동적인 위협이 존재하는 전장에서의 무인 항공기 지역경로계획
A Local Path Planning for Unmanned Aerial Vehicle on the Battlefield of Dynamic Threats
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An unmanned aerial vehicle (UAV) is a powered aerial vehicle that does not carry a human operator, uses aerodynamic forces to provide vehicle lift, can fly autonomously or be piloted remotely, can be expendable or recoverable, and can carry a lethal or non-lethal payload. An UAV is very important weapon system and is currently being employed in many military missions (surveillance, reconnaissance, communication relay, targeting, strike, etc.) in the war. To accomplish UAV’s missions, guarantee of survivability should be preceded. The main objective of this study is a local path planning to maximize survivability for UAV on the battlefield of dynamic threats (obstacles, surface-to-air missiles, radar etc.). A local path planning is capable of producing a new path in response to environmental changes. This study suggests a Smart A*(Smart A-star) algorithm for local path planning. The local path planned by Smart A* algorithm is compared with the results of existing algorithms (A* Replanner,D*)and evaluated performance of Smart A* algorithm. The result of suggested algorithm gives the better solutions when compared with existing algorithms.
Abstract
An unmanned aerial vehicle (UAV) is a powered aerial vehicle that does not carry a human operator, uses aerodynamic forces to provide vehicle lift, can fly autonomously or be piloted remotely, can be expendable or recoverable, and can carry a lethal or non-lethal payload. An UAV is very important weapon system and is currently being employed in many military missions (surveillance, reconnaissance, communication relay, targeting, strike, etc.) in the war. To accomplish UAV’s missions, guarantee of survivability should be preceded. The main objective of this study is a local path planning to maximize survivability for UAV on the battlefield of dynamic threats (obstacles, surface-to-air missiles, radar etc.). A local path planning is capable of producing a new path in response to environmental changes. This study suggests a Smart A*(Smart A-star) algorithm for local path planning. The local path planned by Smart A* algorithm is compared with the results of existing algorithms (A* Replanner,D*)and evaluated performance of Smart A* algorithm. The result of suggested algorithm gives the better solutions when compared with existing algorithms.
참고문헌 (18)
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