Course of Action (COA) Generation for Robotic Military Ground Vehicles

<div class="section abstract"><div class="htmlview paragraph">Course of action (COA) generation for robotic military ground vehicles is required to support autonomous operations in well-structured and non-structured environments. Traditional pathing algorithms such as Dijkstra, A*, Hybrid A*, or D* are exhaustive and well structured, and as a result, a single COA may be derived if one exists. Traditional path-planning algorithms have been optimized to identify paths that achieve a single scalar objective (duration, distance, energy, etc.). The algorithms are not natively able to account for multi-objective cost considerations. Military operations represent multi-objective optimization problems, impacted by time, space, and atmospherics. The battlefield is dynamic and ever-changing, thus pathing algorithms must incorporate multi-objective costs and constraints and be provided in near-real-time or real-time. For this reason, the use of a genetic algorithm (GA) and Artificial Intelligence/ Machine Learning (AI/ML) were investigated for COA generation. Genetic algorithms can be used to solve globally optimal multi-objective optimization problems given sufficient time and computational resources; the GA may also be used to identify a potential solution space of COAs. AI/ML is well-versed in solving linear and non-linear problems and is well-suited for diverse predictive analytics ranging from time series forecasting, image analysis, classification, and contextual analysis and recognition. Training AI/ML is computationally intensive; however, the implementation can be accomplished with reduced computational requirements. Depending on the complexity, both methods can be implemented in near-real-time or real-time, making the algorithms ideal for mission planning, and near-real-time and real-time course of action generation. This manuscript generates multiple COAs for military ground vehicles, producing thousands of options with varying optimality regarding energy consumption and multi-variable objectives (energy, time, or detection). Traditionally, COA generation for autonomous mission planning relies on graph-based methods, reinforcement learning, or Q-learning. Non-traditional approaches, such as LSTMNs and CNNs, require the use of transformers to be effective.</div></div>