Planning Random path distributions for ambush games in unstructured environments

TL;DR

This paper introduces a game-theoretic planning method for vehicle navigation in adversarial, unstructured environments, using linear programming and environment representation networks to improve robustness and performance.

Contribution

It presents a novel approach combining game theory, linear programming, and environment modeling for planning in complex adversarial settings, with analysis of solver sensitivity.

Findings

The proposed planner outperforms conventional methods in adversarial scenarios.

Network-based environment representations improve planning efficiency.

Sensitivity analysis reveals impact of LP solver choices on solutions.

Abstract

Operating vehicles in adversarial environments require non-conventional planning techniques. A two-player, zero-sum non-cooperative game is introduced, which is solved via a linear program. An extension is proposed to construct networks displaying good representations of the environment characteristics, while offering acceptable results for the technique used. Sensitivity of the solution to the LP solver algorithm is identified. The performances of the planner are finally assessed by comparison with those of conventional planners. Results are used to formulate secondary objectives to the problem.