Self-reconfiguring robotic framework using fuzzy and ontological decision making

Abstract

Advanced automation requires complex robotic systems that are susceptible to mechanical, software and sensory failures. While bespoke solutions exist to avoid such situations, there is a requirement to develop generic robotic framework that can allow autonomous recovery from anomalous conditions through hardware or software reconfiguration. This paper presents a novel robotic architecture that combines fuzzy reasoning with ontology-based deliberative decision making to enable self-reconfigurability within a complex robotic system architecture. The fuzzy reasoning module incorporates multiple types of fuzzy inference models that passively monitor the constituent sub-systems for any anomalous changes. Aresponse is generated in retrospect of this monitoring process that is sent to an Ontology-based rational agent in order to perform system reconfiguration. A reconfiguration routine is generated to maintain optimal performance within such complex architectures. The current research work will apply the proposed framework to the problem of autonomous visual navigation of unmanned ground vehicles. An increase in system performance is observed every time a reconfiguration routine is triggered. Experimental analysis is carried out using real-world data, concluding that the proposed system concept gives superior performance against non-reconfigurable robotic frameworks.