A genetic algorithm (GA) is a global search algorithm based on biological genetics. GAs are generally used for industrial applications, artificial neural networks, web applications, the defense industry, and so on. However, it is difficult to apply GAs to more complex situations because of the fixed number of chromosomes. In this research, in order to overcome this limitation, we propose a variable-chromosome GA with a chromosome attachment feature. Verification of the algorithm is carried out through anti-submarine high value unit (HVU) escort mission simulations. Ultimately, it is confirmed that the GA using the variable chromosome is more effective in dealing with highly complex missions, whereby the number of chromosomes gradually increases.
Most warship combat systems inquire human operator to control several sensor and another equipments as well as decision-modeling. For this reason, many researches with multi-agent based M&S (Modeling and Simulation) have been increasingly conducted. However there cannot find any researches of M&S based analysis for anti-submarine warfare that requires a high level of mission complexity between multiple platforms. In this research, we have been developed various combat platform models such as warship, submarine and helicopter, etc. In order to apply the multi-agent-based M&S technology to the anti-submarine warfare i.e. a HVU (High Value Unit) escort mission scenario. Then we have successfully analyzed the measures of effectiveness according to the different tactics and different situations. In future, the defence engineer maybe employ our methodology and tools to analyze actual tactical problem by simply inserting actual data into our agent model.
In order to operate multiple UAVs for multiple tasks efficiently, we need a task allocation algorithm with minimum cost, i.e.,total moving distance required to accomplish the whole mission. In this paper, we have proposed the MCBAA (Modified Consensus Based Auction Algorithm) which can be suitably applied to the operation of multiple UAVs. The key idea of proposed algorithm is to minimize sum of distance from current location of agents to location of tasks based on the conventional CBAA. Several simulation test performed on three UAV agents with multiple tasks demonstrates the overall efficiency both in time and total distance.
The UAV systems working in difficult environment should be able to performs various actions autonomously required to achieve the given mission without the human interventions. However, the actual tests for such UAV system will take heavy cost. Thus, the simulation test in advance before the actual test is important. This paper proposes a 3D visual simulation environment for autonomous agent-based UAV systems. The several simulation tests performed on the rescue scenarios will demonstrate our techniques.
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