Rethinking the system integration and classification of intelligent mobile robots: A meta analysis

• Main Authors: ChenYuan Chen, 陳震遠
• Other Authors: MengHsiang Hsu
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/6fwf5n

博士 === 國立高雄第一科技大學 === 管理學院博士班 === 102 === Although there have been numerous studies about the mechanical control of autonomous intelligent robots and human-robot interactions, there has been little discussion of the whole system within a local area required to manage numerous robots with cross-platform and cross-language features. Reports on the deployment or placing of robots in local areas have revealed more about what comprise effective and low-cost robots without taking into consideration the weight and size of the objects. Current recognition systems for human-robot interaction should allow for quick behavioral decisions and the feedback for different objects is important. However, the natural environment is extremely complex and there are many factors that will affect the behavior of a robot. This study proposes a conceptual model that will provide satisfactory optimal mechanical control anywhere and be expandable to any type of area. A divide and conquer strategy is taken to the problem by solving small problems in the whole system. Recent studies have evaluated categories to help determine where the main attention should be directed. In this review study we look at how to construct a proposed system within a local area then expand it to a wider area using Python. The development environment communicates with each robot and device through connection modules, thus the individual robot does not require any specific hardware other than the communication channel. Developers can monitor the status of the robot through the custom interface. Before the intelligent development of robot module functions, the robot must have the connection device and protocol installed. This system allows developers to extend their own function modules, or build associations with the existing modules to save time required for development. Classification is a practical technique that generates classes which make it possible to predict and describe the behavior of a variable based on the characteristics of a dataset. Despite an extensive literature on robotics, a solid conceptual understanding and classification of mechanical control and mobile navigation has not yet been achieved. In Part II of this study, we develop a conceptual robotic model that helps improve robotic technology management through specific hardware and increase knowledge about the utilization of dynamic mobile robots. The intelligent robot control system is classified into four control systems and six navigation management units based on their features to permit sustainable and independent development. The classification definitions are likely to be applicable for relationships with the other robots.