A fuzzy Petri net approach to reasoning about uncertainty in robotic systems

A generalized definition of the fuzzy Petri net (FPN) and the reasoning structures of transitions in the PFN are proposed. A fuzzy Petri net is used to model the incomplete, uncertain, and approximate information associated with firing of transitions and changing of states in robotics and manufacturing systems. Using PFNs to model a system, a fuzzy reasoning strategy can be used to infer new fuzzy values in output places after the corresponding enabled transition is fired. A global fuzzy variable is used to sequence operations with key procedure relations for a manufacturing system. A local fuzzy variable is used to represent the uncertainty in local configuration variables of the system and may be used to control online reasoning about sensor-based execution. Properties of boundedness, liveness, and reversibility of the resulting FPN depend on the reasoning rules, and formal analysis of these properties is being pursued