How can intelligent machines assess their competency to complete a task? This question has come into focus for autonomous systems that algorithmically make decisions under uncertainty. We argue that machine self-confidence—a form of meta-reasoning based on self-assessments of system knowledge about the state of the world, itself, and ability to reason about and execute tasks—leads to many computable and useful competency indicators for such agents. This article presents our body of work, so far, on this concept in the form of the Factorized Machine Self-Confidence (FaMSeC) framework, which holistically considers several major factors driving competency in algorithmic decision-making: outcome assessment, solver quality, model quality, alignment quality, and past experience. In FaMSeC, self-confidence indicators are derived via “problem-solving statistics” embedded in Markov Decision Process solvers and related approaches. These statistics come from evaluating probabilistic exceedance margins in relation to certain outcomes and associated competency standards specified by an evaluator. Once designed, and evaluated, the statistics can be easily incorporated into autonomous agents and serve as indicators of competency. We include detailed descriptions and examples for Markov Decision Process agents and show how outcome assessment and solver quality factors can be found for a range of tasking contexts through novel use of meta-utility functions, behavior simulations, and surrogate prediction models. Numerical evaluations are performed to demonstrate that FaMSeC indicators perform as desired (references to human subject studies beyond the scope of this article are provided).

For the first time, the performance of the range imaging technique of the very high frequency band middle and upper atmosphere (MU) radar (Shigaraki observatory, Japan), when using frequency diversity, is assessed. This is done by the detection of unmanned aerial vehicles (UAVs) operated near the radar during the Shigaraki UAV radar experiment campaign, carried out from June 1, 2015 to June 14, 2015. During this campaign devoted to the measurements of fine-scale turbulence and stability in the lower troposphere using the turbulence sensors on the DataHawk UAV and the MU radar, the detection of the small UAV by the MU radar provided an excellent opportunity for taking stock of the range imaging technique in the presence of a single hard target. It was found that range imaging reproduces a faithful image of the aircraft position and its displacements with an excellent accuracy (of the order of ~10 m), giving extra credence to the thin echo layers and their vertical displacements generally observed from the range imaging technique in stably stratified conditions.

An airborne wireless sensor network (WSN) composed of bird-sized micro aerial vehicles (MAVs) enables low cost high granularity atmospheric sensing of toxic plume behavior and storm dynamics, and provides a unique three-dimensional vantage for monitoring wildlife and ecological systems. This paper describes a complete implementation of our SensorFlock airborne WSN, spanning the development of our MAV airplane, its avionics, semi-autonomous flight control software, launch system, flock control algorithm, and wireless communication networking between MAVs. We present experimental results from flight tests of flocks of MAVs, and a characterization of wireless RF behavior in air-to-air communication as well as air-to-ground communication.

Specific rendering modes are developed for a combined visual/haptic interface to allow exploration and understanding of fluid dynamics data. The focus is on visualization of shock surfaces and vortex cores. Advantages provided by augmenting traditional graphical rendering modes with haptic rendering modes are discussed. Particular emphasis is placed on synergistic combinations of visual and haptic modes which enable rapid, exploratory interaction with the data. Implementation issues are also discussed.

Rate-hardness is introduced as a quality metric for hard virtual surfaces, and linked to human perception of hardness via a psychophysical study. A 3 degree-of-freedom haptic interface is used to present pairs of virtual walls to users for side-by-side comparison, 19 subjects are tested in a series of three blocks of trials, where different virtual walls are presented in randomly ordered pairs. Results strongly support the use of rate-hardness, as opposed to mechanical stiffness, as the more relevant metric for haptic interface performance in rendering hard virtual surfaces. It is also shown that common techniques of enhancing stability of the rendered surfaces tend to actually enhance performance as measured by rate-hardness.

Numerical computation of forward kinematics for an in-parallel interface are developed which conform to the needs of real-time haptic rendering. This approach produces a computationally fast algorithm for computing Cartesian position and orientation from actuator position measurements. Explicit bounds on the convergence rates are derived which can be used to design appropriate computational strategies to maximize control loop cycle times, yet preserve acceptable accuracy.

Realistic specifications are developed for hand controller mechanical and control design, based on limitations of human operator motion and perception capabilities observed in initial experimental studies. A representative dynamic model is used to develop expressions for the limits on closed loop hand controller impedance. Specific trade-offs are derived between mechanical design options and control design options for achieving realistic performance specifications. Implications of these trade-offs in designing "ideal-equivalent" hand controllers are discussed, and applied to a high-performance laboratory hand controller prototype.>

Tools for quantifying teleoperation system performance and stability when communication delays are present are provided. A general multivariable system architecture is utilized which includes all four-types of data transmission between master and slave: force and velocity in both directions. It is shown that a proper use of an four channels is of critical importance in achieving high performance telepresence in the sense of accurate transmission of task impedances to the operator. It is also shown that transparency and robust stability (passivity) are conflicting design goals in teleoperation systems. The analysis is illustrated by comparing transparency and stability in two common architectures, as well as a recent passivated approach and a new transparency-optimized architecture, using simplified one-degree-of-freedom examples.>

The author provides tools for analyzing the performance of various teleoperation systems, including the effects of communication delay. A general multivariable system architecture is utilized which includes all four types of data transmission between master and slave robot; force and position in both directions. It is shown that a proper use of all four channels is of critical importance in achieving high-performance telepresence in the sense of accurate transmission of task impedances to the operator. Achieved transparency and stability properties of two common architectures, as well as a transparency-optimized architecture are quantitatively compared on simplified one-degree-of-freedom examples.>

The effects of actuator torque/speed constraints on achievable manipulator mechanical impedance are discussed. Beginning with a simplified dynamic model, a describing function analysis is used to examine the constraints for linear behavior as well as the effective closed-loop impedance resulting from the actuator nonlinear characteristics. It is shown that the closed-loop manipulator impedance is a blend of two extreme impedances, and the degree of nonlinearity in the actuator controls the proportion of these two components in the overall dynamic response. The extension to multiple-degree-of-freedom manipulators is also discussed, revealing behavioral properties similar to the single-degree-of-freedom case. The relevance of this simple analysis to manipulator system preliminary design and feasibility studies is stressed.>

Various implementations of impedance control have been suggested, usually based on idealized models of the physical system. This work considers the nonideal, practical effects of computation and/or communication delays and manipulator dynamics on the behavior of two primary approaches to impedance control. The results are cast in the form of stability boundaries, i.e. the relationships between desired impedance parameters which cause marginally stable behavior in the overall system. These stability boundaries are compared for the two primary implementations, and relative benefits of each approach are discussed. These comparisons provide the basis for quantitative tradeoffs, allowing selection of control implementation approaches suited for particular manipulators or allowing quantitative decisions to be made in manipulator system design.>