A multi-robot coordination approach for autonomous runway Foreign Object Debris (FOD) clearance

This paper introduces an effective Foreign Object Debris (FOD) clearance system based on multi-robot coordination. The main objective of this study is to minimize the amount of time airports need to close for the execution of the FOD collection process. At the present time, FOD collection is done by human-operated follow-me vehicles, which is time consuming, expensive and error-prone. Time is a very critical parameter for the aviation domain and a delay of even one minute may cause the failure of a critical military mission or the loss of thousands of dollars at civil aviation. To this end, a multi-robot coordination approach based on auction-based task allocation is proposed in order to handle dynamic task occurrences on the runways of an airport. Robots that are located at stations are coordinated to collect active FOD in the shortest time. An agent-based experimental simulation framework is developed and several FOD collection scenarios are executed on Istanbul Ataturk Airport. The proposed model, which is based on effective robot locating by using heat maps and optimal task assignments, has provided promising results when compared to existing well-known techniques. Application of this study will enable a fully autonomous, safe and fast system that collects FOD, and human-operated vehicles will no longer be needed. © 2015 Elsevier B.V

Foreign Object Debris Detection System Cost-Benefit Analysis

Foreign object debris (FOD) poses significant safety and financial threats to aviation. Estimates of the annual global costs of FOD range up to $22.7 billion in current United States dollars. The Federal Aviation Administration (FAA) recognizes that airport FOD detection systems can help reduce FOD risks. The FAA Airport Technology Research and Development Branch research team reviewed a recent cost-benefit analysis (CBA) of such systems. Inputs to this analysis included stakeholder interviews, literature review, safety and operational databases, and airport FOD detection records

On Comparative Algorithmic Pathfinding in Complex Networks for Resource-Constrained Software Agents

Software engineering projects that utilize inappropriate pathfinding algorithms carry a significant risk of poor runtime performance for customers. Using social network theory, this experimental study examined the impact of algorithms, frameworks, and map complexity on elapsed time and computer memory consumption. The 1,800 2D map samples utilized were computer random generated and data were collected and processed using Python language scripts. Memory consumption and elapsed time results for each of the 12 experimental treatment groups were compared using factorial MANOVA to determine the impact of the 3 independent variables on elapsed time and computer memory consumption. The MANOVA indicated a significant factor interaction between algorithms, frameworks, and map complexity upon elapsed time and memory consumption, F(4, 3576) = 94.09, p \u3c .001, h2 = .095. The main effects of algorithms, F(4, 3576) = 885.68, p \u3c .001, h2 = .498; and frameworks, F(2, 1787) = 720,360.01, p .001, h2 = .999; and map complexity, F(2, 1787) = 112,736.40, p \u3c .001, h2 = .992, were also all significant. This study may contribute to positive social change by providing software engineers writing software for complex networks, such as analyzing terrorist social networks, with empirical pathfinding algorithm results. This is crucial to enabling selection of appropriately fast, memory-efficient algorithms that help analysts identify and apprehend criminal and terrorist suspects in complex networks before the next attack