Quantitative Assessment of Robotic Swarm Coverage

This paper studies a generally applicable, sensitive, and intuitive error metric for the assessment of robotic swarm density controller performance. Inspired by vortex blob numerical methods, it overcomes the shortcomings of a common strategy based on discretization, and unifies other continuous notions of coverage. We present two benchmarks against which to compare the error metric value of a given swarm configuration: non-trivial bounds on the error metric, and the probability density function of the error metric when robot positions are sampled at random from the target swarm distribution. We give rigorous results that this probability density function of the error metric obeys a central limit theorem, allowing for more efficient numerical approximation. For both of these benchmarks, we present supporting theory, computation methodology, examples, and MATLAB implementation code.Comment: Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Porto, Portugal, 29--31 July 2018. 11 pages, 4 figure

Reliability assessment of cutting tool life based on surrogate approximation methods

A novel reliability estimation approach to the cutting tools based on advanced approximation methods is proposed. Methods such as the stochastic response surface and surrogate modeling are tested, starting from a few sample points obtained through fundamental experiments and extending them to models able to estimate the tool wear as a function of the key process parameters. Subsequently, different reliability analysis methods are employed such as Monte Carlo simulations and first- and second-order reliability methods. In the present study, these reliability analysis methods are assessed for estimating the reliability of cutting tools. The results show that the proposed method is an efficient method for assessing the reliability of the cutting tool based on the minimum number of experimental results. Experimental verification for the case of high-speed turning confirms the findings of the present study for cutting tools under flank wear

Analyzing helicopter evasive maneuver effectiveness against rocket-propelled grenades

It has long been acknowledged that military helicopters are vulnerable to ground-launched threats, in particular, the RPG-7 rocket-propelled grenade. Current helicopter threat mitigation strategies rely on a combination of operational tactics and selectively placed armor plating, which can help to mitigate but not entirely remove the threat. However, in recent years, a number of active protection systems designed to protect land-based vehicles from rocket and missile fire have been developed. These systems all use a sensor suite to detect, track, and predict the threat trajectory, which is then employed in the computation of an intercept trajectory for a defensive kill mechanism. Although a complete active protection system in its current form is unsuitable for helicopters, in this paper, it is assumed that the active protection system’s track and threat trajectory prediction subsystem could be used offline as a tool to develop tactics and techniques to counter the threat from rocket-propelled grenade attacks. It is further proposed that such a maneuver can be found by solving a pursuit–evasion differential game. Because the first stage in solving this problem is developing the capability to evaluate the game, nonlinear dynamic and spatial models for a helicopter, RPG-7 round, and gunner, and evasion strategies were developed and integrated into a new simulation engine. Analysis of the results from representative vignettes demonstrates that the simulation yields the value of the engagement pursuit–evasion game. It is also shown that, in the majority of cases, survivability can be significantly improved by performing an appropriate evasive maneuver. Consequently, this simulation may be used as an important tool for both designing and evaluating evasive tactics and is the first step in designing a maneuver-based active protection system, leading to improved rotorcraft survivability

Quantifying Robotic Swarm Coverage

In the field of swarm robotics, the design and implementation of spatial density control laws has received much attention, with less emphasis being placed on performance evaluation. This work fills that gap by introducing an error metric that provides a quantitative measure of coverage for use with any control scheme. The proposed error metric is continuously sensitive to changes in the swarm distribution, unlike commonly used discretization methods. We analyze the theoretical and computational properties of the error metric and propose two benchmarks to which error metric values can be compared. The first uses the realizable extrema of the error metric to compute the relative error of an observed swarm distribution. We also show that the error metric extrema can be used to help choose the swarm size and effective radius of each robot required to achieve a desired level of coverage. The second benchmark compares the observed distribution of error metric values to the probability density function of the error metric when robot positions are randomly sampled from the target distribution. We demonstrate the utility of this benchmark in assessing the performance of stochastic control algorithms. We prove that the error metric obeys a central limit theorem, develop a streamlined method for performing computations, and place the standard statistical tests used here on a firm theoretical footing. We provide rigorous theoretical development, computational methodologies, numerical examples, and MATLAB code for both benchmarks.Comment: To appear in Springer series Lecture Notes in Electrical Engineering (LNEE). This book contribution is an extension of our ICINCO 2018 conference paper arXiv:1806.02488. 27 pages, 8 figures, 2 table