A Developmental Learning Approach of Mobile Manipulator via Playing

Wu, Ruiqi; Zhou, Changle; Chao, Fei; Zhu, Zuyuan; Lin, Chunshui; Yang, Longzhi

doi:10.3389/fnbot.2017.00053

Cited by 4 publications

(3 citation statements)

References 45 publications

(50 reference statements)

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“…The robot will have some new abilities after it has experienced all the constrained conditions. The LCAS algorithm has been successfully applied to several developmental models [32], [33]. Based on the LCAS algorithm, the calligraphic robot first develops a rough stroke style and then gradually generates more detailed stroke shapes.…”

Section: Background Knowledgementioning

confidence: 99%

A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing

Chao

Zhou

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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Section: Background Knowledgementioning

confidence: 99%

A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing

Chao

Zhou

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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“…Additionally, non-traditional methods such as robot learning based on infant development theory has been presented in [94]. In this setting, the mobile manipulator is made to play games with varying difficulty as shown in Fig.…”

Section: Moving Smartly (Path and Motion Planning)mentioning

confidence: 99%

A review of the challenges in mobile manipulation: systems design and RoboCup challenges

Sereinig

Werth

Faller

2020

Elektrotech. Inftech.

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Mobile robotics is already well established in today’s production lines. Navigation, control and perception for mobile robots are vivid fields of research fostering advances in Industry 4.0. In order to increase the flexibility of such mobile platforms, it is also common practice to add serial manipulator arms to their yielding systems with nine degrees of freedom and more. These platforms are not limited to industry but are supportive in various field such as service, assistance, teleoperation and also rehabilitation. Due to the operation of such increasingly complex systems in less structured and dynamic environments - often in close contact with humans - more demanding challenges evolve in terms of systems design, control and sensors. These challenges are also reflected in the various RoboCup leagues. In this paper, we discuss state-of-the-art developments in mobile manipulation using developments and work done in the context of the RoboCup competition as design examples. Additionally, we elaborate on the recent challenges of the RoboCup Rescue League as well as on the RoboCup@Work League.

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“…The mobile manipulator robot fixes the traditional manipulator on the mobile platform, thereby extending the substantial potential applications of the fixed manipulator [1,2]. Due to the combination of the high mobility of the mobile platform and the dexterous maneuverability of the manipulator, it can be widely used in current life and work, such as in planetary exploration, nuclear reactor maintenance, landmine detection, agriculture missions, and fire rescue operations [3][4][5]. The mobile manipulator will be able to smoothly complete several complex tasks if the degree of freedom of the manipulator system is increased and a more reasonable structure for the entire system is designed.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Kinematic Modeling and Self-Collision Detection of a Mobile Manipulator Robot by Considering the Actual Physical Structure

Qiao

Luo

et al. 2021

Applied Sciences

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In this paper, an optimized kinematic modeling method to accurately describe the actual structure of a mobile manipulator robot with a manipulator similar to the universal robot (UR5) is developed, and an improved self-collision detection technology realized for improving the description accuracy of each component and reducing the time required for approximating the whole robot is introduced. As the primary foundation for trajectory tracking and automatic navigation, the kinematic modeling technology of the mobile manipulator has been the subject of much interest and research for many years. However, the kinematic model established by various methods is different from the actual physical model due to the fact that researchers have mainly focused on the relationship between driving joints and the end positions while ignoring the physical structure. To improve the accuracy of the kinematic model, we present a kinematic modeling method with the addition of key points and coordinate systems to some components that failed to model the physical structure based on the classical method. Moreover, self-collision detection is also a primary problem for successfully completing the specified task of the mobile manipulator. In traditional self-collision detection technology, the description of each approximation is determined by the spatial transformation of each corresponding component in the mobile manipulator robot. Unlike the traditional technology, each approximation in the paper is directly established by the physical structure used in the kinematic modeling method, which significantly reduces the complicated analysis and shortens the required time. The numerical simulations prove that the kinematic model with the addition of key point technology is similar to the actual structure of mobile manipulator robots, and the self-collision detection technology proposed in the article effectively improves the performance of self-collision detection. Additionally, the experimental results prove that the kinematic modeling method and self-collision detection technology outlined in this paper can optimize the inverse kinematics solution.

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A Developmental Learning Approach of Mobile Manipulator via Playing

Cited by 4 publications

References 45 publications

A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing

A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing

A review of the challenges in mobile manipulation: systems design and RoboCup challenges

Optimizing Kinematic Modeling and Self-Collision Detection of a Mobile Manipulator Robot by Considering the Actual Physical Structure

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