An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning

Mobile robotic platforms are an indispensable tool for various scientific and industrial applications. Robots are used to undertake missions whose execution is constrained by various factors, such as the allocated time or their remaining energy. Existing solutions for resource constrained multi-robot sensing mission planning provide optimal plans at a prohibitive computational complexity for online application [1],[2],[3]. A heuristic approach exists for an online, resource constrained sensing mission planning for a single vehicle [4]. This work proposes a Genetic Algorithm (GA) based heuristic for the Correlated Team Orienteering Problem (CTOP) that is used for planning sensing and monitoring missions for robotic teams that operate under resource constraints. The heuristic is compared against optimal Mixed Integer Quadratic Programming (MIQP) solutions. Results show that the quality of the heuristic solution is at the worst case equal to the 5% optimal solution. The heuristic solution proves to be at least 300 times more time efficient in the worst tested case. The GA heuristic execution required in the worst case less than a second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and Automation Letters (RA-L

The Problem of Too Many Statistical Tests: Subgroup Analyses in a Study Comparing the Effectiveness of Online and Live Lectures

The more statistical analyses performed in the analysis of research data, the more likely it is that one or more of the conclusions will be in error. Multiple statistical analyses can occur when the sample contains several subgroups and the researchers perform separate analyses for each subgroup. For example, separate analyses may be done for different ethnic groups, different levels of education, and/or for both genders. Media reports of research frequently omit information on the number of subgroup analyses performed thus leaving the reader with insufficient information to assess the validity of the conclusions. This article discusses the problems with a media report on research that was analyzed by conducting many subgroup analyses. The article concludes that the quantitatively literate reader should be skeptical of articles that report subgroup analyses without reporting the number of analyses that were done

Calibration of Computational Models with Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA

It has become commonplace to use complex computer models to predict outcomes in regions where data does not exist. Typically these models need to be calibrated and validated using some experimental data, which often consists of multiple correlated outcomes. In addition, some of the model parameters may be categorical in nature, such as a pointer variable to alternate models (or submodels) for some of the physics of the system. Here we present a general approach for calibration in such situations where an emulator of the computationally demanding models and a discrepancy term from the model to reality are represented within a Bayesian Smoothing Spline (BSS) ANOVA framework. The BSS-ANOVA framework has several advantages over the traditional Gaussian Process, including ease of handling categorical inputs and correlated outputs, and improved computational efficiency. Finally this framework is then applied to the problem that motivated its design; a calibration of a computational fluid dynamics model of a bubbling fluidized which is used as an absorber in a CO2 capture system

Epicentral confidence regions of nuclear test events at teleseismic distances

The accurate location of seismic events is a basic discriminant for underground nuclear test monitoring (Bolt, 1976; Dahlman and Israelson, 1977; Blandford, 1982). Of particular interest are determining epicentral confidence regions and providing constraints on estimated focal depths. In this study, only routine teleseismic P travel-time data are used, as provided by worldwide stations reporting to the International Seismological Centre (ISC). This lessens the need to model the effects of crustal and shallow-mantle velocity variations, as is necessary with seismographic networks operating at regional distances (Blandford, 1981; Evernden et al., 1986)

Carbon-13 in groundwater from English and Norwegian crystalline rock aquifers: a tool for deducing the origin of alkalinity?

The 13C signature is evaluated for various environmental compartments (vegetation, soils, soil gas, rock and groundwater) for three crystalline rock terrains in England and Norway. The data are used to evaluate the extent to which stable carbon isotopic data can be applied to deduce whether the alkalinity in crystalline bedrock groundwaters has its origin in hydrolysis of carbonate or silicate minerals by CO2. The resolution of this issue has profound implications for the role of weathering of crystalline rocks as a global sink for CO2. In the investigated English terrain (Isles of Scilly), groundwaters are hydrochemically immature and DIC is predominantly in the form of carbonic acid with a soil gas signature. In the Norwegian terrains, the evidence is not conclusive but is consistent with a significant fraction of the groundwater DIC being derived from silicate hydrolysis by CO2. A combined consideration of pH, alkalinity and carbon isotope data, plotted alongside theoretical evolutionary pathways on bivariate diagrams, strongly suggests real evolutionary pathways are likely to be hybrid, potentially involving both open and closed CO2 conditions