A New Methodology for Optimizing Evasive Maneuvers Under Uncertainty in the Extended Two-Dimensional Pursuer/Evader Problem

Traditional analytic or controltheoretic solutions to the problem of optimizing evasive maneuvers in the extended two-dimensional pursuer/evader problem require the evader to execute specific sequences of maneuvers at precise pursuer/evader distances. These solutions depend upon several pursuer-specific characteristics, and fail to effectively account for uncertainty about the state of the pursuer. This paper describes the implementation of a genetic programming system that evolves optimized solutions to the extended twodimensional pursuer/evader problem that do not depend upon knowledge of pursuer’s current state. Best-of-run programs execute strategies by which an evader may maneuver to successfully evade a pursuer starting from a wide range of relative initial positions, under conditions where the state of the pursuer is unknown or uncertain.