Richard Dearden

Diagnosis by a waiter and a Mars explorer

This paper shows how state-of-the-art state estimation techniques can be used to provide efficient solutions to the difficult problem of real-time diagnosis in mobile robots. The power of the adopted estimation techniques resides in our ability to combine particle filters with classical algorithms, such as Kalman filters. We demonstrate these techniques in two scenarios: a mobile waiter robot and planetary rovers designed by NASA for Mars exploration.

Real-time fault detection and situational awareness for rovers: report on the Mars technology program task

In this paper we describe the results of a project funded by the Mars technology program at NASA, aimed at developing algorithms to meet this requirement. We describe a number of particle filtering-based algorithms for state estimation which we have demonstrated successfully on diagnosis problems including the K-9 rover at NASA Ames Research Center and the Hyperion rover at CMU. Due to the close interaction between a rover and its environment, traditional discrete approaches to diagnosis are impractical for this domain. Therefore we model rover subsystems as hybrid discrete/continuous systems. There are three major challenges to make particle filters work in this domain. The first is that fault states typically have a very low probability of occurring, so there is a risk that no samples enter fault states. The second issue is coping with the high-dimensional continuous state spaces of the hybrid system models, and the third is the severely constrained computational power available on the rover. This means that very few samples can be used if we wish to track the system state in real time. We describe a number of approaches to rover diagnosis specifically designed to address these challenges.

The Gaussian Particle Filter for Diagnosis of Non-Linear Systems

Abstract Fault diagnosis is a critical task for autonomous operation of systems such as spacecraft and planetary rovers, and must often be performed on-board. Unfortunately, these systems frequently also have relatively little computational power to devote to diagnosis. For this reason, algorithms for these applications must be extremely efficient, and preferably anytime. In this paper we introduce the Gaussian particle filter (GPF), a new variant on the particle filtering algorithm specifically for non-linear hybrid systems. Each particle samples a discrete mode and approximates the continuous variables by a multivariate Gaussian that is updated at each time-step using an unscented Kalman filter. The algorithm is closely related to Rao-Blackwellized Particle Filtering and equally efficient, but is more broadly applicable. We demonstrate the algorithm on a Mars rover problem and show that it is faster and more accurate than traditional particle filters.

Scheduling in the face of uncertain resource consumption and utility

We discuss the problem of scheduling tasks that consume uncertain amounts of a resource with known capacity and where the tasks have uncertain utility. In these circumstances, we would like to find schedules that exceed a lower bound on the expected utility when executed. We show that the problems are NP-complete, and present some results that characterize the behavior of some simple heuristics over a variety of problem classes.