Multi-Objective Location and Mapping Based on Deep Learning and Visual Slam

Sun, Ying; Hu, Jun; Yun, Juntong; Liu, Ying; Bai, Dongxu; Liu, Xin; Zhao, Guangbo; Jiang, Guozhang; Kong, Jianyi; Chen, Baojia

doi:10.3390/s22197576

Cited by 20 publications

(14 citation statements)

References 67 publications

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“…Experimental study of the control algorithm by combining the hardware and software of the manipulator. An experimental system for the motion trajectory of the manipulator is set up to experiment with the control algorithm designed for adaptive fuzzy SMC of the robot trajectory tracking 92–94 …”

Section: Dobot Magician Manipulator Experimentsmentioning

confidence: 99%

Manipulator trajectory tracking based on adaptive fuzzy sliding mode control

Zhao

Tao

et al. 2023

Concurrency and Computation

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Sliding mode control is one of the common control methods for manipulators, but the discontinuity of sliding mode control can cause jitter and vibration of the manipulator system. This article takes the Dobot Magician manipulator as the research object and constructs a simplified model of the manipulator dynamics. And the adaptive fuzzy sliding mode control method is designed in combination with fuzzy control theory, which effectively solves the jitter problem of the control torque. In the MATLAB/Simulink simulation environment analysis show that the proposed adaptive fuzzy sliding mode control method solves the jitter problem in sliding mode control with good stability, robustness and tracking performance. By combining the adaptive fuzzy sliding mode control method with the manipulator and comparing the motion time of the same motion trajectory with the PID control method in Dobot Studio, the hardware experiment results effectively verify the feasibility and effectiveness of the designed control method.

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Section: Dobot Magician Manipulator Experimentsmentioning

confidence: 99%

Manipulator trajectory tracking based on adaptive fuzzy sliding mode control

Zhao

Tao

et al. 2023

Concurrency and Computation

Self Cite

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“…A method for constructing object-oriented semantic maps developed in [273]. It combines the semantic information extracted from instance segmentation with RGB-D version of ORB-SLAM2 [274].…”

Section: Simultaneous Localization and Mapping (Slam)mentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Golroudbari¹,

Sabour²

2023

Preprint

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This review paper presents a comprehensive overview of end-to-end deep learning frameworks used in the context of autonomous navigation, including obstacle detection, scene perception, path planning, and control. The paper aims to bridge the gap between autonomous navigation and deep learning by analyzing recent research studies and evaluating the implementation and testing of deep learning methods. It emphasizes the importance of navigation for mobile robots, autonomous vehicles, and unmanned aerial vehicles, while also acknowledging the challenges due to environmental complexity, uncertainty, obstacles, dynamic environments, and the need to plan paths for multiple agents. The review highlights the rapid growth of deep learning in engineering data science and its development of innovative navigation methods. It discusses recent interdisciplinary work related to this field and provides a brief perspective on the limitations, challenges, and potential areas of growth for deep learning methods in autonomous navigation. Finally, the paper summarizes the findings and practices at different stages, correlating existing and future methods, their applicability, scalability, and limitations. The review provides a valuable resource for researchers and practitioners working in the field of autonomous navigation and deep learning.

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“…Meanwhile, SLAM determines the position and pose of a robot as it moves, and it can simultaneously map the environment. Considering that SLAM is a pivotal component of truly autonomous robots [24], it has been widely used in Refs. [21,25].…”

Section: Positioning and Trackingmentioning

confidence: 99%

Intelligent robots and human–robot collaboration in the construction industry: A review

Wei¹,

Zhang²,

Sun³

et al. 2023

Journal of Intelligent Construction

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Keywords human-robot collaboration (HRC) robotics innovation adaption taxonomyThe construction industry is a typical labor-intensive industry, which suffers from low productivity and labor shortage in the past decades. Recently, the developments in robotics and artificial intelligence technologies highlight the evolutionary reforming potential in the construction industry. An increasing number of robots are joining construction tasks and collaborating with human workers. This study reviews the major developments in intelligent robots and human-robot collaboration (HRC) in the construction industry. The technological foundations and fundamental concepts of construction robots and collaborative robots are reviewed, organized, and discussed to reveal that progress has been made. Based on a comprehensive review, the major challenges and future research directions of HRC have been proposed and examined. This study finally developed a comprehensive and in-depth discussion of the state-ofthe-art implementation of robotics technologies in the construction industry and shed light on its path to future development.

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Multi-Objective Location and Mapping Based on Deep Learning and Visual Slam

Cited by 20 publications

References 67 publications

Manipulator trajectory tracking based on adaptive fuzzy sliding mode control

Manipulator trajectory tracking based on adaptive fuzzy sliding mode control

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Intelligent robots and human–robot collaboration in the construction industry: A review

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