An advance in the development of smart munitions entails autonomously modifying target selection during flight in order to maximize the value of the target being destroyed. Target identification and classification provides a basis for target value which is used in conjunction with multi-target tracks to determine an optimal aimpoint for the munition. A unique guidance law can be constructed that exploits attribute and kinematic data from an onboard video sensor. This thesis develops an innovative path planning algorithm that provides an obstacle avoidance function while navigating the munition toward the highest value target. The foundation of this path planning method is found in the principles of minimum effort control optimization. Results demonstrate the ability of the path planning algorithm to determine a path for the munition to follow which is both stable and feasible.
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