Diagnosis makes the difference for a successful execution of high-level robot control programs

Abstract

Faults in action execution and perception that occur at runtime negatively affects the probability that an agent is able to finish a task successfully. There are several techniques such as hand-coded error recovery or diagnosis and repair approaches to deal with this problem. In this paper we present an experimental comparison of the fault tolerance of the popular (CRAM) framework using hand-coded error recovery and an approach using model-based diagnosis. The approaches were evaluated using a simulated robot delivery domain. Experimental results confirm that a control approach using diagnosis is able to significantly increase the success-rate and outperforms hand-coded error strategies.