on the traditional particle swarm optimization (PSO) algorithm do not meet the requirements for controlling an unmanned surface vessel (USV) propulsion motor. To overcome the disadvantages of the PSO algorithm, such as low precision and easily falling into a local optimum, the beetle antennae search (BAS) algorithm can be introduced into the PSO algorithm by replacing particles with beetles, and effectively prevents the PSO algorithm from easily falling into the local optimum. At the same time, the BAS algorithm will no longer be limited to single objective parameterization. Herein, we propose a PID parameter optimization method based on the hybrid BAS-PSO algorithm for a USV propulsion motor. The PID parameter optimization of propulsion motor effectively becomes a beetle foraging problem with group optimization. Numerical results show that the method can effectively solve the problems of PSO and greatly improve convergence speed. Compared with the genetic algorithm and standard PSO algorithm, the BAS-PSO algorithm is superior for PID parameter tuning and can improve performance of USV propulsion system.","PeriodicalId":50343,"journal":{"name":"International Journal of Advanced Robotic Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48125005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/17298806221089198
V. Rosas-Cervantes, Quoc-Dong Hoang, S. Woo, Soon‐Geul Lee
Nowadays, multi-sensor fusion is a popular tool for feature recognition and object detection. Integrating various sensors allows us to obtain reliable information about the environment. This article proposes a 3D robot trajectory estimation based on a multimodal fusion of 2D features extracted from color images and 3D features from 3D point clouds. First, a set of images was collected using a monocular camera, and we trained a Faster Region Convolutional Neural Network. Using the Faster Region Convolutional Neural Network, the robot detects 2D features from camera input and 3D features using the point’s normal distribution on the 3D point cloud. Then, by matching 2D image features to a 3D point cloud, the robot estimates its position. To validate our results, we compared the trained neural network with similar convolutional neural networks. Then, we evaluated their response for the mobile robot trajectory estimation.
{"title":"Mobile robot 3D trajectory estimation on a multilevel surface with multimodal fusion of 2D camera features and a 3D light detection and ranging point cloud","authors":"V. Rosas-Cervantes, Quoc-Dong Hoang, S. Woo, Soon‐Geul Lee","doi":"10.1177/17298806221089198","DOIUrl":"https://doi.org/10.1177/17298806221089198","url":null,"abstract":"Nowadays, multi-sensor fusion is a popular tool for feature recognition and object detection. Integrating various sensors allows us to obtain reliable information about the environment. This article proposes a 3D robot trajectory estimation based on a multimodal fusion of 2D features extracted from color images and 3D features from 3D point clouds. First, a set of images was collected using a monocular camera, and we trained a Faster Region Convolutional Neural Network. Using the Faster Region Convolutional Neural Network, the robot detects 2D features from camera input and 3D features using the point’s normal distribution on the 3D point cloud. Then, by matching 2D image features to a 3D point cloud, the robot estimates its position. To validate our results, we compared the trained neural network with similar convolutional neural networks. Then, we evaluated their response for the mobile robot trajectory estimation.","PeriodicalId":50343,"journal":{"name":"International Journal of Advanced Robotic Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49264508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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