On design of cognitive situation-adaptive autonomous mobile robotic applications
通过模拟地下矿山的案例研究，提出了一种认知情境自适应自主移动机器人应用的设计方法，包含三个设计方面，并基于原型系统进行了实证评估。
Fostered by the recent development in artificial intelligence technologies, digitalization in industries is proceeding towards intelligent automation of various physical work processes with autonomous robotic applications , in dynamic and non-deterministic environments, and in collaboration with human workers. The article presents an explorative case study on designing a cognitive situation-adaptive Autonomous Mobile Robotics (AMR) application for material hauling, in a simulated underground mining context. The goal of the research is to synthesize and present new design knowledge for improving situation-adaptation capabilities of AMR applications, which are increasingly required as the operational environments for the AMRs become dynamic, non-deterministic, and include people working on the same area with the robots. The research applies design science research methodology, and evaluates the results empirically via a prototype system, which is demonstrated in laboratory setting simulating an underground tunnel network. As an outstanding contribution, the results contribute a novel, nascent, and empirically evaluated design approach, which proposes three design aspects combining design and engineering activities across the systems engineering, knowledge engineering , computer science and robotics disciplines. Empirical evaluation is made via design, development, and demonstration of a system architecture and prototype system of a cognitive situation-adaptive AMR application, which is used in synthesis and evaluation of the design approach. The three design aspects proposed by the approach are 1) Context of operation, 2) Knowledge-driven behaviour, and 3) Knowledge driven operation. Also design challenges, future research and development needs, and innovation potential on designing of cognitive situation-adaptive AMR applications for industrial use are identified and discussed.