Simulation Analysis for UAV Search Algorithm Design Using Approximate Dynamic Programming

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Many decision-and-control algorithms have been proposed for autonomous unmanned aerial vehicles (UAVs). The nature of this problem, with large decision spaces and the desire for optimal performance criteria, indicates that closed-form analysis of any approach is nearly impossible, suggesting a simulation-based performance evaluation of relevant scenarios. However, while effective simulation practices have been developed in the operations-research community, awareness of them in the decision-and-control community may be lower than desired for routine application. If applied correctly, these simulation techniques could have a major impact on the quality and effectiveness of UAV algorithms. This paper provides a concrete example that demonstrates that the marriage of UAV decision-and-control algorithms with proper simulation techniques can be done effectively and with great benefits for the UAV algorithm researcher, both in terms of the validity and quality of simulation results. The example used in this case is a study conducted for a stochastic UAV algorithm design. In particular, the study is to find the "optimal" sensitivity of a "future-gain" factor that attempts to balance future and present gains in a stochastic-approximate dynamic-programming solution to the problem faced by a team of UAVs searching in an uncertain environment for targets. This work can serve as a template for similar simulation experiments in this area. Experimental motivation and demonstration of the results are given.

Document Type: Research Article


Publication date: June 1, 2009

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  • Military Operations Research is the leading peer-reviewed journal publishing articles in the fields that describe operations research (OR) methodologies and theories used in key military applications.

    MOR specifically invites papers that are significant military OR applications. Of particular interest are papers that present case studies showing innovative OR applications, apply OR to major policy issues, introduce interesting new problem areas, highlight educational issues, and document the history of military OR.
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