Planning with Diversified Models for Fault-Tolerant Robots B. Lussier, M. Gallien, J. Guiochet, F. Ingrand, M.-O. Killijian, D. Powell

Abstract Planners are central to the notion of complex autonomous systems. They provide the flexibility that autonomous systems need to be able to operate unattended in an unknown and dynamically-changing environment. However, they are notoriously hard to validate. This paper reports an investigation of how redundant, diversified models can be used as a complement to testing, in order to tolerate residual development faults. A fault-tolerant temporal planner has been designed and implemented using diversity, and its effectiveness demonstrated experimentally through fault injection. The paper describes the implementation of the fault-tolerant planner and discusses the results obtained. The results indicate that diversification provides a noticeable improvement in planning dependability (measured, for instance, by the robustness of the plans it produces) with a negligible performance overhead. However, further improvements in dependability will require implementation of an on-line checking mechanism for assessing plan validity before execution.

Keywords: robotics; fault tolerance; dependability; diverse programming; planning