This book describes an approach to intelligent task planning in a robotic system. Petri net and fuzzy logic are integrated and used to represent task sequence planning and error recovery. During the generation and execution of task plans, different kinds of uncertainties need to be handled to ensure the efficiency and reliability of the system. Following a systematic modeling procedure, a fuzzy Petri net is constructed based on geometric relations, fuzzy variables, and reasoning structures. The resulting net can be used to analyze and control the system. Many examples are discussed to illustrate the theory and the applications of fuzzy Petri nets.
Background; Petri nets and task planning; and/or net representation for robotic task sequence planning; task decomposition and analysis of robotic assembly task plans using Petri nets; representation and analysis of uncertainty using fuzzy Petri nets; task sequence planning using fuzzy Petri nets; sensor-based error recovery for task sequence plans using fuzzy Petri nets; summary.