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1、本文格式为Word版，下载可任意编辑DYNAMIC PATH Proceedings of the 2022 Winter Simulation Conference S. Chick, P. J. Snchez, D. Ferrin, and D. J. Morrice, eds. DYNAMIC PATH-PLANNING FOR SEARCH AND DESTROY MISSIONS THE BAY OF BISCAY SCENARIO Subhashini Ganapathy Raymond R. Hill Department of Biomedical, Industrial an
2、d Human Factors Engineering Wright State University 207, Russ Engineering Center Dayton, OH 45324, U.S.A. ABSTRACT Among the many modeling methods used for military ap-plications, simulation modeling is one of the most popular as it offers flexibility and an ability to perform “what-if” analysis.In
3、this paper, we discuss search and destroy mis-sions in the context of the World War II Bay of Biscay U-boat scenario. We present a simulation architecture that supports integration of human reasoning with simulation-based optimization methods. 1 INTRODUCTION Simulation models are used extensively fo
4、r studying mili-tary applications. The primary goal of a military simulation is to provide a high fidelity representation of the combat conditions. Among the many modeling methods used for military applications, simulation modeling is one of the most popular as it offers flexibility and an ability t
5、o per-form “what-if” analysis (Battilega and Grange 1978).In the Search and Destroy (SAD) missions, a troop would search for the enemy and when found would capture or destroy the enemy force. Typically, in such cases, there is a threat to the search team by the enemy targets, directly or indirectly.
6、 In a direct threat there are attacks without an intervening agency or resource acting on behalf of the threat. In a SAD scenario the information about the objects of interest (targets) is very important and is dynamic in nature. There could be two situations, (a) searching for an elusive target, an
7、d (b) if a target is located and is subse-quently lost. In the latter case, it behooves us to start the search in the area the target was last located. The knowl-edge about the target location (search area) temporally decays and this knowledge may no longer be relevant with change in time. The dynam
8、ics and uncertainty associated with this problem scenario makes it very difficult to apply purely analytic approaches. Human reasoning integrated with heuristic optimization methods offer a potentially attrac- tive alternative. In this article, we present the simulation architecture in the context o
9、f Bay of Biscay scenario. We present the deci- sion algorithm that the simulation architecture uses in plan- ning the path over the search area. Our model implements the computational infrastructure to support reusable soft- ware components. The architecture represents a dynamic path planning in the
10、 context of the Bay of Biscay scenario involving interactions associated with search for targets with minimum resources and maximum efficiency. Simula- tion models give the flexibility of expanding and compress- ing time (real world time) and, thus, allow extensive inves- tigation of the system duri
11、ng any period of time. It accounts for the dynamics and uncertainty of the system. 2 PROBLEM DOMAIN This study focuses on the U-boat hunting problem during World War II in the Bay of Biscay area. The Bay of Bis- cay is an inlet of the Atlantic Ocean in southeastern Europe, bounded by France and Spai
12、n. The German U- boats operated from captured ports in occupied France, crossing the Bay of Biscay to gain access to the North At- lantic.In the Atlantic, the U-boat wolf packs would look for and attack allied convoys.The Allied convoys pro- vided critical logistical support to the war effort. The c
13、on- voys would ship basic necessities and war material to Great Britain.As counter measures, the Allied force sent out airplanes to search and destroy the U-boats. Accord- ing to McCue (1990), one of the biggest challenges faced by the Allies was the Axis U-boat threat. U-boats most imperiled North
14、Atlantic shipping in 1942 and 1943.The Allied forces sent aircraft to search the Bay of Biscay area for U-boats that were in transit be- tween their ports in occupied France and the areas in North Atlantic. This search effort was more of an “offen-999 Ganapathy and Hill sive” campaign than the “defe
15、nsive” task of protecting convoys (Waddington 1973). The problem domain can be described by the entities present in the system, their states and events as shown in Figure 1. The U-boats can be present in one of the three states: surfaced, submerged or sunk. The aircraft remain either on the base to be deployed or on the bay searching for the U-boats. The three major functional components of the system are: 1. The place of origin of the U-boats (French Ports Gallo and Pallotino 1988; Nils- son 1982;). Dijkstras algorithm has been the widely used algo-rithm because it is simple
