Efficient Monitoring for Planetary Rovers

Planetary rovers operate in environments where human intervention is expensive, slow, unreliable, or impossible. It is therefore essential to monitor the behavior of these robots so that contingencies may be addressed before they result in catastrophic failures. This monitoring needs to be efficient since there is limited computational power available on rovers. We propose an efficient particle filter for monitoring faults that combines the Unscented Kalman Filter (UKF) [7] and the Variable Resolution Particle Filter (VRPF) [16]. We begin by using the UKF to obtain an improved proposal distribution for a particle filter which tracks discrete fault variables as part of its state space. This requires computing an unscented transform for every particle and every possible discrete transition to a fault or nominal state at each instant in time. Since there are potentially a large number of faults that may occur at any instant, this approach does not scale well. We use the VRPF to address this concern. The VRPF tracks abstract states that may represent single states or sets of states. There are many fewer transitions between states when they are represented in abstraction. We show that the VRPF in conjunction with a UKF proposal improves performance and may potentially be used in large state spaces. Experimental results show a significant improvement in efficiency.