Particle Filters for Real-Time Fault Detection in Planetary Rovers

Planetary rovers provide a considerable challenge for artificial intelligence in that they must operate for long periods autonomously, or with relatively little intervention. To achieve this, they need to have on-board fault detection and diagnosis capabilities. Traditional model-based diagnosis techniques are not suitable for rovers due to the tight coupling between the vehicle’s performance and its environment. Hybrid diagnosis using particle filters is presented as an alternative, and its strengths and weaknesses are examined. We also present some extensions to particle filters that are designed to make them more suitable for use in diagnosis problems.