31 research outputs found
Hans Belting, Face and Mask: a Double History
No full textIn this work, Hans Belting sets out to substantiate the assumption that the possibility and development of the image of the face have been tied to the concept of mask from time immemorial up to the very now of the “consumption of media faces” as the author puts it. The enterprise appears awe-inspiring, despite the charm of the oppositional pair face/mask only reminiscent of the once sturdy structuralism. The book falls into three parts, each of them divided in sundry usually short chapters. T..
Risk sensitive particle filters
No full textWe propose a new particle filter that incorporates a model of costs when generating particles. The approach is motivated by the observation that the costs of accidentally not tracking hypotheses might be significant in some areas of state space, and irrelevant in others. By incorporating a cost model into particle filtering, states that are more critical to the system performance are more likely to be tracked. Automatic calculation of the cost model is implemented using an MDP value function calculation that estimates the value of tracking a particular state. Experiments in two mobile robot domains illustrate the appropriateness of the approach
Abstract
No full textPlanetary 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.
Abstract
No full textPlanetary 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.
Variable resolution particle filter
No full textParticle filters are used extensively for tracking the state of non-linear dynamic systems. This paper presents a new particle filter that maintains samples in the state space at dynamically varying resolution for computational efficiency. Resolution within statespace varies by region, depending on the belief that the true state lies within each region. Where belief is strong, resolution is fine. Where belief is low, resolution is coarse, abstracting multiple similar states together. The resolution of the statespace is dynamically updated as the belief changes. The proposed algorithm makes an explicit bias-variance tradeoff to select between maintaining samples in a biased generalization of a region of state space versus in a high variance specialization at fine resolution. Samples are maintained at a coarser resolution when the bias introduced by the generalization to a coarse resolution is outweighed by the gain in terms of reduction in variance, and at a finer resolution when it is not. Maintaining samples in abstraction prevents potential hypotheses from being eliminated prematurely for lack of a sufficient number of particles. Empirical results show that our variable resolution particle filter requires significantly lower computation for performance comparable to a classical particle filter
Survey of Command Execution Systems for NASA Spacecraft and Robots
No full textNASA spacecraft and robots operate at long distances from Earth Command sequences generated manually, or by automated planners on Earth, must eventually be executed autonomously onboard the spacecraft or robot. Software systems that execute commands onboard are known variously as execution systems, virtual machines, or sequence engines. Every robotic system requires some sort of execution system, but the level of autonomy and type of control they are designed for varies greatly. This paper presents a survey of execution systems with a focus on systems relevant to NASA missions
