A Simulation Framework for the Path Planning of Unmanned Autonomous Systems

This paper presents a simulation framework designed to plan the movements of unmanned autonomous systems (UAS’s) in hazardous environments, to coordinate their actions, predict their behavior and evaluate their mission success in various combat situations. Current simulation methods do not predict the complex interrelations among vehicles, operating environments, and paths, thus providing inadequate tests. A family of methods for coordinating, positioning, routing and assessing diverse military units or “agents” (such as unmanned ground vehicles) is described in this paper. The methods are tested and evaluated through computer simulations to ensure suitability for operating unmanned autonomous systems (UAS’s). The path evaluation is performed using a dynamic GIS, distance transform, and genetic algorithms. The optimization algorithms for use in testing future unmanned systems are based on multiple objectives and criteria, including: (1) timeliness, (2) detectability & exposure time, (3) probabilities of survival & mission completion; (4) energy use; and (5) obstacle avoidance. A series of tests are presented which mimics real-world combat situation to test the effectiveness of the developed