The knowledge representation and programming for robotic assembly task

Aramaki, Shigeto; Nagasawa, Isao; Kurono, Shigeru; Nagai, Tatsuichiro; Morita, T.; Suetsugu, Takeshi

doi:10.1109/isatp.1999.782968

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…pre-condition, post-condition, input and results. In another work of knowledge representation in action sequencing [28] four levels of hierarchical structure was represented, i.e. control primitive level, control skill level, skill level and task level, where an action sequence is considered as a process.…”

Section: Ontologies and Other Knowledge Representation Techniquesmentioning

confidence: 99%

Approaches for Action Sequence Representation in Robotics: A Review

Nakawala

Gonçalves

Fiorini

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Robust representation of actions and its sequences for complex robotic tasks would transform robot's understanding to execute robotic tasks efficiently. The challenge is to understand action sequences for highly unstructured environments and to represent and construct action and action sequences. In this manuscript, we present a review of literature dealing with representation of action and action sequences for robot task planning and execution. The methodological review was conducted using Google Scholar and IEEE Xplore, searching the specific keywords. This manuscript gives an overview of current approaches for representing action sequences in robotics. We propose a classification of different methodologies used for action sequences representation and describe the most important aspects of the reviewed publications. This review allows the reader to understand several options that do exist in the research community, to represent and deploy such action representations in real robots.

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Section: Ontologies and Other Knowledge Representation Techniquesmentioning

confidence: 99%

Approaches for Action Sequence Representation in Robotics: A Review

Nakawala

Gonçalves

Fiorini

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Nicolescu [21] considered ‘a task to be an activity involving the coordination of an existing set of skills in order to achieve a given set of goals’. Aramaki et al [22] considered that a task sequence is defined by concatenation of skills. Gilbreth [4] investigated 18 types of elemental motions (so-called therbligs) to represent human motion used in a workspace.…”

Section: Review Of Related Researchmentioning

confidence: 99%

Understanding Human Hand Gestures for Learning Robot Pick-and-Place Tasks

Lin

Chiang

2015

International Journal of Advanced Robotic Systems

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Programming robots by human demonstration is an intuitive approach, especially by gestures. Because robot pick-and-place tasks are widely used in industrial factories, this paper proposes a framework to learn robot pick-and-place tasks by understanding human hand gestures. The proposed framework is composed of the module of gesture recognition and the module of robot behaviour control. For the module of gesture recognition, transport empty (TE), transport loaded (TL), grasp (G), and release (RL) from Gilbreth's therbligs are the hand gestures to be recognized. A convolution neural network (CNN) is adopted to recognize these gestures from a camera image. To achieve the robust performance, the skin model by a Gaussian mixture model (GMM) is used to filter out non-skin colours of an image, and the calibration of position and orientation is applied to obtain the neutral hand pose before the training and testing of the CNN. For the module of robot behaviour control, the corresponding robot motion primitives to TE, TL, G, and RL, respectively, are implemented in the robot. To manage the primitives in the robot system, a behaviour-based programming platform based on the Extensible Agent Behavior Specification Language (XABSL) is adopted. Because the XABSL provides the flexibility and re-usability of the robot primitives, the hand motion sequence from the module of gesture recognition can be easily used in the XABSL programming platform to implement the robot pick-and-place tasks. The experimental evaluation of seven subjects performing seven hand gestures showed that the average recognition rate was 95.96%. Moreover, by the XABSL programming platform, the experiment showed the cube-stacking task was easily programmed by human demonstration.

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