How to deal with the complexity in robotic systems?

Complex engineering systems (CES) have proven their ability to describe a wide range of practical applications: logistics, transportation systems, smart grids, communication networks, networked robotics networks, etc. An inherent feature of these systems is the interaction of multiple independent systems, which can be exhibited as a system of systems[1] (SoS) at large. For instance, within the context of smart grid, the paper entitled A distributed electricity energy trading strategy under energy shortage environment[2] puts forward a new power dispatch strategy based on the aggregative game theory and Cournot price mechanism for the grid network. The purpose of this dispatch strategy is to enhance the resilience of electric power, in particular, solving the power shortage problem using a distributed algorithm. The distributed algorithm can provide privacy protection and information safety and improve the power grid’s extendibility[2]. Recently, robotic systems are frequently used to automate the inspection of distributed or decentralized power infrastructure, which enhances the system’s reliability and decreases the operation and maintenance costs. The main characteristics of CES, including[3] adaptation, self-organization, and emergence, make it so that CES is generally modeled in terms of a set of interconnected systems whose global behaviors are somewhat difficult to predict or manage. In this aspect, emergence in robotics can be realized through human-robot interaction or increasing robot autonomy by developing machine learning algorithms.