Spatial semantic hybrid map building and application of mobile service robot

Wu, Hao; Tian, Guohui; Li, Yan; Zhou, Fengyu; Duan, Ping

doi:10.1016/j.robot.2013.01.001

Cited by 31 publications

(20 citation statements)

References 21 publications

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“…Being aware of the implications of the choice of technology and the level of abstraction to be used in the mapping, Wu et al, considered of great importance the application that has the mapping in service robots and present a hybrid model of the semantic map [98]. Classifying the navigation maps into two categories: the metric maps and the topological maps.…”

Section: Semantic Mapsmentioning

confidence: 99%

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Semantic Information for Robot Navigation: A Survey

et al. 2020

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There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situations.

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Section: Semantic Mapsmentioning

confidence: 99%

“…Nor are high-level semantic concepts used to interact with people. This situation where the robot planner, location and navigation based on these types of maps do not meet the needs of the robot's service tasks, motivated the definition of semantic maps [98], as a more suitable option than cognitive maps.…”

Section: Semantic Mapsmentioning

confidence: 99%

Semantic Information for Robot Navigation: A Survey

et al. 2020

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“…Rostkowska and Topolski have followed the QR code for robots in the workplace using a low cost camera. The researchers take the QR codes placed on the top of the robots in their work and the robot's direction and position information instantaneously [7]. Zhang and his colleagues used the QR code matrix in the upper part of the study area to direct and position the robot.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Mobile Robots Based on QR Code

Atali¹,

Garip²,

Özkan³

et al. 2018

acperpro

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The place and importance of mobile robots in today's industry is increasing day by day. The use of mobile robots for industrial purposes such as sorting, moving and placing is no longer a preference but a necessity. With methods such as line, magnetic field, infrared ray or image tracking, positioning of mobile robots and path planning are possible. The use of these methods depends on the application fields. The industry started with mobile line following robot&rsquo;s application at the beginning, gradually they have been developing day by day, and pursuits for different methods. If the topic examined under the heading of Industry 4.0, it is necessary to equip mobile robots with innovative technologies. The need for mobile robots that can provide fast, effective and practical solutions is being investigated by academics and commercial product developers.In this study, it is aimed to carry out a QR code based study on path planning of industrial mobile robots with distributed mobility. For this purpose, coordinates of the mobile robot work area are represented by QR labels and prepared in the form of a matrix. This way, developed path planning algorithm can be evaluated as a derivative of existing algorithms.

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“…Mobile robots are increasingly present in the industrial [1][2][3] and service robotics [4][5][6], particularly when motion control is becoming a hot topic in the robot field. Mobile robot depends on several controlled variables.…”

Section: Introductionmentioning

confidence: 99%

Research on Open-Closed-Loop Iterative Learning Control with Variable Forgetting Factor of Mobile Robots

Wang

Dong

Wang

2016

Discrete Dynamics in Nature and Society

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We propose an iterative learning control algorithm (ILC) that is developed using a variable forgetting factor to control a mobile robot. The proposed algorithm can be categorized as an open-closed-loop iterative learning control, which produces control instructions by using both previous and current data. However, introducing a variable forgetting factor can weaken the former control output and its variance in the control law while strengthening the robustness of the iterative learning control. If it is applied to the mobile robot, this will reduce position errors in robot trajectory tracking control effectively. In this work, we show that the proposed algorithm guarantees tracking error bound convergence to a small neighborhood of the origin under the condition of state disturbances, output measurement noises, and fluctuation of system dynamics. By using simulation, we demonstrate that the controller is effective in realizing the prefect tracking.

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Spatial semantic hybrid map building and application of mobile service robot

Cited by 31 publications

References 21 publications

Semantic Information for Robot Navigation: A Survey

Semantic Information for Robot Navigation: A Survey

Path Planning of Mobile Robots Based on QR Code

Research on Open-Closed-Loop Iterative Learning Control with Variable Forgetting Factor of Mobile Robots

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