Material-integrated cluster computing in self-adaptive robotic materials using mobile multi-agent systems

Recent trends like internet-of-things (IoT) and internet-of-everything (IoE) require new distributed computing and communication approaches as size of interconnected devices moves from a cm 3 - to the sub-mm 3 -scale. Technological advance behind size reduction will facilitate integration of networked computing on material rather than structural level, requiring algorithmic and architectural scaling towards distributed computing. Associated challenges are linked to use of low reliability, large scale computer networks operating on low to very low resources in robotic materials capable of performing cluster computing on micro-scale. Networks of this type need superior robustness to cope with harsh conditions of operation. These can be provided by self-organization and-adaptivity. On macro scale, robotic materials afford unified distributed data processing models to allow their connection to smart environments like IoT/IoE. The present study addresses these challenges by applying mobile Multi-agent systems (MAS) and an advanced JavaScript agent processing platform (JAM), realizing self-adaptivity as feature of both data processing and the mechanical system itself. The MAS' task is to solve a distributed optimization problem using a mechanically adaptive robotic material in which stiffness is increased via minimization of elastic energy. A practical realization of this example necessitates environmental interaction and perception, demonstrated here via a reference architecture employing a decentralized approach to control local property change in service based on identification of the loading situation. In robotic materials, such capabilities can support actuation and/or lightweight design, and thus sustainability.