Vandi Verma

Real-time fault diagnosis [robot fault diagnosis]

This article presents a number of complementary algorithms for detecting faults on-board operating robots, where a fault is defined as a deviation from expected behavior. The algorithms focus on faults that cannot directly be detected from current sensor values but require inference from a sequence of time-varying sensor values. Each algorithm provides an independent improvement over the basic approach. These improvements are not mutually exclusive, and the algorithms may be combined to suit the application domain. All the approaches presented require dynamic models representing the behavior of each of the fault and operational states. These models can be built from analytical models of the robot dynamics, data from simulation, or from the real robot. All the approaches presented detect faults from a finite number of known fault conditions, although there may potentially be a very large number of these faults.

First Experiments in the Robotic Investigation of Life in the Atacama Desert of Chile

The Atacama Desert of northern Chile may be the most lifeless place on Earth, yet where the desert meets the Pacific coastal range desiccation-tolerant micro-organisms are known to exist. The gradient of biodiversity and habitats in the Atacama’s subregions remain unexplored and are the focus of the Life in the Atacama project. To conduct this investigation, long traverses must be made across the desert with instruments for geologic and biologic measurements. In this paper we motivate the Life in the Atacama project from both astrobiologic and robotic perspectives. We focus on some of the research challenges we are facing to enable endurance navigation, resource cognizance, and long-term survivability. We conducted our first scientific investigation and technical experiments in Chile with the mobile robot Hyperion. We describe the experiments and the results of our analysis. These results give us insight into the design of an effective robotic astrobiologist and into the methods by which we will conduct scientific investigation in the next field season.

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.

Decision-theoretic Monte Carlo smoothing for scaling tracking in hybrid dynamic systems

Detecting faults on-board planetary rovers is important since human intervention may not be possible due to communication delays. In This work we propose a scalable method for on-board fault detection and identification that may be applied to general fault models with limited computation. Although our application focus is on diagnosing rover faults, this method is applicable in general for tracking any general non-linear, non-Gaussian hybrid (discrete-continuous) dynamic system online. Our formulation of the fault detection problem requires estimating robot and environmental state, as it changes over time, from a sequence of noisy sensor measurements. We propose a Monte Carlo algorithm that generates new trajectories if the probability of the current set of fault hypothesis being tracked is low. This approach maintains a fixed lag history of measurements, controls and samples. Experimental results of a dynamic simulation of a six-wheel rocker-bogie rover show a significant improvement in performance over the classical approach.