Best combination of multiple objectives for UAV search & track path optimization

This paper addresses the problem of designing objective functions for autonomous surveillance—target search & tracking (S&T)—by unmanned aerial vehicles (UAVs). A typical S&T mission inherently includes multiple, most often conflicting, objectives such as detection, survival, and tracking. A common approach to cope with this issue is to optimize a convex combination (weighted sum) of the individual objectives. In practice, determining the weights of a multiobjective combination is, more or less, a guesswork whose success is highly dependant on the designer’s assessment and intuition. An optimal (tradeoff) point in the performance space is hard to come up with by varying the weights of the individual objectives. In this paper the optimal weights design problem is treated more systematically, in a rigorous multiobjective optimization (MOO) framework. The approach is based on finding a set of optimal points (Pareto front) in the performance space and solving the inverse problem – determine the weights corresponding to a chosen optimal performance (trade-off) point. The implementation is done through the known normal boundary intersection (NBI) numerical method for computing the Pareto front. The use of the proposed methodology is illustrated by several case studies of typical S&T scenarios.