Sensor-based error recovery for robotic task sequences using fuzzy Petri nets

The authors address the problem of representing and automatically invoking error recovery sequences in response to sensed error during execution. The approach is based on the use of a fuzzy Petri net model in which sensory verification operations determine fuzzy values of tokens in the net. The outcome of a sensory verification operation changes the fuzzy values of tokens and leads to an altered firing sequence and resulting error recovery. An algorithm is described for adding sensory verification transitions and associated fuzzy transition rules which implement error recovery through retry or alternative sequence mechanisms