Improving the Scalability of the Magnitude-Based Deceptive Path-Planning Using Subgoal Graphs

摘要

Deceptive path-planning is the task of finding a path so as to minimize the probability of an observer (or a defender) identifying the observed agent’s final goal before the goal has been reached. Magnitude-based deceptive path-planning takes advantage of the quantified deceptive values upon each grid or position to generate paths that are deceptive. Existing methods using optimization techniques cannot satisfy the time constraints when facing with the large-scale terrain, as its computation time grows exponentially with the size of road maps or networks. In this work, building on recent developments in the optimal path planner, the paper proposes a hybrid solution between map scaling and hierarchical abstractions. By leading the path deception information down into a general purpose but highly-efficient path-planning formulation, the paper substantially speeds up the task upon large scale terrains with an admissible loss of deception.