On design of cognitive situation-adaptive autonomous mobile robotic applications

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. • A design approach for cognitive situation-adaptive robotics is adopted. • A cognitive situation-adaptive AMR prototype for material hauling is developed. • Neurosymbolic AI enabling situation-adaptation in AMR applications is achieved.