Enhanced Robotic Hand–Eye Coordination Inspired From Human-Like Behavioral Patterns

Chao, Fei; Zhu, Zuyuan; Lin, Chunshui; Hu, Huosheng; Yang, Longzhi; Shang, Changjing; Zhou, Changle

doi:10.1109/tcds.2016.2620156

Cited by 13 publications

(10 citation statements)

References 48 publications

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“…After the first two steps of training, the robot developed fixation and saccade abilities, and thus the robot can play the simple game (Chao et al, 2016 ). In the simple game mode, the third and fourth steps of the lift-constraint strategy are executed, and the robot develops hand-eye coordination and near-body grasping.…”

Section: The Proposed Methodsmentioning

confidence: 99%

A Developmental Learning Approach of Mobile Manipulator via Playing

Zhou

Chao

et al. 2017

Front. Neurorobot.

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Inspired by infant development theories, a robotic developmental model combined with game elements is proposed in this paper. This model does not require the definition of specific developmental goals for the robot, but the developmental goals are implied in the goals of a series of game tasks. The games are characterized into a sequence of game modes based on the complexity of the game tasks from simple to complex, and the task complexity is determined by the applications of developmental constraints. Given a current mode, the robot switches to play in a more complicated game mode when it cannot find any new salient stimuli in the current mode. By doing so, the robot gradually achieves it developmental goals by playing different modes of games. In the experiment, the game was instantiated into a mobile robot with the playing task of picking up toys, and the game is designed with a simple game mode and a complex game mode. A developmental algorithm, “Lift-Constraint, Act and Saturate,” is employed to drive the mobile robot move from the simple mode to the complex one. The experimental results show that the mobile manipulator is able to successfully learn the mobile grasping ability after playing simple and complex games, which is promising in developing robotic abilities to solve complex tasks using games.

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Section: The Proposed Methodsmentioning

confidence: 99%

A Developmental Learning Approach of Mobile Manipulator via Playing

Zhou

Chao

et al. 2017

Front. Neurorobot.

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“…Once the target object is found, the robot's hand-eye coordination system automatically performs capturing movements using the manipulator. The implementation details of this subsystem is beyond the focus of this paper and thus omitted due to space limit but can be found in the work of [24,25].…”

Section: Overall Control Proceduresmentioning

confidence: 99%

“…When the human users intend to let the mobile robot capture an object, which appears within the robot's workspace, the human user straightens the arms and holds the hands together in front of the chest. If the mobile robot receives this command, the manipulator module is invoked to complete an object-capturing movement (refer to [24,25] for more details on the implementation on the object-capturing ability).…”

Section: Fuzzy Controller Modulementioning

confidence: 99%

Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller

et al. 2018

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Mobile robots with manipulators have been more and more commonly applied in extreme and hostile environments to assist or even replace human operators for complex tasks. In addition to autonomous abilities, mobile robots need to facilitate the human-robot interaction control mode that enables human users to easily control or collaborate with robots. This paper proposes a system which uses human gestures to control an autonomous mobile robot integrating a manipulator and a video surveillance platform. A human user can control the mobile robot just as one drives an actual vehicle in the vehicle's driving cab. The proposed system obtains human's skeleton joints information using a motion sensing input device, which is then recognized and interpreted into a set of control commands. This is implemented, based on the availability of training data set and requirement of in-time performance, by an adaptive cerebellar model articulation controller neural network, a finite state machine, a fuzzy controller and purposely designed gesture recognition and control command generation systems. These algorithms work together implement the steering and velocity control of the mobile robot in real-time. The experimental results demonstrate that the proposed approach is able to conveniently control a mobile robot using virtual driving method, with smooth manoeuvring trajectories in various speeds.

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“…Note that robotics has taken the centre in the control field to perform tasks from basic robot calligraphy system [94] to complex tasks which require hand-eye (camera) coordination [95]. FISs can also be applied to such advanced areas in the field of robotics, which require further investigation.…”

Section: Modern Fuzzy Control Systems and Its Applicationsmentioning

confidence: 99%

Fuzzy Interpolation Systems and Applications

Yang¹,

Zuo²,

Chao³

et al. 2017

Modern Fuzzy Control Systems and Its Applications

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Fuzzy inference systems provide a simple yet effective solution to complex non-linear problems, which have been applied to numerous real-world applications with great success. However, conventional fuzzy inference systems may suffer from either too sparse, too complex or imbalanced rule bases, given that the data may be unevenly distributed in the problem space regardless of its volume. Fuzzy interpolation addresses this. It enables fuzzy inferences with sparse rule bases when the sparse rule base does not cover a given input, and it simplifies very dense rule bases by approximating certain rules with their neighbouring ones. This chapter systematically reviews different types of fuzzy interpolation approaches and their variations, in terms of both the interpolation mechanism (inference engine) and sparse rule base generation. Representative applications of fuzzy interpolation in the field of control are also revisited in this chapter, which not only validate fuzzy interpolation approaches but also demonstrate its efficacy and potential for wider applications.

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Enhanced Robotic Hand–Eye Coordination Inspired From Human-Like Behavioral Patterns

Cited by 13 publications

References 48 publications

A Developmental Learning Approach of Mobile Manipulator via Playing

A Developmental Learning Approach of Mobile Manipulator via Playing

Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller

Fuzzy Interpolation Systems and Applications

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