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Study on the internal legalization process of strategic change for a large number of ultra-large enterprises in China.

This paper takes formulation process of Suning Appliance Group’s 10-year strategy (2010–2020) as the research case, designs the research issues, propositions and analysis unit of the case study, and uses the data collection and analysis methods in the grounded theory to realize the theoretical development from data to viewpoint conceptualizing and to proposition categorizing.

There are four key concepts that affect the judgment of overall strategic legitimacy of super-large enterprises: Emerging-market opportunities and strategic operational positioning, legitimacy perspective mainly manifests as legitimacy judgment of strategic direction within organization. Positioning of core resources (including intangible resources) and their value identification methods or value evaluation criteria, the legitimacy perspective is mainly reflected in the organization's internal legitimacy judgment of functional planning, especially implementation path. The impact factors of the key performance of each SBU are positioned, and the legitimacy perspective is mainly reflected in the organization’s internal judgment on the legitimacy of strategic supporting measures, especially the resources needed for the implementation of the strategy and capacity development. The periodical strategic objectives and performance measurement indicators of each SBU are mainly reflected in the organization’s internal legitimacy judgment on strategic alignment and specific action plans for strategic operational units. The legitimacy of these four key concepts is strongly influenced by the rationality of these strategic concepts, which are closely related to their shaping patterns driven by right-brain and left-brain thinking modes.

This case is a longitudinal study of the strategic decision-making process, not a longitudinal follow-up of the actual implementation of the strategy. In addition, given that the case enterprise was facing the emerging market at that time and focused on pushing firms to seize opportunities, not much research has been done on the impact of external legitimacy on the strategic formulation process, a variable that is increasingly being focused on today.

This model has guidance significance and practical demonstration role for a large number of enterprises that are implementing the “+Internet” strategic change under traditional offline operation.

According to the summary of the connection between data and propositions in several rounds, this paper constructs a theoretical model of left and right brain thinking mode driving key concepts to achieve the internal legalization process of strategic changes.

In the analysis process, the legalization theory and the sense-making method are introduced into enterprises’ strategy making process. Based on this analysis framework, this paper analyzes in detail that the top decision-making level and the middle and high executive level form key strategic concepts to promote the internal legalization process of strategic decision-making driven by the right-brain intuitive thinking mode and the left-brain rational thinking mode, which greatly improves the quality of strategy formulation and the operability of strategy implementation.

In multi-robot cooperation, the cloud can share sensor data, which can help robots better perceive the environment. For cloud robotics, robot grasping is an important ability that must be mastered. Usually, the information source of grasping mainly comes from visual sensors. However, due to the uncertainty of the working environment, the information acquisition of the vision sensor may encounter the situation of being blocked by unknown objects. This paper aims to propose a solution to the problem in robot grasping when the vision sensor information is blocked by sharing the information of multi-vision sensors in the cloud.

First, the random sampling consensus algorithm and principal component analysis (PCA) algorithms are used to detect the desktop range. Then, the minimum bounding rectangle of the occlusion area is obtained by the PCA algorithm. The candidate camera view range is obtained by plane segmentation. Then the candidate camera view range is combined with the manipulator workspace to obtain the camera posture and drive the arm to take pictures of the desktop occlusion area. Finally, the Gaussian mixture model (GMM) is used to approximate the shape of the object projection and for every single Gaussian model, the grabbing rectangle is generated and evaluated to get the most suitable one.

In this paper, a variety of cloud robotic being blocked are tested. Experimental results show that the proposed algorithm can capture the image of the occluded desktop and grab the objects in the occluded area successfully.

In the existing work, there are few research studies on using active multi-sensor to solve the occlusion problem. This paper presents a new solution to the occlusion problem. The proposed method can be applied to the multi-cloud robotics working environment through cloud sharing, which helps the robot to perceive the environment better. In addition, this paper proposes a method to obtain the object-grabbing rectangle based on GMM shape approximation of point cloud projection. Experiments show that the proposed methods can work well.